阅读说明：本技术 氧气底吹炉铜熔炼过程参数在线预测方法 (On-line prediction method for parameters of copper smelting process of oxygen bottom-blowing furnace ) 是由 张哲铠 黎敏 李兵 吴金财 于 2020-04-10 设计创作，主要内容包括：本发明公开了氧气底吹炉铜熔炼过程参数在线预测方法。该方法包括：根据原料输入条件并基于物料平衡模型、能量平衡模型及多相平衡模型建立底吹熔炼炉机理模型；根据实际生产数据并基于目标参数和输入参数之间的铜锍品位神经网络模型、硅铁比神经网络模型及渣温度神经网络模型建立底吹熔炼炉数据驱动模型；利用智能协调器对机理模型和数据驱动模型进行集成,得到关于铜锍品位预测值、硅铁比预测值和渣温度预测值的底吹熔炼炉混合模型,利用所述混合模型输出铜底吹熔炼过程中铜锍品位、硅铁比和渣温度的最终预测值。该预测方法可以解决现有预测方法适应能力差、实际运行效果不理想的问题,显著提高预测结果的准确性。(The invention discloses an oxygen bottom blowing furnace copper smelting process parameter online prediction method. The method comprises the following steps: establishing a bottom-blowing smelting furnace mechanism model according to raw material input conditions and based on a material balance model, an energy balance model and a multiphase balance model; establishing a bottom-blown smelting furnace data driving model according to actual production data and based on a copper matte grade neural network model, a ferrosilicon ratio neural network model and a slag temperature neural network model between target parameters and input parameters; and integrating the mechanism model and the data driving model by using an intelligent coordinator to obtain a bottom-blowing smelting furnace mixing model related to a copper matte grade predicted value, a ferrosilicon ratio predicted value and a slag temperature predicted value, and outputting final predicted values of the copper matte grade, the ferrosilicon ratio and the slag temperature in the copper bottom-blowing smelting process by using the mixing model. The prediction method can solve the problems of poor adaptability and unsatisfactory actual operation effect of the existing prediction method, and the accuracy of the prediction result is obviously improved.)

1. An oxygen bottom blowing furnace copper smelting process parameter online prediction method is characterized by comprising the following steps:

establishing a bottom-blowing smelting furnace mechanism model related to a copper matte grade predicted value, a ferrosilicon ratio predicted value and a slag temperature predicted value according to raw material input conditions and based on a material balance model, an energy balance model and a multiphase balance model;

establishing a bottom-blown smelting furnace data driving model related to a copper matte grade predicted value, a ferrosilicon ratio predicted value and a slag temperature predicted value according to actual production data and based on a copper matte grade neural network model, a ferrosilicon ratio neural network model and a slag temperature neural network model between target parameters and input parameters;

integrating the mechanism model and the data driving model by using an intelligent coordinator to obtain a bottom-blowing smelting furnace mixing model related to a copper matte grade predicted value, a ferrosilicon ratio predicted value and a slag temperature predicted value, outputting final predicted values of the copper matte grade, the ferrosilicon ratio and the slag temperature in the copper bottom-blowing smelting process by using the mixing model,

the intelligent coordinator is suitable for calculating the weighting coefficients of the mechanism model and the data driving model in the mixed model based on the deviation between the copper matte grade predicted value, the ferrosilicon ratio predicted value and the slag temperature predicted value which are output by the mechanism model and the data driving model respectively and the actual measured values of the copper matte grade, the ferrosilicon ratio and the slag temperature, and outputting the final predicted values of the copper matte grade, the ferrosilicon ratio and the slag temperature according to the weighting coefficients, the mechanism model predicted value and the data driving model predicted value.

2. The on-line 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, the multiphase balance model is established based on a multiphase balance equation, and the material balance equation, the energy balance equation and the multiphase balance equation are solved simultaneously by using METCAL software or METSIM software and the mechanism model is established by combining process characteristics of a copper bottom blowing smelting process.

3. The on-line prediction method of claim 1, wherein the copper matte grade neural network, the silicon iron ratio neural network and the slag temperature neural network each independently comprise a plurality of artificial neurons including, but not limited to, oxygen enrichment rate, coal blending rate, oxygen-to-charge ratio, flux rate, amount of Cu charged into the furnace, amount of Fe charged into the furnace, amount of S charged into the furnace, amount of CaO charged into the furnace and SiO charged into the furnace in the copper bottom-blowing smelting process₂Amount of the compound (A).

4. The on-line prediction method of claim 1, wherein the data-driven model is established by using a modeling mechanism of a BP neural network and combining industrial production big data of a copper bottom blowing smelting process, a copper matte grade neural network, a ferrosilicon ratio neural network and a slag temperature neural network.

5. The online prediction method of claim 1, wherein the hybrid model is calibrated periodically or in real time based on actual production results.

6. The online prediction method of claim 1, wherein correcting the mixture model comprises: and comparing the final predicted values of the copper matte grade, the ferrosilicon ratio and the slag temperature output by the mixed model with actual measured values:

if the error is within the expected range, keeping the weighting coefficient in the mixed model unchanged;

and if the error is out of the expected range, returning the final predicted value to the intelligent coordinator, adjusting the weighting coefficient, and repeating the operation until the error is reduced to be in the expected range.

7. The online prediction method according to any one of claims 1 to 6, wherein the intelligent coordinator adopts a method of fuzzy dividing input variable regions and integrating to calculate the weighting coefficients of the mechanism model and the data-driven model prediction method.

8. The online prediction method of claim 7, wherein f is utilized₁Representing the prediction result output from the mechanism model by using f₂Representing the output prediction result of the data-driven model, representing the weighting coefficient of the data-driven model in the mixed model by mu (x), representing the weighting coefficient of the mechanism model in the mixed model by (1-mu (x)), and the output prediction result of the intelligent coordinator is as follows:

y＝f₂×μ(x)+f₁×(1-μ(x))，

wherein y represents a prediction result, the prediction result comprises a copper matte grade prediction value, a ferrosilicon ratio prediction value and a slag temperature prediction value, and the weighting coefficient mu (x) of the data driving model in the mixed model is as follows:

x represents input variables, and the selection range of the input variables comprises but is not limited to oxygen enrichment rate, coal blending rate, oxygen-material ratio, flux rate, Cu amount in the furnace, Fe amount in the furnace, S amount in the furnace, CaO amount in the furnace and SiO amount in the furnace₂An amount; a. b, c and d are characteristic parameters corresponding to the input variables, which are obtained according to technical data of actual industrial production, and the characteristic parameters determine membership functions of the input variables.

9. The online prediction method of claim 8, wherein the weighting coefficient μ (x) of the data-driven model in the hybrid model is:

wherein, mu_iCalculating a membership function lambda of the input variable i and the corresponding characteristic parameters a, b, c and d_iIs the weight coefficient occupied by the input variable i in j input variables, lambda_iDetermined by empirical values.

Technical Field

The invention belongs to the field of metallurgy, and particularly relates to an on-line prediction method for parameters of a copper smelting process of an oxygen bottom blowing furnace.

Background

The smelting process is a relatively important process in the copper smelting process, and iron and sulfur in raw materials such as copper concentrate with low copper content are oxidized and removed to obtain copper matte with high copper content, so that the raw materials are provided for the subsequent converting process. The oxygen bottom blowing copper smelting furnace is the core equipment of the process. The oxygen bottom blowing furnace is a device commonly used in the domestic copper smelting process at present, the smelting process has the characteristics of continuity and instantaneity, corresponding control technology is required to further exert the process characteristics, and the stable product quality and the optimized furnace condition are ensured. Meanwhile, the copper smelting process is a very complex process and is a multi-input and multi-output system, each variable has the characteristics of strong coupling, time varying, distributed parameters, obvious uncertainty and the like, and with the continuous improvement of copper smelting strength and the continuous improvement of copper smelting indexes, enterprises put forward higher requirements on the control of the continuous copper smelting production process.

The three parameters of the copper matte grade, the iron-silicon ratio in the slag and the slag temperature are important parameters for inspecting the copper smelting process of the oxygen bottom blowing furnace, are important detection indexes in the bottom blowing smelting process, and reflect the smelting state in the oxygen bottom blowing copper smelting process. The detection method comprises the steps of sampling in the copper matte and slag discharging processes, then testing the components of the sample by using a fluorescence analyzer, and calculating to obtain the iron-silicon ratio of the slag and the copper matte grade value. The key parameter in the oxygen bottom blowing copper smelting process is the melt temperature, but due to the severe production environment, the melt temperature cannot be directly measured on line, and the slag temperature is generally adopted as an indirect characterization parameter of the melt temperature in the production. That is to say, three key parameters of the copper matte grade of the product, the iron-silicon ratio of the smelting slag and the slag temperature cannot be monitored in real time in the actual production of the copper smelting of the bottom blowing furnace. In general, the copper smelting process requires that three important parameters, namely copper matte grade, iron-silicon ratio in smelting slag and slag temperature, are kept in a proper range and have small fluctuation as much as possible, so that the three important parameters in the bottom blowing smelting process need to be predicted by establishing a corresponding and reliable prediction model, and guidance suggestions are provided for decision and operation of field workers.

Disclosure of Invention

The present invention is directed to solving, at least to some extent, one of the technical problems in the related art. Therefore, the invention aims to provide an on-line prediction method for the parameters of the copper smelting process of the oxygen bottom blowing furnace. According to the prediction method, the mechanism model and the data driving model are integrated, so that the accuracy of a prediction result can be obviously improved, and the problems of poor adaptability and unsatisfactory actual operation effect caused by the fact that one prediction method is singly applied for modeling are effectively solved.

The present application was made by the inventors based on the following problems and findings:

at present, a research method for a parameter prediction model of an oxygen bottom blowing copper smelting process mainly adopts a mechanism modeling method or an intelligent modeling method based on data driving, but the two methods have the defects that: the mechanism modeling can essentially reflect the law of an objective process, has good extrapolation and strong interpretability, but has the following defects: 1) as for the specificity of the model, as long as the objects of the model are different, the structure and parameters of the mechanism model are greatly different, and the model has poor portability; 2) the whole modeling process of the mechanism model costs a lot of manpower and material resources, and each step is very difficult to be carried out in the research of reaction intrinsic dynamics, the determination of various equipment models, the characterization of heat and mass transfer effect of the device in practical application and the estimation of a large number of parameters (including test equipment and devices); 3) the mechanism model generally consists of an algebraic equation set, a differential equation set and even a partial differential equation set, when the model structure is huge, a large amount of mathematical calculation is required to be faced during solution, the convergence is slow, and the requirement on online real-time estimation is difficult to meet; 4) the establishment of the mechanism model is usually based on certain assumed conditions, and the assumed conditions have certain differences from the actual conditions, so that the accuracy of the model is difficult to ensure. The performance of an intelligent modeling method constructed based on data driving, such as a neural network, is not only influenced by the quality, spatial distribution and training algorithm of a training sample, but also has poor extrapolation performance, and the model has inexplicability. For the smelting of the bottom blowing furnace, parameters such as raw fuel conditions have obvious influence on the copper matte grade, the slag iron silicon ratio and the slag temperature, so that the influence of raw material fluctuation on three important parameters of smelting is reduced by adopting the optimization of a batching system and the optimization of an operation system. The inventor imagines that an integrated model can be developed to predict three important parameters in copper smelting by combining a mechanism model and a data driving model, so that the important parameters of a product can be predicted by inputting the parameters in the feeding materials and the smelting process, and the prediction model is corrected by comparing with the actual detection result of the product, thereby improving the prediction precision and ensuring that the prediction accuracy of the integrated model can meet the requirement of guiding the actual production.

Therefore, according to one aspect of the invention, the invention provides an oxygen bottom blowing furnace copper smelting process parameter online prediction method. According to an embodiment of the present invention, the prediction method includes:

establishing a bottom-blowing smelting furnace mechanism model related to a copper matte grade predicted value, a ferrosilicon ratio predicted value and a slag temperature predicted value according to raw material input conditions and based on a material balance model, an energy balance model and a multiphase balance model;

establishing a bottom-blown smelting furnace data driving model related to a copper matte grade predicted value, a ferrosilicon ratio predicted value and a slag temperature predicted value according to actual production data and based on a copper matte grade neural network model, a ferrosilicon ratio neural network model and a slag temperature neural network model between target parameters and input parameters;

integrating the mechanism model and the data driving model by using an intelligent coordinator to obtain a bottom-blowing smelting furnace mixing model related to a copper matte grade predicted value, a ferrosilicon ratio predicted value and a slag temperature predicted value, outputting final predicted values of the copper matte grade, the ferrosilicon ratio and the slag temperature in the copper bottom-blowing smelting process by using the mixing model,

the intelligent coordinator is suitable for calculating the weighting coefficients of the mechanism model and the data driving model in the mixed model based on the deviation between the copper matte grade predicted value, the ferrosilicon ratio predicted value and the slag temperature predicted value which are output by the mechanism model and the data driving model respectively and the actual measured values of the copper matte grade, the ferrosilicon ratio and the slag temperature, and outputting the final predicted values of the copper matte grade, the ferrosilicon ratio and the slag temperature according to the weighting coefficients, the mechanism model predicted value and the data driving model predicted value.

According to the method for predicting the parameters of the copper smelting process of the oxygen bottom-blowing furnace in the embodiment of the invention, three important parameters, namely the copper matte grade, the iron-silicon ratio in the smelting slag and the slag temperature in the bottom-blowing smelting process, are mainly considered, the mechanism model and the data driving model of the bottom-blowing smelting furnace are respectively established to predict the three important parameters, and an appropriate intelligent coordinator is designed on the basis to integrate the two parameters, the prediction result of the integrated mixed model is compared and corrected with the actual production result, the mechanism model and the data driving model are continuously perfected, and the parameters of the intelligent coordinator are corrected, so that the prediction result of the intelligent coordinator meets the actual production result, and the prediction precision of the three parameters in the copper smelting process of the oxygen bottom-blowing furnace is obviously improved. In conclusion, the prediction method can fully combine the advantages of the mechanism model and the data driving model, make full use of advantages and avoid disadvantages, remarkably improve the accuracy of the prediction result, effectively solve the problems of poor adaptability and unsatisfactory actual operation effect caused by singly applying one prediction method for modeling, and has great significance and value in theoretical and actual application.

In addition, the online prediction method for the copper smelting process parameters of the oxygen bottom-blowing furnace according to the embodiment of the invention can also have the following additional technical characteristics:

in some embodiments of the invention, 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, and the material balance equation, the energy balance equation and the multiphase balance equation are solved simultaneously by using METCAL software or METSIM software and the mechanism model is established by combining process characteristics of a copper bottom blowing smelting process.

In some embodiments of the invention, the copper matte grade neural network, the silicon to iron ratio neural network and the slag temperature neural network each independently comprise a plurality of artificial neurons including, but not limited to, oxygen enrichment rate, coal blending rate, oxygen to charge ratio, flux rate, amount of Cu charged into the furnace, amount of Fe charged into the furnace, amount of S charged into the furnace, amount of CaO charged into the furnace and SiO charged into the furnace during the copper bottom blowing smelting process₂Amount of the compound (A).

In some embodiments of the invention, the data-driven model is established by using a modeling mechanism of a BP neural network and combining industrial production big data of a copper bottom blowing smelting process, a copper matte grade neural network, a ferrosilicon ratio neural network and a slag temperature neural network.

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

In some embodiments of the invention, correcting the mixture model comprises: and comparing the final predicted values of the copper matte grade, the ferrosilicon ratio and the slag temperature output by the mixed model with actual measured values: if the error is within the expected range, keeping the weighting coefficient in the mixed model unchanged; and if the error is out of the expected range, returning the final predicted value to the intelligent coordinator, adjusting the weighting coefficient, and repeating the operation until the error is reduced to be in the expected range.

In some embodiments of the present invention, the intelligent coordinator uses a fuzzy partitioning method for the variable regions of the input and a comprehensive method to calculate the weighting coefficients of the mechanism model and the data-driven model prediction method.

In some embodiments of the invention, f is utilized₁Representing the prediction result output from the mechanism model by using f₂Representing the output prediction result of the data-driven model, representing the weighting coefficient of the data-driven model in the mixed model by mu (x), representing the weighting coefficient of the mechanism model in the mixed model by (1-mu (x)), and the output prediction result of the intelligent coordinator is as follows:

y＝f₂×μ(x)+f₁×(1-μ(x))，

wherein y represents a prediction result, the prediction result comprises a copper matte grade prediction value, a ferrosilicon ratio prediction value and a slag temperature prediction value, and the weighting coefficient mu (x) of the data driving model in the mixed model is as follows:

x represents input variables, and the selection range of the input variables comprises but is not limited to oxygen enrichment rate, coal blending rate, oxygen-material ratio, flux rate, Cu amount in the furnace, Fe amount in the furnace, S amount in the furnace, CaO amount in the furnace and SiO amount in the furnace₂An amount; a. b, c and d are characteristic parameters corresponding to the input variables, which are obtained according to technical data of actual industrial production, and the characteristic parameters determine membership functions of the input variables.

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

wherein, mu_iCalculating a membership function lambda of the input variable i and the corresponding characteristic parameters a, b, c and d_iIs the weight coefficient occupied by the input variable i in j input variables, lambda_iDetermined by empirical values.

Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.

Drawings

The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:

FIG. 1 is a schematic diagram of a hybrid model of an on-line prediction method for copper matte grade, ferrosilicon ratio and slag temperature according to an embodiment of the present invention.

Fig. 2 is a schematic diagram of a neuron model structure according to an embodiment of the present invention.

Fig. 3 is a schematic structural diagram of a BP neural network model according to an embodiment of the present invention.

FIG. 4 is a flow chart of a method for on-line prediction of oxygen bottom-blown furnace copper smelting process parameters according to one embodiment of the present invention.

Detailed Description

Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.

According to one aspect of the invention, the invention provides an oxygen bottom blowing furnace copper smelting process parameter online prediction method. According to an embodiment of the present invention, referring to fig. 1, the prediction method includes: establishing a bottom-blowing smelting furnace mechanism model related to a copper matte grade predicted value, a ferrosilicon ratio predicted value and a slag temperature predicted value according to raw material input conditions and based on a material balance model, an energy balance model and a multiphase balance model; establishing a bottom-blown smelting furnace data driving model related to a copper matte grade predicted value, a ferrosilicon ratio predicted value and a slag temperature predicted value according to actual production data and based on a copper matte grade neural network model, a ferrosilicon ratio neural network model and a slag temperature neural network model between target parameters and input parameters; and integrating the mechanism model of the bottom-blown smelting furnace and the data driving model of the bottom-blown smelting furnace by using an intelligent coordinator to obtain a mixed model of the bottom-blown smelting furnace, which is related to a copper matte grade predicted value, a ferrosilicon ratio predicted value and a slag temperature predicted value, and outputting final predicted values of the copper matte grade, the ferrosilicon ratio and the slag temperature in the copper bottom-blown smelting process by using the mixed model. The intelligent coordinator is suitable for calculating the weighting coefficients of the bottom-blowing smelting furnace mechanism model and the bottom-blowing smelting furnace data driving model in the mixed model based on the deviation between the copper matte grade predicted value, the silicon iron ratio predicted value and the slag temperature predicted value which are output by the bottom-blowing smelting furnace mechanism model and the bottom-blowing smelting furnace data driving model respectively, and outputting the final predicted values of the copper matte grade, the silicon iron ratio and the slag temperature according to the weighting coefficients, the predicted value of the bottom-blowing smelting furnace mechanism model and the predicted value of the bottom-blowing smelting furnace data driving model. The prediction method can fully combine the advantages of a mechanism model and a data driving model, makes full use of advantages and avoids disadvantages, obviously improves the accuracy of a prediction result, effectively solves the problems of poor adaptability and unsatisfactory actual operation effect caused by singly applying one prediction method for modeling, and has great significance and value in theory and actual application.

The method for predicting the parameters of the copper smelting process of the oxygen bottom-blowing furnace in the embodiment of the invention in an online manner is described in detail with reference to fig. 1 to 4.

According to a specific embodiment of the invention, 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, simultaneous solution can be performed on the material balance equation, the energy balance equation and the multiphase balance equation by using METCAL software or METSIM software, and a bottom blowing smelting furnace mechanism model is established by combining process characteristics of a copper bottom blowing smelting process, wherein:

the material balance equation is:

wherein the content of the first and second substances,_Varrepresents the variable of the variable(s),_Conrepresents 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 represents a_c,eRepresents a specific element in a specific component;andrespectively representing the mole fractions of the input item materials and the specific components of the specific materials in the input item materials;andrespectively representing the input item materials and specific components of specific materials in the input item materials;andrespectively representing the mole fraction of the input item material and the specific material in the input item material;andrespectively representing input item materials and specific materials in the input item materials;andrespectively representing the sum of each element of each component in the input item material and the output item material in the smelting process;andrespectively representing the sum of each component in the input material and the output material in the smelting process;andrespectively representing the sum of the mass of each element in each component in the input material and the output material in the smelting process, and the equation represents the bottomThe elements, components and mass of input material and output material are conserved in the blowing smelting process, namely material conservation.

The energy balance equation is:

wherein, Δ H_298,AiAs an input item A_iStandard enthalpy of formation; Δ H_298,BjTo output item B_jStandard enthalpy of formation; cp_AiAs an input item A_iThe heat capacity of (c); cp_BjTo output item B_jThe heat capacity of (c); q_LossIs the heat loss in the smelting process. The equation represents that the heat of the input item material is equal to the heat of the output item material in the bottom blowing smelting process, namely energy conservation.

The multiphase equilibrium equation is:

wherein G is the total Gibbs free energy of the system,gibbs free energy for the standard formation of pure material c component in p phase; gamma ray_pcIs the activity factor of the c component in the p phase; chi shape_pcIs the mole fraction of the c component in the p phase; c_pIs the number of components in the p phase; t is the temperature; r is a gas universal constant; n is a radical of_pcIs the mole number of the c component in the p phase. The equation indicates that the Gibbs free energy of the system reaches the minimum in the smelting process, namely the system reaches a steady state.

When the material balance equation, the energy balance equation and the multiphase balance equation are solved simultaneously, a gaussian method may be used to obtain a multiple linear system of equations for each element of each component, for example, the multiple linear system of equations obtained for a specific element of a specific component is Ax ═ b:

performing primary row transformation on the equation set, and gradually performing de-metaplasia on the nonsingular matrix A into an upper three-solution matrix:

and (3) back substitution solving, gradually substituting and calculating to obtain a solution of an equation set:

on the basis of the calculation principle, third-party software with high reliability such as METCAL, METSIM and the like is adopted to establish a bottom blowing smelting furnace mechanism model by combining the process characteristics of the copper bottom blowing smelting process, and three important parameters of predicting the copper matte grade, the iron-silicon ratio in the smelting slag and the slag temperature in the copper bottom blowing smelting process are calculated.

According to another embodiment of the present invention, the copper matte grade neural network, the silicon iron ratio neural network and the slag temperature neural network each independently comprise a plurality of artificial neurons including, but not limited to, oxygen enrichment rate, coal blending rate, oxygen-to-charge ratio, flux rate, charged Cu amount, charged Fe amount, charged S amount, charged CaO amount and charged SiO amount₂Amount of the compound (A). The data-driven model can be processed by adopting an artificial neural network method, the artificial neural network is a nonlinear information processing system which is formed by a large number of processing units and simulates the biological structure and related functions of human brain, each artificial neural network is formed by a plurality of artificial neurons, the schematic structure of a model of the neurons is shown in figure 2, the neuron model is provided with R inputs, each input is connected with the next layer through a weight w, f is a transfer function representing the input/output relation, in order to conveniently understand the transfer function of the input/output relation, the jth neuron model is taken as an example, and the input/output relation of the jth neuron model is as follows:

y_j＝f(s_j)＝f(wp+b)

wherein S is_jAs an output function, b_jIs a threshold value, w_j,_iTo connect weight, x₀＝b_j，w_j,₀P, y are the input and output, respectively, of the neuron. f (wp + b) is the transfer function. After the neuron model is weighted and summed on the inputAnd a threshold value b_jIn comparison, if the weighted sum exceeds a threshold, the neuron is activated with an output of 1, otherwise the neuron is not activated with an output of 0.

According to another embodiment of the invention, a bottom-blowing smelting furnace data driving model can be established by utilizing a modeling mechanism of a BP neural network and combining industrial production big data of a copper bottom-blowing smelting process, a copper matte grade neural network, a ferrosilicon ratio neural network and a slag temperature neural network. The BP neural network is one of the most widely and successfully applied artificial neural networks at present, is a reverse-thrust learning algorithm of a multilayer network, the learning process of the BP neural network consists of two processes of signal forward propagation and error reverse transmission, and the BP neural network can learn a large number of input and output mapping relations without revealing a definite mathematical equation of the mapping relation. As shown in fig. 3, the BP neural network is composed of an input layer, an intermediate layer or an implicit layer and an output layer, and when a signal is transmitted in the forward direction, input sample data is transmitted from the input layer, and is transmitted to the output layer after implicit layer processing, information transformation and learning. If the output is not in accordance with the expectation, the error is reversely transmitted to the hidden layer and the input layer in a certain form, and the error is distributed to each layer in the process to be used as a basis for modifying each weight and threshold. With the repeated learning process, the weight and the threshold are continuously adjusted until the output error is reduced to an acceptable degree or reaches a preset learning time. Therefore, the reliability of predicting three important parameters of copper bottom blowing smelting, such as copper matte grade, iron-silicon ratio in smelting slag and slag temperature, can be further improved by constructing a copper bottom blowing smelting process neural network model by utilizing a modeling mechanism of a BP neural network and combining industrial production big data of the copper bottom blowing smelting process and training and optimizing the model by utilizing the production big data.

According to another embodiment of the invention, the intelligent coordinator can calculate the weighting coefficients of the bottom-blowing smelting furnace mechanism model and the bottom-blowing smelting furnace data driving model prediction method by fuzzy partition of the input variable region and a comprehensive method, namely, the intelligent coordinator integrates the two prediction models based on fuzzy partition, when the industrial production parameters of the bottom-blowing furnace copper smelting process change stably and the working condition is normal, the neural network model obtains larger weight, and the compensation function of the neural network model ensures the prediction precision; when the copper smelting balance of the bottom-blowing furnace is disturbed and the working condition is unstable, the mechanism model obtains higher weight, so that the global fitting capability of the intelligent integrated hybrid prediction model to the copper smelting process of the bottom-blowing furnace is ensured. Therefore, the two models are intelligently integrated by using the intelligent coordinator, so that the reliability of the integrated mixed model is greatly enhanced, and the artificial neural network formed by combining a large number of neurons can show the characteristics similar to the human brain, so that the mixed model has the self-adaption and self-organization capabilities, and the accuracy of a prediction result can be obviously improved. Further, f can be utilized₁A prediction result showing the output of the mechanism model of the bottom-blowing melting furnace is obtained by using f₂And the prediction result of the output of the data driving model of the bottom-blowing smelting furnace is represented by mu (x), the weighting coefficient of the data driving model of the bottom-blowing smelting furnace in the mixed model is represented by (1-mu (x)), and the prediction result of the output of the intelligent coordinator is as follows:

y＝f₂×μ(x)+f₁×(1-μ(x))，

wherein y represents a prediction result, the prediction result comprises a copper matte grade prediction value, a ferrosilicon ratio prediction value and a slag temperature prediction value, the value of a weighting coefficient mu (x) of the data driving model in the mixed model is obtained by a membership function, and the relation between the mu (x) and the membership function is as follows:

x represents a certain input variable, such as oxygen enrichment rate, coal blending rate, oxygen-material ratio, flux rate, charged Cu amount, charged Fe amount, charged S amount, charged CaO amount and charged SiO amount₂Amount, etc.; a. b, c and d are characteristic parameters, different input variables correspond to different characteristic parameters a, b, c and d, each characteristic parameter can obtain a reference initial value of a membership function parameter of each input variable by referring to a history record of actual industrial production, and is obtained by optimizing by using industrial data, for example, when the oxygen enrichment rate is taken as the input variable, the oxygen enrichment rate and the corresponding characteristic parameters a, b, c and d can be:

{ oxygen enrichment ratio a, b, c, d } - {0.57,0.64,0.69,0.72 }.

It should be noted that the values of the characteristic parameters a, b, c, and d are not fixed and may be adjusted according to actual production, for example, the values of the characteristic parameters a, b, c, and d are different from each other in different production conditions.

Further, the weighting coefficients of the data-driven model in the hybrid model can predetermine the input variables and the corresponding characteristic parameters a, b, c, d thereof to obtain the membership functions μ of the input variables_iThe value of the final weighting coefficient mu (x) is then calculated by means of a weighted average, i.e. the value of

Wherein, mu_iCalculating a membership function lambda of the input variable i and the corresponding characteristic parameters a, b, c and d_iIs the weight coefficient occupied by the input variable i in j input variables, lambda_iDetermined by empirical values.

According to yet another embodiment of the present invention, when there are 9 input variables, the weighting coefficient μ (x) of the data-driven model in the hybrid model is

Wherein, mu_i(i ═ 1,2, …, 9) is a membership function calculated from an input variable and its corresponding characteristic parameters a, b, c, d, and the membership function is calculatedOr 0, λ_i(i ═ 1,2, …, 9) is a weight coefficient that a certain input variable occupies among all the input variables, and can be determined with reference to an empirical value.

According to another embodiment of the invention, the mixed model can be corrected regularly or in real time based on the actual production result, so that the reliability and the accuracy of the online prediction method for the copper smelting process parameters of the oxygen bottom-blowing furnace can be further improved.

According to a further specific embodiment of the present invention, the correcting the mixture model may include: and comparing the final predicted values of the copper matte grade, the ferrosilicon ratio and the slag temperature output by the mixed model with actual measured values: if the error is in the expected range, keeping the weighting coefficient in the mixed model unchanged; if the error is outside the expected range, the final predicted value is returned to the intelligent coordinator, the weighting coefficient is adjusted, and the operations are repeated until the error is reduced to be within the expected range.

According to another embodiment of the invention, a flow chart of the on-line prediction method for parameters of the oxygen-enriched bottom-blowing smelting process based on the hybrid model can be shown in fig. 4, wherein the key process parameters are collected in real time by measuring actual parameters such as the feeding quantity of the on-site raw fuel and the oxygen-enriched gas flow in the oxygen-enriched bottom-blowing smelting process through detection sensors (weighing sensors, flow sensors and the like) and transmitting the actual parameters to the prediction model; the key parameters for inputting the off-line detection are key process parameters such as furnace copper discharge flow, components, furnace slag discharge flow, components, temperature and the like which are input on a human-computer interaction interface; the establishment of the online prediction model refers to the establishment of a mechanism model of the oxygen-enriched bottom blowing smelting process by using the principles of material balance, energy balance and phase balance, the establishment of a neural network model (data-driven model) by using production big data, and the integration of the mechanism model and the neural network model by using an intelligent coordinator to obtain a mixed model for predicting three important parameters of the bottom blowing furnace smelting process. The three important parameter prediction models in the bottom blowing furnace smelting process are preliminarily established to predict the three important parameters, the final prediction result is compared with an actual measurement value, if the error is within a required range, the models are established, the predicted values of the three important parameters in the bottom blowing furnace smelting process are input into a server database to be stored, and the server database is displayed on a computer interface; if the error is larger than the required range, returning to correct the model again, correspondingly adjusting the intelligent coordination coefficient, and repeating the steps until the error is reduced to the required range.

To sum up, according to the online prediction method for the parameters of the copper smelting process of the oxygen bottom-blowing furnace in the embodiment of the invention, by mainly investigating three important parameters, namely the copper matte grade, the iron-silicon ratio in the smelting slag and the slag temperature in the bottom-blowing smelting process, a mechanism model and a data driving model of the bottom-blowing smelting furnace are respectively established to predict the three important parameters, on one hand, the advantages of good extrapolation property and strong interpretability of the mechanism model are utilized, on the other hand, a neural network analysis method is adopted to carry out big data analysis and prediction on the three important parameters in the bottom-blowing smelting process, on the basis, a proper intelligent coordinator is designed to integrate the three important parameters, the prediction result of the integrated mixing model is compared and corrected with the actual production result, the mechanism model and the data driving model are continuously perfected, the parameters of the intelligent coordinator are corrected, and the prediction result meets the actual production result, thereby remarkably improving the prediction precision of the method on three parameters in the copper smelting process of the oxygen bottom blowing furnace. In conclusion, the prediction method can fully combine the advantages of the mechanism model and the data driving model, make full use of advantages and avoid disadvantages, remarkably improve the accuracy of the prediction result, effectively solve the problems of poor adaptability and unsatisfactory actual operation effect caused by singly applying one prediction method for modeling, and has great significance and value in theoretical and actual application.

In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.

Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.