WO2021203912A1

WO2021203912A1 - Online prediction method for parameters in copper converting process based on oxygen bottom blowing furnace

Info

Publication number: WO2021203912A1
Application number: PCT/CN2021/080600
Authority: WO
Inventors: 张哲铠; 黎敏; 李兵; 吴金财; 张官祥; 董择上
Original assignee: 中国恩菲工程技术有限公司
Priority date: 2020-04-10
Filing date: 2021-03-12
Publication date: 2021-10-14
Also published as: CN111598293A

Abstract

Disclosed in the present application is an online prediction method for parameters in a copper converting process based on an oxygen bottom blowing furnace. The method comprises: establishing a bottom blowing converting furnace mechanism model according to a raw material input condition and on the basis of a material balance model, an energy balance model, and a multiphase balance model; establishing a bottom blowing converting furnace data driving model according to actual production data and on the basis of a crude copper grade neural network model, a slag ferrosilicon ratio neural network model, and a slag temperature neural network model between a target parameter and an input parameter; integrating the mechanism model and the data driving model by using an intelligent coordinator to obtain a bottom blowing converting furnace mixing model relating to a crude copper grade prediction value, a slag ferrosilicon ratio prediction value, and a slag temperature prediction value; and outputting final prediction values of the crude copper grade, the slag ferrosilicon ratio, and the slag temperature in a copper bottom blowing converting process by using the mixing model. The prediction method can effectively solve the problem that the existing prediction models and methods are poor in adaptability and not satisfactory in actual operation effect, and can significantly improve the accuracy of a prediction result.

Description

Online prediction method of copper blowing process parameters in oxygen bottom blowing furnace

Cross-references to related applications

This application requires the priority and rights of a Chinese patent application with an application number of 202010281411.8 and an application date of April 10, 2020. The entire content of the above Chinese patent application is hereby incorporated into this application by reference.

Technical field

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.

Background technique

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. At the same time, the copper blowing process is a very complex process. It is a multi-input and multi-output system. Each variable has the characteristics of strong coupling, time-varying, distributed parameters and significant uncertainty. Most of the key parameters of the blowing process are difficult to detect in real time, and there is a time lag. Only relying on the production experience of the operator to operate, the blowing end is not suitable to be controlled, resulting in unsatisfactory furnace conditions and fluctuations in product quality, which restricts the use of the advantages of the process.

Among them, 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, and 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. In other words, in the actual production of bottom-blowing furnace copper blowing, it is impossible to monitor the three key parameters of product blister copper grade, iron-silicon ratio in slag and slag temperature in real time. Generally, 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.

Application content

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. By integrating the mechanism model and the data-driven model, 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.

This application is based on the following questions and findings:

At present, 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. When the model structure is relatively large, a large number of mathematical calculations will be required to solve the problem. The convergence is slow and it is difficult to achieve online real-time Estimate; 4) The establishment of a mechanism model is usually based on certain assumptions, and these assumptions are different from the actual situation, and it is difficult to ensure the accuracy of the model. However, the performance of intelligent modeling methods based on data-driven construction, such as neural networks, is not only affected by the quality of training samples, spatial distribution and training algorithms, but also has poor extrapolation performance. This model is unexplainable. And although there are currently online parameter prediction methods based on hybrid models, they are actually based on the establishment of a complex mechanism model and only use actual production data to modify the mechanism model. In essence, a single mechanism model is used, and there is a process of constructing a model. Complicated, large amount of calculation, small effect of actual data on the mechanism model and other shortcomings. For bottom-blowing furnace blowing, the raw material and fuel conditions and other parameters have significant effects on the blister copper grade, slag-iron-silicon ratio, and slag temperature. Therefore, it is necessary to adopt the optimization of the batching system and the optimization of the operating system to reduce the fluctuation of raw materials. Refining the influence of three important parameters. The applicant envisions that the mechanism model can be combined with the data-driven model to develop an integrated model to predict the three important parameters in the copper smelting process, so as to predict the product performance by inputting the feedstock and the parameters in the blowing process. Important parameters, and compare with the actual test results of the product to modify the prediction model to improve the prediction accuracy, so that the prediction accuracy of the integrated model can meet the guidance of actual production.

For this reason, according to one aspect of the present application, the present application proposes an online prediction method for the parameters of the copper blowing process in an oxygen bottom blowing furnace. According to an embodiment of the present application, the prediction method includes:

According to the raw material input conditions and based on the material balance model, energy balance model and multi-phase balance model, establish the bottom blowing furnace mechanism model on the predicted value of blister copper grade, the predicted value of slag-iron-silicon ratio and the predicted value of slag temperature;

According to the 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, 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. Bottom-blown converting furnace data-driven model for the predicted value of slag temperature;

Use an intelligent coordinator to integrate the mechanism model and the data-driven model to obtain a bottom blowing furnace mixing model with respect to the predicted value of blister copper grade, the predicted value of the slag-to-silicon-iron ratio, and the predicted value of the slag temperature. The model outputs the final predicted value of blister copper grade, slag silicon-to-iron ratio and slag temperature in the copper bottom blowing process,

Wherein, 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.

According to the online prediction method of oxygen bottom-blowing furnace copper blowing process parameters in the above-mentioned embodiments of this application, 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. , Continuously improve the mechanism model model and data-driven model, and modify the parameters of the intelligent coordinator to make the prediction results more in line with the actual production results, thereby significantly improving the prediction accuracy of the three major parameters in the copper blowing process of the oxygen bottom blowing furnace. In summary, 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.

In addition, the method for online prediction of copper blowing process parameters in an oxygen bottom blowing furnace according to the foregoing embodiment of the present application may also have the following additional technical features:

In some embodiments of the present application, the material balance model is established based on a material balance equation, the energy balance model is established based on an energy balance equation, and 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, and the mechanism model is established in combination with the process characteristics of the copper bottom blowing process.

In some embodiments of the present application, 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.

In some embodiments of the present application, 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.

In some embodiments of the present application, the hybrid model is corrected periodically or in real time based on actual production results.

In some embodiments of the present application, 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.

In some embodiments of the present application, 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.

In some embodiments of the present application, 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, and μ(x) is used to represent the data-driven model in the The weighting coefficient in the hybrid model uses (1-μ(x)) to represent the weighting coefficient of the mechanism model in the hybrid model, and the prediction result of the output of the intelligent coordinator is:

y=f ₂ ×μ(x)+f ₁ ×(1-μ(x)),

Wherein, y represents the prediction result, and 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, and the weighting coefficient μ(x) of the data-driven model in the hybrid model is:

x represents the input variable, 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.

In some embodiments of the present application, the weighting coefficient μ(x) of the data-driven model in the hybrid model is:

Among them, μ _i is the membership function calculated from the input variable i and its corresponding characteristic parameters a, b, c, d, λ _i is the weight coefficient of the input variable i in the j input variables, and λ _i is the empirical value Sure.

The additional aspects and advantages of the present application will be partly given in the following description, and part of them will become obvious from the following description, or be understood through the practice of the present application.

Description of the drawings

The above and/or additional aspects and advantages of the present application will become obvious and easy to understand from the description of the embodiments in conjunction with the following drawings, in which:

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.

Detailed ways

The embodiments of the present application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, in which the same or similar reference numerals denote the same or similar elements or elements with the same or similar functions. The embodiments described below with reference to the drawings are exemplary, and are intended to explain the present application, but should not be understood as a limitation to the present application.

According to one aspect of this application, this application proposes an online method for predicting parameters of the copper blowing process in an oxygen bottom blowing furnace. According to the embodiment of the present application, referring to Fig. 1, 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. Among them, 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.

Hereinafter, the online prediction method of the copper blowing process parameters of the oxygen bottom blowing furnace in the above-mentioned embodiment of the present application will be described in detail with reference to FIGS. 1 to 4.

According to a specific embodiment of the present 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, and 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. Among them:

The material balance equation is:

Among them, _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;

with

Respectively represent the mole fraction of the input item material and the specific component of the specific material in the input item material;

with

Respectively represent the input item material and the specific component of the specific material in the input item material;

with

Respectively represent the mole fraction of the input item material and the specific material in the input item material;

with

Respectively represent the input item material and the specific material in the input item material;

with

Respectively represent the sum of the elements of each component in the input material and output material in the blowing process;

with

Respectively represent the sum of the components in the input material and output material in the blowing process;

with

Respectively represent the sum of the mass of each component of each component in the input material and output material in the blowing process. This equation represents the conservation of the elements, components and mass of the input material and output material in the bottom blowing process, that is, the conservation of material .

The energy balance equation is:

_Wherein, ΔH _{298, Ai} is the entry A _i standard enthalpy; ΔH _{298, Bj} output item B _j standard enthalpy; Cp _Ai is the entry A _i is the heat capacity; Cp _Bj output item B _j is the heat capacity; Q _Loss is the heat loss during the blowing process. 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:

Among them, 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; γ pc} is the activity factor of component c in phase p; χ _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.

When solving the material balance equation, energy balance equation, and multiphase balance equation simultaneously, 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 obtained multivariate linear equations is Ax=b:

Perform elementary row transformation on the system of equations, and gradually eliminate the non-singular matrix A into the upper three solution matrix:

Back to the solution, gradually substituted into the calculation to get the solution of the equations:

On the basis of the above calculation principles, combined with the process characteristics of the copper bottom blowing process, 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.

According to another specific embodiment of the present application, 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 the copper bottom blowing process Copper matte grade, copper matte temperature, oxygen matte ratio, flux rate, electrolytic residual electrode rate and oxygen enrichment rate. Among them, 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 In order to facilitate the understanding of the transfer function of the input/output relationship, we take the j-th neuron model as an example. The input-output relationship of the j-th neuron model is:

y _j = f(s _j ) = f(wp+b)

Among them, 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, and p and y are the input and output of the neuron, respectively. f(wp+b) is the transfer function. After the neuron model is weighted and summed with the input

Comparison with the threshold value b _j, and if the weight exceeds the threshold, then the neuron is active, the output is 1, otherwise, the neuron is not activated, the output is 0.

According to another specific embodiment of the present application, 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. Establish a data-driven model for the bottom-blown converting furnace. 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. As shown in Figure 3, the BP neural network is composed of an input layer, an intermediate layer or a hidden layer and an output layer. When the signal is propagating forward, 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. As the learning process is carried out multiple times, 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. Therefore, by using 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.

According to another specific embodiment of the present application, 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. When the industrial production parameters of the bottom-blowing furnace copper blowing process change smoothly and the working conditions are normal, 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 . Therefore, by using 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. _{Furthermore, 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, and μ(x) can be used to represent the bottom-blown converting furnace data-driven The weighting coefficient of the model in the mixing model, using (1-μ(x)) to represent the weighting coefficient of the bottom blowing furnace mechanism model in the mixing model, the prediction result of the output of the intelligent coordinator is:

y=f ₂ ×μ(x)+f ₁ ×(1-μ(x)),

Among them, 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 μ(x) of the data-driven model in the mixed model is obtained by the membership function. The relationship between μ(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 When it is an input variable, the copper matte grade and its corresponding characteristic parameters a, b, c, d can be:

{Copper matte grade|a,b,c,d}={60%,67%,72%,75%}

It should be noted that the values of characteristic parameters a, b, c, and d are not fixed and can be adjusted according to actual production. For example, the values of characteristic parameters a, b, c, and d are different for different production conditions. It's not all the same. Further, 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 μ _i value of each input variable, and then use the weighted average method to calculate The final weighting coefficient μ(x) value, namely

According to another specific embodiment of the present application, when there are 6 input variables, the weighting coefficient μ(x) of the data-driven model in the mixed model is

Among them, μ _i (i=1, 2,..., 6) is the membership function calculated according to a certain input variable and its corresponding characteristic parameters a, b, c, d, and the membership function is

Or 0, λ _i (i=1, 2,..., 6) is the weight coefficient of a certain input variable in all input variables, which can be determined with reference to empirical values.

According to another specific embodiment of the present application, 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.

According to another specific embodiment 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.

According to another specific embodiment of the present application, 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. Use the preliminarily established prediction model for the three important parameters of the bottom-blowing furnace blowing process to predict the three important parameters, and compare the final prediction results with the actual measured values. If the error is within the required range, the model is established and the The predicted values of the three important parameters of bottom blowing are input to the server database for storage and displayed on the computer interface; if the error exceeds the required range, return to revise the model and adjust the intelligent coordination coefficient accordingly. Repeat the above steps. Until the error is reduced to the required range.

To sum up, according to the online prediction method of oxygen bottom-blowing furnace copper blowing process parameters in the above-mentioned embodiments of this application, 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. Important parameters, the bottom blowing furnace mechanism model and data-driven model are respectively established to predict the three important parameters. On the one hand, 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. On this basis, design a suitable intelligent coordinator to integrate the two, and compare and correct the prediction results of the integrated hybrid model with the actual production results. Continuously improve the mechanism model model and data-driven model, and modify the parameters of the intelligent coordinator to make the prediction results more suitable for the actual production results, thereby significantly improving its prediction accuracy of the three major parameters in the copper blowing process of the oxygen bottom blowing furnace. In summary, 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.

In the description of this specification, descriptions with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" etc. mean specific features described in conjunction with the embodiment or example , The structure, materials, or characteristics are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above-mentioned terms are not necessarily directed to the same embodiment or example. Moreover, the described specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine the different embodiments or examples and the features of the different embodiments or examples described in this specification without contradicting each other.

Although the embodiments of the present application have been shown and described above, it can be understood that the above-mentioned embodiments are exemplary and should not be construed as limiting the present application. A person of ordinary skill in the art can comment on the foregoing within the scope of the present application. The embodiment undergoes changes, modifications, substitutions, and modifications.

Claims

An online prediction method for copper blowing process parameters in an oxygen bottom blowing furnace, which is characterized in that it comprises:

According to the raw material input conditions and based on the material balance model, energy balance model and multi-phase balance model, establish a bottom-blown converting furnace mechanism model on the predicted value of blister copper grade, the predicted value of slag-iron-silicon ratio and the predicted value of slag temperature;

According to the 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, 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. Bottom-blown converting furnace data-driven model for the predicted value of slag temperature;

Use an intelligent coordinator to integrate the mechanism model and the data-driven model to obtain a bottom blowing furnace mixing model with respect to the predicted value of blister copper grade, the predicted value of the slag-to-silicon-iron ratio, and the predicted value of the slag temperature. The model outputs the final predicted value of blister copper grade, slag silicon-to-iron ratio and slag temperature in the copper bottom blowing process,

Wherein, 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 online prediction method of claim 1, wherein the material balance model is established based on a material balance equation, the energy balance model is established based on an energy balance equation, and the multiphase balance model is based on multiple The phase balance equation is established, the material balance equation, the energy balance equation, and the multiphase balance equation are solved simultaneously using METCAL software or METSIM software, and the mechanism is established by combining the process characteristics of the copper bottom blowing process Model.
The online prediction method according to claim 1, wherein 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 Artificial neurons include, but are not limited to, copper matte grade, copper matte temperature, oxygen matte ratio, flux rate, electrolytic residual electrode rate, and oxygen enrichment rate in the copper bottom blowing process.
The online prediction method according to claim 1, wherein the modeling mechanism of the 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 establishes the data-driven model.
The online prediction method according to claim 1, wherein the hybrid model is corrected periodically or in real time based on actual production results.
The online prediction method according to claim 1, wherein the correction of the hybrid model comprises: combining the final predicted value of the blister copper grade, the slag silicon-to-iron ratio, and the slag temperature output by the hybrid model with the actual measured value comparing:

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, the final predicted value is returned to the smart coordinator and the weighting coefficient is adjusted, and the above operations are repeated until the error is reduced to the expected range.
The online prediction method according to any one of claims 1 to 6, wherein the intelligent coordinator uses a method of fuzzy division of input variable regions and synthesis to calculate the mechanism model and the data-driven model prediction method The weighting factor.
The in-line prediction method according to claim 7, wherein f 1 represents the results using the prediction model of the output mechanism, f 2 is the use of a data-driven model prediction result output by μ (x) represents the The weighting coefficient of the data-driven model in the hybrid model, using (1-μ(x)) to represent the weighting coefficient of the mechanism model in the hybrid model, and the output prediction result of the smart coordinator is:

y=f 2 ×μ(x)+f 1 ×(1-μ(x)),

Wherein, y represents the prediction result, and 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, and the weighting coefficient μ(x) of the data-driven model in the hybrid model is:

x represents the input variable, 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 online prediction method according to claim 8, wherein the weighting coefficient μ(x) of the data-driven model in the hybrid model is:

Among them, μ i is the membership function calculated from the input variable i and its corresponding characteristic parameters a, b, c, d, λ i is the weight coefficient of the input variable i in the j input variables, and λ i is the empirical value Sure.