CN111598293A - Online prediction method for copper converting process parameters of oxygen bottom blowing furnace - Google Patents
Online prediction method for copper converting process parameters of oxygen bottom blowing furnace Download PDFInfo
- Publication number
- CN111598293A CN111598293A CN202010281411.8A CN202010281411A CN111598293A CN 111598293 A CN111598293 A CN 111598293A CN 202010281411 A CN202010281411 A CN 202010281411A CN 111598293 A CN111598293 A CN 111598293A
- Authority
- CN
- China
- Prior art keywords
- model
- slag
- copper
- bottom blowing
- neural network
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 139
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 title claims abstract description 120
- 229910052802 copper Inorganic materials 0.000 title claims abstract description 115
- 239000010949 copper Substances 0.000 title claims abstract description 115
- 238000007664 blowing Methods 0.000 title claims abstract description 86
- 230000008569 process Effects 0.000 title claims abstract description 83
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 title claims abstract description 39
- 229910052760 oxygen Inorganic materials 0.000 title claims abstract description 39
- 239000001301 oxygen Substances 0.000 title claims abstract description 39
- 239000002893 slag Substances 0.000 claims abstract description 81
- 230000007246 mechanism Effects 0.000 claims abstract description 56
- 239000000463 material Substances 0.000 claims abstract description 46
- 238000004519 manufacturing process Methods 0.000 claims abstract description 21
- XEEYBQQBJWHFJM-UHFFFAOYSA-N iron Substances [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 claims abstract description 19
- 229910052742 iron Inorganic materials 0.000 claims abstract description 18
- 238000003062 neural network model Methods 0.000 claims abstract description 18
- 239000002994 raw material Substances 0.000 claims abstract description 7
- 238000013528 artificial neural network Methods 0.000 claims description 33
- XWHPIFXRKKHEKR-UHFFFAOYSA-N iron silicon Chemical compound [Si].[Fe] XWHPIFXRKKHEKR-UHFFFAOYSA-N 0.000 claims description 26
- 230000006870 function Effects 0.000 claims description 18
- 210000002569 neuron Anatomy 0.000 claims description 15
- 238000009776 industrial production Methods 0.000 claims description 8
- 238000005868 electrolysis reaction Methods 0.000 claims description 7
- 230000004907 flux Effects 0.000 claims description 7
- 239000000203 mixture Substances 0.000 claims description 5
- 229910000519 Ferrosilicon Inorganic materials 0.000 claims description 4
- 230000000694 effects Effects 0.000 abstract description 8
- 230000008901 benefit Effects 0.000 description 10
- 238000004364 calculation method Methods 0.000 description 4
- 238000001514 detection method Methods 0.000 description 4
- 238000012546 transfer Methods 0.000 description 4
- 230000015572 biosynthetic process Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 239000000155 melt Substances 0.000 description 3
- 238000012549 training Methods 0.000 description 3
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N Silicium dioxide Chemical compound O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 description 2
- 238000003723 Smelting Methods 0.000 description 2
- 210000004556 brain Anatomy 0.000 description 2
- 238000012512 characterization method Methods 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000007599 discharging Methods 0.000 description 2
- 238000013213 extrapolation Methods 0.000 description 2
- 239000000446 fuel Substances 0.000 description 2
- 239000007789 gas Substances 0.000 description 2
- 238000013507 mapping Methods 0.000 description 2
- 239000011159 matrix material Substances 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 206010054949 Metaplasia Diseases 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000005272 metallurgy Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003647 oxidation Effects 0.000 description 1
- 238000007254 oxidation reaction Methods 0.000 description 1
- 238000007670 refining Methods 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 229910052710 silicon Inorganic materials 0.000 description 1
- 239000010703 silicon Substances 0.000 description 1
- 235000012239 silicon dioxide Nutrition 0.000 description 1
- 239000000377 silicon dioxide Substances 0.000 description 1
- 238000000638 solvent extraction Methods 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 238000005303 weighing Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- C—CHEMISTRY; METALLURGY
- C22—METALLURGY; FERROUS OR NON-FERROUS ALLOYS; TREATMENT OF ALLOYS OR NON-FERROUS METALS
- C22B—PRODUCTION AND REFINING OF METALS; PRETREATMENT OF RAW MATERIALS
- C22B15/00—Obtaining copper
- C22B15/0026—Pyrometallurgy
- C22B15/0028—Smelting or converting
- C22B15/003—Bath smelting or converting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/04—Manufacturing
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Physics & Mathematics (AREA)
- Manufacturing & Machinery (AREA)
- Chemical & Material Sciences (AREA)
- Marketing (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Primary Health Care (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Development Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Materials Engineering (AREA)
- Mechanical Engineering (AREA)
- Metallurgy (AREA)
- Organic Chemistry (AREA)
- Manufacture And Refinement Of Metals (AREA)
Abstract
The invention discloses an on-line prediction method for parameters of a copper converting process of an oxygen bottom blowing furnace. The method comprises the following steps: establishing a bottom blowing converting 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 blowing converting furnace data driving model according to actual production data and based on a crude copper grade neural network model, a slag-silicon-iron 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 converting furnace mixing model related to a crude copper grade predicted value, a slag-silicon-iron ratio predicted value and a slag temperature predicted value, and outputting final predicted values of the crude copper grade, the slag-silicon-iron ratio and the slag temperature in the copper bottom blowing converting process by using the mixing model. The prediction method can effectively solve the problems that the existing prediction model and method are poor in adaptability and not ideal in actual operation effect, and the accuracy of the prediction result is obviously improved.
Description
Technical Field
The invention belongs to the field of metallurgy, and particularly relates to an on-line prediction method for parameters of a copper converting process of an oxygen bottom blowing furnace.
Background
The blowing process is an extremely important process in the copper smelting process, and the process of blowing hot copper matte, a flux and other raw materials (electrolysis anode scrap) into oxygen-enriched air at the bottom to generate oxidation exothermic reaction to form blister copper provides raw materials for the subsequent refining process. The oxygen bottom blowing copper converting furnace is the core equipment of the converting of the oxygen-enriched bottom blowing copper converting new process completely independent of intellectual property rights in China, and is also the equipment of the copper converting process commonly used in China at present, the converting process has the characteristics of continuity and instantaneity, the corresponding control technology is required to further exert the process characteristics, and the stable product quality and the furnace condition in the optimized state are ensured. Meanwhile, the copper converting process is a very complex process, is a multi-input and multi-output system, has the characteristics of strong coupling, time varying, distributed parameters, obvious uncertainty and the like among all variables, most key parameters in the converting process are difficult to detect in real time, time lag exists, operation is carried out only by depending on production experience of operators, converting end points are not easy to control, furnace conditions are not smooth, product quality fluctuates, and the exertion of the process advantages is restricted.
The three parameters of the crude copper grade, the iron-silicon ratio in the slag and the slag temperature are important parameters in the process of observing the copper converting of the oxygen bottom blowing furnace, and reflect the state in the process of oxygen bottom blowing copper converting. The raw copper grade refers to the mass fraction of copper element in the raw copper, the slag iron-silicon ratio refers to the mass ratio of iron element to silicon dioxide in the slag, the detection methods of the raw copper grade and the slag iron-silicon ratio all adopt sampling in the processes of rough copper discharging and slag discharging, then a fluorescence analyzer is adopted to test the components of the sample, and the slag iron-silicon ratio and the raw copper grade value are obtained through calculation. The key parameter in the oxygen bottom blowing copper converting process is the melt temperature, but the melt temperature cannot be directly measured on line due to the severe production environment, 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 crude copper grade of the product, the iron-silicon ratio in the slag and the slag temperature cannot be monitored in real time in the actual production of copper converting of the bottom blowing furnace. Generally, three important parameters, namely the crude copper grade, the iron-silicon ratio in converting slag and the slag temperature, are required to be kept in a proper range and have small fluctuation as much as possible in the copper converting process, so that the three important parameters in the bottom converting 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 converting 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 that the existing prediction model and method are poor in adaptability and not ideal in actual operation effect 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 converting process mainly adopts a mechanism modeling method or an intelligent modeling method based on data driving, but the two methods have the defects that: 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. Although the method relates to a parameter online prediction method based on a mixed model, the method actually corrects a mechanism model only through actual production data on the basis of establishing a complex mechanism model, and actually adopts a single mechanism model, so that the method has the defects of complex model establishing process, large calculation amount, small effect of actual data on the mechanism model and the like. For the bottom-blowing furnace blowing, parameters such as raw fuel conditions have obvious influence on the crude copper grade, the slag iron-silicon ratio and the slag temperature, so that the influence of raw material fluctuation on three important parameters of the blowing needs to be reduced by adopting optimization of a batching system and optimization of an operation system. The inventor imagines that a mechanism model and a data driving model can be combined to develop an integrated model for predicting three important parameters in copper smelting and converting, so that the important parameters of products can be predicted by inputting the parameters in the processes of feeding materials and converting, and the prediction model is corrected by comparing with the actual detection result of the products, 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 converting process parameter online prediction method. According to an embodiment of the present invention, the prediction method includes:
establishing a bottom blowing converting furnace mechanism model related to a crude copper grade predicted value, a slag iron silicon 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 blowing converting furnace data driving model related to a crude copper grade predicted value, a slag silicon iron ratio predicted value and a slag temperature predicted value according to actual production data and based on a crude copper grade neural network model, a slag silicon iron 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 converting furnace mixing model related to a crude copper grade predicted value, a slag-silicon-iron ratio predicted value and a slag temperature predicted value, outputting final predicted values of the crude copper grade, the slag-silicon-iron ratio and the slag temperature in the copper bottom blowing converting 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 predicted values of the crude copper grade, the predicted value of the slag-silicon-iron ratio and the slag temperature, which are output by the mechanism model and the data driving model respectively, and outputting the final predicted values of the crude copper grade, the slag-silicon-iron ratio and the slag temperature according to the weighting coefficients, the predicted values of the mechanism model and the predicted values of the data driving model.
According to the method for predicting the parameters of the copper converting process of the oxygen bottom blowing furnace in the embodiment of the invention, three important parameters, namely the raw copper grade, the iron-silicon ratio in converting slag and the slag temperature in the bottom blowing converting process, are mainly considered, the mechanism model and the data driving model of the bottom blowing converting furnace are respectively established to predict the three important parameters, an appropriate intelligent coordinator is designed on the basis to integrate the raw copper grade and the converting slag, 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 important parameters in the copper converting 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 modeling of the existing prediction method, and has great significance and value in both theoretical and actual applications.
In addition, the online prediction method for the copper converting 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 simultaneously solved by using METCAL software or METSIM software and the mechanism model is established by combining process characteristics of a copper bottom blowing process.
In some embodiments of the invention the blister copper grade neural network, the slag silicon iron ratio neural network and the slag temperature neural network each independently comprise a plurality of artificial neurons including, but not limited to, copper matte grade, copper matte temperature, oxygen matte ratio, flux rate, electrolysis stub rate and oxygen enrichment rate in a copper bottom blowing process.
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 process and a raw copper grade neural network, a slag-silicon-iron 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 crude copper grade, the slag 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 crude copper grade prediction value, a slag silicon-iron 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 copper matte grade, copper matte temperature, oxygen matte ratio, flux rate, electrolysis anode scrap rate and oxygen enrichment rate; 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, muiCalculating a membership function lambda of the input variable i and the corresponding characteristic parameters a, b, c and diIs the weight coefficient occupied by the input variable i in j input variables, lambdaiDetermined 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 blister copper grade, slag-to-silicon 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 converter copper converting 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 on-line prediction method for parameters of a copper converting process of an oxygen bottom blowing furnace. According to an embodiment of the present invention, referring to fig. 1, the prediction method includes: establishing a bottom blowing converting furnace mechanism model related to a crude copper grade predicted value, a slag iron silicon 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 blowing converting furnace data driving model related to a crude copper grade predicted value, a slag silicon iron ratio predicted value and a slag temperature predicted value according to actual production data and based on a crude copper grade neural network model, a slag silicon iron 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 converting furnace mixing model related to a crude copper grade predicted value, a slag-silicon-iron ratio predicted value and a slag temperature predicted value, and outputting final predicted values of the crude copper grade, the slag-silicon-iron ratio and the slag temperature in the copper bottom blowing converting 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 predicted value of the crude copper grade, the predicted value of the slag-silicon-iron ratio and the predicted value of the slag temperature, and the actual measured values of the crude copper grade, the slag-silicon-iron ratio and the slag temperature which are output by the mechanism model and the data driving model respectively, and outputting the final predicted values of the crude copper grade, the slag-silicon-iron ratio and the slag temperature according to the weighting coefficients, the predicted value of the mechanism model and the predicted value of the data driving model. The prediction method can fully combine the advantages of a mechanism model and a data driving model, makes good 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 modeling of the existing prediction method, and has great significance and value in theoretical and actual application.
The method for predicting the parameters of the copper converting 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, a material balance model is established based on a material balance equation, an energy balance model is established based on an energy balance equation, a multiphase balance model is established based on a multiphase balance equation, simultaneous solution is carried out on the material balance equation, the energy balance equation and the multiphase balance equation by adopting METCAL software or METSIM software, and a bottom blowing converting furnace mechanism model is established by combining process characteristics of a copper bottom blowing converting process, wherein:
the material balance equation is:
wherein,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 ac,eRepresents a specific element in a specific component;
and
respectively representing the mole fractions of the input item materials and the specific components of the specific materials in the input item materials;
and
respectively representing the input item materials and specific components of specific materials in the input item materials;
and
respectively representing the mole fraction of the input item material and the specific material in the input item material;
and
respectively representing input item materials and specific materials in the input item materials;
and
respectively representing the sum of each element of each component in the input material and the output material in the converting process;
and
respectively representing the sum of each component in the input material and the output material in the blowing process;
and
respectively representing the sum of the mass of each element of each component in the input item material and the output item material in the converting process, and representing the conservation of the elements, the components and the mass of the input item material and the output item material in the bottom blowing converting process, namely the conservation of the materials.
The energy balance equation is:
wherein, Δ H298,AiAs an input item AiStandard enthalpy of formation; Δ H298,BjTo output item BjStandard enthalpy of formation; cpAiAs an input item AiThe heat capacity of (c); cpBjTo output item BjThe heat capacity of (c); qLossIs the heat loss during the blowing 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 process, namely the 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 raypcIs the activity factor of the c component in the p phase; chi shapepcIs the mole fraction of the c component in the p phase; cpIs the number of components in the p phase; t is the temperature; r is a gas universal constant; n is a radical ofpcIs the mole number of the c component in the p phase. This equation indicates that the gibbs free energy of the system is minimized during the converting process, i.e., the system reaches 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 higher reliability such as METCAL, METSIM and the like is adopted to establish a bottom blowing converting furnace mechanism model by combining the process characteristics of the copper bottom blowing converting process, and three important parameters of predicting the crude copper grade, the iron-silicon ratio in converting slag and the slag temperature in the copper bottom blowing converting process are calculated.
According to another embodiment of the invention, the raw copper grade neural network, the slag ferrosilicon ratio neural network and the slag temperature neural network respectively and independently comprise a plurality of artificial neurons, wherein the artificial neurons comprise but are not limited to copper matte grade, copper matte temperature, oxygen matte ratio, flux rate, electrolysis residual anode rate and oxygen enrichment rate in the copper bottom blowing process. 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:
yj＝f(sj)＝f(wp+b)
wherein S isjAs an output function, bjIs a threshold value, wj,iTo connect weight, x0＝bj，wj,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 input
And a threshold value bjIn 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 data driving model of the bottom blowing converting furnace can be established by utilizing a modeling mechanism of the BP neural network and combining industrial production big data of the copper bottom blowing converting process, a raw copper grade neural network, a slag-silicon-iron 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 the copper bottom blowing, namely the crude copper grade, the iron-silicon ratio in the blowing slag and the slag temperature, can be further improved by constructing a copper bottom blowing process neural network model by utilizing the modeling mechanism of the BP neural network and combining industrial production big data of the copper bottom blowing 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 prediction methods of the mechanism model and the data driving model of the bottom blowing converting furnace by adopting a fuzzy division input variable area and comprehensive method, namely, the intelligent coordinator integrates the two prediction models based on fuzzy division, when the industrial production parameters of the copper converting process of the bottom blowing converting furnace 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 converting balance of the bottom blowing furnace is disturbed and the working condition is unstable, the mechanism model obtains larger weight, so that the global fitting capacity of the intelligent integrated hybrid prediction model to the copper converting 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 utilized1The prediction result output by the bottom blowing converting furnace mechanism model is expressed by using f2And the prediction result of the output of the intelligent coordinator is represented by a prediction result of the data driving model of the bottom blowing converting furnace, the weighting coefficient of the data driving model of the bottom blowing converting furnace in the mixed model is represented by mu (x), and the weighting coefficient of the mechanism model of the bottom blowing converting furnace in the mixed model is represented by (1-mu (x)), so that 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 crude copper grade prediction value, a slag silicon iron 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
x represents a certain input variable, such as copper matte grade, copper matte temperature, oxygen matte ratio, flux rate, electrolysis anode scrap rate, oxygen enrichment rate and the like; 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 industrial data, for example, when the copper matte grade is taken as an input variable, the copper matte grade and the corresponding characteristic parameters a, b, c and d can be:
{ copper matte grade | a, b, c, d } - { 60%, 67%, 72%, 75% }
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 variablesiThe value of the final weighting coefficient mu (x) is then calculated by means of a weighted average, i.e. the value of
Wherein, muiCalculating a membership function lambda of the input variable i and the corresponding characteristic parameters a, b, c and diIs the weight coefficient occupied by the input variable i in j input variables, lambdaiDetermined by empirical values.
According to yet another embodiment of the present invention, when there are 6 input variables, the weighting coefficient μ (x) of the data-driven model in the hybrid model is
Wherein, mui(i ═ 1,2, …, 6) is a membership function calculated from an input variable and its corresponding characteristic parameters a, b, c, d, and the membership function is
1、
Or 0, λi(i-1, 2, …, 6) is a weight coefficient that a certain input variable occupies among all input variables, and can be determined by referring 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 converting 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 crude copper grade, the slag 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 method for predicting parameters of the oxygen-enriched bottom blowing process on line 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 amount of the raw fuel and the oxygen-enriched gas flow in the oxygen-enriched bottom blowing process through a detection sensor (a weighing sensor, a flow sensor, etc.) and transmitting the actual parameters to the prediction model; the key parameters for inputting the off-line detection are key process parameters such as the flow and the composition of the discharged copper, the flow and the composition of the discharged slag, the 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 process by using the principles of material balance, energy balance and phase balance, the establishment of a neural network model (data driving 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 blowing process. Predicting three important parameters by utilizing a preliminarily established three-important-parameter prediction model in the blowing process of the bottom blowing furnace, comparing a final prediction result with an actual measurement value, if an error is within a required range, establishing the model, inputting the predicted values of the three important parameters of the bottom blowing furnace into a server database for storage, and displaying the values 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 converting process of the oxygen bottom-blowing furnace in the embodiment of the invention, by mainly investigating three important parameters, namely the grade of crude copper, the iron-silicon ratio in converting slag and the slag temperature in the bottom-blowing converting process, a bottom-blowing converting furnace mechanism model and a data driving model are respectively established to predict the three important parameters, on one hand, the advantages of good extrapolation 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 converting 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 converting 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 modeling of the existing prediction method, and has great significance and value in both theoretical and actual applications.
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.
Claims (9)
1. An oxygen bottom blowing furnace copper converting process parameter on-line prediction method is characterized by comprising the following steps:
establishing a bottom blowing converting furnace mechanism model related to a crude copper grade predicted value, a slag iron silicon 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 blowing converting furnace data driving model related to a crude copper grade predicted value, a slag silicon iron ratio predicted value and a slag temperature predicted value according to actual production data and based on a crude copper grade neural network model, a slag silicon iron 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 converting furnace mixing model related to a crude copper grade predicted value, a slag-silicon-iron ratio predicted value and a slag temperature predicted value, outputting final predicted values of the crude copper grade, the slag-silicon-iron ratio and the slag temperature in the copper bottom blowing converting 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 predicted values of the crude copper grade, the predicted value of the slag-silicon-iron ratio and the slag temperature, which are output by the mechanism model and the data driving model respectively, and outputting the final predicted values of the crude copper grade, the slag-silicon-iron ratio and the slag temperature according to the weighting coefficients, the predicted values of the mechanism model and the predicted values of the data driving model.
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 converting process.
3. The on-line prediction method of claim 1, wherein the blister copper grade neural network, the slag silicon iron ratio neural network and the slag temperature neural network each independently comprise a plurality of artificial neurons including but not limited to copper matte grade, copper matte temperature, oxygen matte ratio, flux rate, electrolysis stub rate and oxygen enrichment rate in a copper bottom blowing process.
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 and a blister copper grade neural network, a slag silicon iron ratio neural network and a slag temperature neural network of a copper bottom blowing process.
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 crude copper grade, the slag 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 crude copper grade prediction value, a slag silicon-iron 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 copper matte grade, copper matte temperature, oxygen matte ratio, flux rate, electrolysis anode scrap rate and oxygen enrichment rate; 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, muiCalculating a membership function lambda of the input variable i and the corresponding characteristic parameters a, b, c and diIs the weight coefficient occupied by the input variable i in j input variables, lambdaiDetermined by empirical values.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010281411.8A CN111598293A (en) | 2020-04-10 | 2020-04-10 | Online prediction method for copper converting process parameters of oxygen bottom blowing furnace |
| PCT/CN2021/080600 WO2021203912A1 (en) | 2020-04-10 | 2021-03-12 | Online prediction method for parameters in copper converting process based on oxygen bottom blowing furnace |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010281411.8A CN111598293A (en) | 2020-04-10 | 2020-04-10 | Online prediction method for copper converting process parameters of oxygen bottom blowing furnace |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111598293A true CN111598293A (en) | 2020-08-28 |
Family
ID=72184921
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010281411.8A Pending CN111598293A (en) | 2020-04-10 | 2020-04-10 | Online prediction method for copper converting process parameters of oxygen bottom blowing furnace |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN111598293A (en) |
| WO (1) | WO2021203912A1 (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112434961A (en) * | 2020-12-01 | 2021-03-02 | 内蒙古科技大学 | Method and device for predicting temperature drop of molten iron on iron-steel interface and terminal equipment |
| WO2021203912A1 (en) * | 2020-04-10 | 2021-10-14 | 中国恩菲工程技术有限公司 | Online prediction method for parameters in copper converting process based on oxygen bottom blowing furnace |
| CN114217592A (en) * | 2021-12-17 | 2022-03-22 | 北京瑞太智联技术有限公司 | Control system and method for copper top-blowing production process |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114219175B (en) * | 2021-12-28 | 2024-07-19 | 鞍钢集团自动化有限公司 | Mechanical property prediction method for container coiled plate |
| CN114593663B (en) * | 2022-02-23 | 2023-10-03 | 本钢板材股份有限公司 | Secondary current model-based refining LF slag thickness measurement method |
| CN116258087B (en) * | 2023-05-15 | 2023-08-01 | 矿冶科技集团有限公司 | Matte grade soft measurement method and device, electronic equipment and storage medium |
| CN117690505B (en) * | 2024-01-25 | 2024-06-18 | 昆明理工大学 | Method for predicting key parameters of oxygen-enriched bottom-blown copper smelting by LSTM fusion mechanism model |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101353729A (en) * | 2008-07-18 | 2009-01-28 | 中南大学 | Intelligent integrated modelling approach based on operating condition judgment |
| CN104328285A (en) * | 2014-10-29 | 2015-02-04 | 中国科学院沈阳自动化研究所 | Hybrid-model-based on-line estimation method of oxygen-enriched bottom blowing copper smelting process parameters |
| CN105624425A (en) * | 2014-11-05 | 2016-06-01 | 中国科学院沈阳自动化研究所 | Intelligent control method of oxygen bottom blowing copper smelting process |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| RU2297463C1 (en) * | 2006-02-09 | 2007-04-20 | Государственное образовательное учреждение высшего профессионального образования "Санкт-Петербургский государственный горный институт имени Г.В. Плеханова (технический университет)" | Gas mode control method of conversion in converter for non-ferrous metallurgy |
| CN101482750B (en) * | 2009-02-06 | 2011-10-26 | 北京矿冶研究总院 | Cobalt oxalate granularity prediction method in hydrometallurgical synthesis process |
| CN104296801B (en) * | 2014-06-12 | 2017-01-18 | 东北大学 | Hydrometallurgy thick washing process key variable detection method |
| CN111554353A (en) * | 2020-04-10 | 2020-08-18 | 中国恩菲工程技术有限公司 | On-line prediction method for parameters of copper smelting process of oxygen bottom-blowing furnace |
| CN111598293A (en) * | 2020-04-10 | 2020-08-28 | 中国恩菲工程技术有限公司 | Online prediction method for copper converting process parameters of oxygen bottom blowing furnace |
-
2020
- 2020-04-10 CN CN202010281411.8A patent/CN111598293A/en active Pending
-
2021
- 2021-03-12 WO PCT/CN2021/080600 patent/WO2021203912A1/en active Application Filing
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101353729A (en) * | 2008-07-18 | 2009-01-28 | 中南大学 | Intelligent integrated modelling approach based on operating condition judgment |
| CN104328285A (en) * | 2014-10-29 | 2015-02-04 | 中国科学院沈阳自动化研究所 | Hybrid-model-based on-line estimation method of oxygen-enriched bottom blowing copper smelting process parameters |
| CN105624425A (en) * | 2014-11-05 | 2016-06-01 | 中国科学院沈阳自动化研究所 | Intelligent control method of oxygen bottom blowing copper smelting process |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021203912A1 (en) * | 2020-04-10 | 2021-10-14 | 中国恩菲工程技术有限公司 | Online prediction method for parameters in copper converting process based on oxygen bottom blowing furnace |
| CN112434961A (en) * | 2020-12-01 | 2021-03-02 | 内蒙古科技大学 | Method and device for predicting temperature drop of molten iron on iron-steel interface and terminal equipment |
| CN112434961B (en) * | 2020-12-01 | 2022-04-15 | 内蒙古科技大学 | Method and device for predicting temperature drop of molten iron on iron-steel interface and terminal equipment |
| CN114217592A (en) * | 2021-12-17 | 2022-03-22 | 北京瑞太智联技术有限公司 | Control system and method for copper top-blowing production process |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2021203912A1 (en) | 2021-10-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111598293A (en) | Online prediction method for copper converting process parameters of oxygen bottom blowing furnace | |
| CN111554353A (en) | On-line prediction method for parameters of copper smelting process of oxygen bottom-blowing furnace | |
| Chen et al. | Modelling and optimization of fed-batch fermentation processes using dynamic neural networks and genetic algorithms | |
| CN107526927B (en) | Blast furnace molten iron quality online robust soft measurement method | |
| Wang et al. | Applying input variables selection technique on input weighted support vector machine modeling for BOF endpoint prediction | |
| CN107299170B (en) | A kind of blast-melted quality robust flexible measurement method | |
| CN110609476B (en) | Multivariate nonlinear dynamic system model prediction control method based on Gaussian process model | |
| CN102540879A (en) | Multi-target evaluation optimization method based on group decision making retrieval strategy | |
| Jiang et al. | Real-time moisture control in sintering process using offline–online NARX neural networks | |
| CN106503919A (en) | A kind of power distribution network evaluation methodology based on power supply zone characteristic | |
| CN104778361B (en) | The method of modified EMD Elman neural network prediction molten iron silicon contents | |
| CN109359320B (en) | Blast furnace index prediction method based on multiple sampling rate autoregressive distribution hysteresis model | |
| CN113011102B (en) | Multi-time-sequence-based Attention-LSTM penicillin fermentation process fault prediction method | |
| CN112083694A (en) | Feedback control method and device for oxygen bottom blowing copper converting process and electronic equipment | |
| CN107511823A (en) | The method of robot manipulating task track optimizing analysis | |
| CN101285816A (en) | Copper matte air refining procedure parameter soft sensing instrument and its soft sensing method | |
| Li et al. | Dual ensemble online modeling for dynamic estimation of hot metal silicon content in blast furnace system | |
| Ndiaye et al. | Qualitative modelling of a multi-step process: The case of French breadmaking | |
| Yang et al. | Modeling and optimal-setting control of blending process in a metallurgical industry | |
| US11987855B2 (en) | Method and system for determining converter tapping quantity | |
| Mi et al. | Prediction of accumulated temperature in vegetation period using artificial neural network | |
| CN112070408A (en) | Agglomerate composition forecasting model based on big data and deep learning | |
| CN110643485A (en) | Automatic control system and method for temperature field in grain fermentation process with intelligent prediction compensator | |
| CN103675009B (en) | The industrial melt index soft measurement instrument of fuzzifying equation and method | |
| CN115081301A (en) | Dynamic carbon emission evolution method based on mixed PSO-MKPLS |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200828 |