CN116705211B - Digital twin-based online prediction method and system for copper loss rate of oxygen-enriched copper molten pool - Google Patents
Digital twin-based online prediction method and system for copper loss rate of oxygen-enriched copper molten pool Download PDFInfo
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Abstract
The invention discloses an online prediction method and an online prediction system for a copper loss rate of an oxygen-enriched copper molten pool based on digital twinning, and belongs to the technical field of intelligent control of the smelting process of the oxygen-enriched copper molten pool. According to the prediction method, a full-element digital twin model is constructed according to the smelting process of an oxygen-enriched copper molten pool, the grade of copper sulfide ores and the target copper loss rate are conveyed into the digital twin model to obtain initial operation parameters, and the digital twin model is utilized to be in virtual-real linkage with smelting equipment to continuously optimize the operation parameters, so that the copper loss rate in smelting is controlled within a target range; and drawing a copper loss rate trend curve by combining with the real-time prediction of the copper loss rate in the smelting process. The prediction method provided by the invention can optimize smelting operation, reduce copper loss in the smelting process and improve production efficiency. The prediction system can control the loss rate of copper in the production process within a target range through the mutual coordination of the modules, reduces the energy consumption and the production cost, and has wide application prospect.
Description
Technical Field
The invention belongs to the technical field of intelligent control of an oxygen-enriched copper molten pool smelting process, and particularly relates to an online prediction method and an online prediction system for a copper loss rate of an oxygen-enriched copper molten pool based on digital twinning.
Background
The copper industry is one of the backbones of the chinese processing industry. The new modern intensified smelting technology is developed and applied in the beginning of the 70 th century in various places of the world to replace the traditional smelting technology, and oxygen-enriched smelting is a new technology which is independently developed by China on the basis of Waniekov smelting, and the new technology is rapidly developed and technology is gradually matured since the new technology is automatically put into industrial application. However, many key parameters in the oxygen-enriched copper bath smelting process cannot be measured in real time and predicted, and part of data information is mutually independent. This results in high copper loss rate in the oxygen-enriched copper bath smelting process, which results in great copper loss and greatly influences the overall industrial benefit.
At present, aiming at the problem that the copper loss rate in the smelting process cannot be measured and predicted on line in China, the national academy of sciences Shenyang automation institute provides an on-line estimation method for the copper loss rate in the smelting process in a mode of mixing mechanism and experience modeling so as to reduce copper loss. However, because of the empirical parameters, accurate estimation cannot be made under the condition of large fluctuation of working conditions, and the problems of high copper loss rate in the smelting process cannot be effectively solved due to the characteristics of multivariable data, nonlinearity, mixed analog quantity and discrete quantity and the like. Aiming at the difficulty in adjusting equipment parameters in the smelting process, foreign students propose to divide the smelting process into two units and calculate the current copper loss rate according to the copper production amount of a first unit, so that the equipment parameters are adjusted to reduce the copper loss, but the copper loss cannot be reduced to the greatest extent due to long production flow of the two smelting units and time delay of the adjusting process. The scheme of series connection of an oxygen-enriched smelting furnace, a depletion electric furnace and a continuous blowing furnace is proposed by the limited company of the copper industry of the smoke counter Penghui, the measurement and the prediction of the copper loss rate are realized to a certain extent, the information isolation is eliminated, the copper loss is controlled in a proper range, but the scheme can only aim at a small-sized molten pool, the smelting yield is limited, and the service requirement cannot be met.
Therefore, the invention provides the online prediction method and the online prediction system for the copper loss rate of the oxygen-enriched copper molten pool based on digital twinning, which can realize the data sharing of physical equipment and virtual equipment in the smelting process, and further realize the virtual-real linkage between the physical equipment and the virtual equipment. Therefore, the operation parameters are continuously optimized, and finally, the real-time prediction of the copper loss rate in the smelting process is realized, and the copper loss rate is kept in a target range.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an online prediction method and an online prediction system for the copper loss rate of an oxygen-enriched copper molten pool based on digital twinning. The method can realize the data sharing of the physical equipment and the virtual equipment in the smelting process, and further realize the virtual-real linkage between the physical equipment and the virtual equipment. Therefore, the operation parameters are continuously optimized, and finally, the real-time prediction of the copper loss rate in the smelting process is realized, and the copper loss rate is kept in a target range.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows: the online prediction method for the copper loss rate of the oxygen-enriched copper molten pool based on digital twin comprises the following steps:
(1) Constructing a digital twin model of the copper smelting process comprising operation parameters, process parameters and quality indexes based on the smelting process of the oxygen-enriched copper molten pool;
(2) Inputting the copper sulfide ore grade of 3% and the copper loss rate of less than 2% in the smelting process into a digital twin model, and obtaining the operation parameters alpha of each process link through initial simulation;
(3) The method comprises the steps that an initialization operation parameter alpha obtained through digital twin model simulation is transmitted to entity smelting equipment, the entity smelting equipment starts to operate according to the initialization operation parameter alpha, and a real-time operation parameter beta in the operation process of the smelting equipment is collected;
(4) Based on a real-time operation parameter beta of smelting equipment, predicting the copper loss rate in the smelting process through a digital twin model;
(5) Comparing the predicted copper loss rate with a set target value, and judging whether the predicted copper loss rate is less than 2%;
(6) If the predicted copper loss rate exceeds 2%, optimizing the operation parameters through a digital twin model, and inputting the optimized operation parameters gamma into smelting equipment to continue operation; if the predicted final copper loss rate is lower than 2%, then judging whether the copper making process of the current smelting equipment is finished;
(7) If the smelting copper is not finished, the equipment continues to operate, the actual operation parameter beta is obtained, and the steps (4) - (6) are repeated; and if the copper production is completed, ending copper smelting in the period of time.
As a preferred embodiment of the invention, the operating parameters include blast volume, smelting time, slag blowing time, flux usage; the process parameters comprise furnace temperature, oxygen enrichment concentration and wind pressure, and the quality indexes comprise copper loss rate.
As a preferred embodiment of the invention, the operation parameters alpha, the real-time operation parameters beta and the optimized operation parameters gamma comprise the furnace temperature, the flux consumption, the smelting time, the oxygen enrichment concentration, the air quantity, the air pressure in the FSF flash smelting furnace, and the furnace temperature, the flux consumption, the smelting time, the oxygen enrichment concentration, the air quantity, the air pressure and the slag blowing time in the PSC converter.
As a preferred embodiment of the invention, the digital twin model firstly builds a virtual production line of the smelting process through a virtual modeling technology based on the multi-data information of the physical production line, and performs capacity analysis and production balance optimization on a process scheme through simulation and previewing of a digital model of physical equipment, thereby building the digital twin model capable of truly reflecting the physical production environment from multiple dimensions for the copper smelting production line; modeling demand analysis is carried out on physical equipment in a production line, wherein the modeling demand analysis comprises mapping of geometric physics, state and rule characteristics of the physical equipment, so that the twin model can truly express specific motion of the physical model in the real world.
The digital twin model comprises a physical entity and a digital twin body, wherein the physical entity executes an industrial process of copper smelting and mainly comprises production equipment, transportation equipment, materials and environmental entity objects, and the manufacturing, processing, transportation and storage of copper products are completed through the combination and cooperative operation of units of a copper smelting production line; the digital twin body mainly completes data fusion, namely, the data of physical equipment and the data of virtual equipment in the smelting process are connected and interacted; the digital twin reflects the mapping relation between physical equipment and virtual equipment of the copper smelting production line; the real-time mapping of the physical equipment data is realized by a sensor, a PLC and the like, and the data transmission is carried out with the virtual equipment; data from the virtual devices may also be sent back to the physical devices via the interface in reverse, in conjunction with the controller to achieve corresponding production control.
The online prediction system for the copper loss rate of the oxygen-enriched copper molten pool based on digital twinning comprises a data acquisition module, a real-time prediction module, a process parameter optimization module and a communication service module; in the smelting process, the data acquisition module acquires real-time operation parameters beta in the smelting process, the communication service module transmits the acquired real-time operation parameters beta to the real-time prediction module, the current copper loss rate is predicted, if the copper loss rate does not meet the requirement, the process parameter optimization module optimizes the operation parameters according to the current working condition, and the optimized operation parameters gamma are transmitted to corresponding smelting equipment through the communication service module; the communication service module is used for data transmission among the data acquisition module, the real-time prediction module and the process parameter optimization module, so that real-time transmission of data of each module is ensured.
As a preferred embodiment of the present invention, the data acquisition module includes a data acquisition unit, a data preprocessing unit; the data acquisition unit acquires parameters in the running process of the smelting equipment in real time; the data preprocessing unit removes extreme values, fills up missing values and performs normalization preprocessing on the parameters acquired by the data acquisition unit to obtain real-time operation parameters beta.
As a preferred embodiment of the present invention, the real-time prediction module includes a feature extraction unit, an algorithm library unit, and a visualization unit; the feature extraction unit receives data from the data acquisition module, and then extracts features to reduce the dimension of the data and capture key information; the algorithm library unit comprises different prediction algorithms and models, and uses the feature data acquired from the feature extraction unit to apply proper algorithms and models to conduct real-time prediction; the visualization unit visualizes the prediction result, so that a user can intuitively understand and analyze the prediction result.
As a preferred embodiment of the present invention, the feature extraction unit extracts features using principal component analysis, wavelet transform, or statistical feature method.
As a preferred embodiment of the present invention, the algorithm library unit comprises a regression model, a decision tree, a neural network model.
As a preferred embodiment of the present invention, the process parameter optimization module includes an optimization algorithm unit, a model verification unit, and a feedback control unit; the optimization algorithm unit adopts an intelligent algorithm based on deep learning, and deeply fuses the collected real-time operation parameters into material flow and information flow, so as to finish accurate parameter optimization; the model verification unit inputs the optimized operation parameters to the flow simulator, the simulator calculates a predicted result of the smelting process under a given operation condition according to the input parameters, and the accuracy of the model is verified by comparing the predicted result with an actually observed result; the feedback control unit uses the verified optimized operation parameters to adjust the operation state of smelting equipment, so as to realize accurate adjustment of the copper smelting process.
The process parameter optimization module can optimize the process parameters in the smelting process, and comprises the following specific steps:
firstly, determining an optimization target, namely controlling the copper loss rate within a target range; secondly, collecting parameter data related to a copper smelting process, wherein the parameter data comprise ore components, furnace temperature, slag temperature, oxygen enrichment concentration, air quantity, air pressure and the like; then, carrying out statistics and analysis on the data, and finding out key parameters affecting the copper loss rate through a statistical method, regression analysis and the like; next, searching for an optimal parameter combination using an optimization algorithm including a genetic algorithm, an ant colony algorithm, a simulated degradation algorithm, and the like; finally, the process parameters are continuously monitored, and necessary adjustment and improvement are carried out according to the actual situation so as to keep the copper loss rate within a reasonable range.
As a preferred embodiment of the present invention, the communication service module includes a data transmission unit, a communication control unit, a data decoding unit; the data transmission unit packs the real-time parameters acquired by the data acquisition module to smelting equipment according to a unified data format and then transmits the real-time parameters to the corresponding modules; the communication control unit is used for constructing an end-to-end communication service model and specifically comprises an oxygen-enriched copper melting pool smelting equipment client, a data acquisition module server and a real-time prediction client; the data decoding unit receives the analog signals obtained from the sensor and converts the analog signals into digital quantities, so that a data source is provided for a subsequent real-time prediction and process parameter optimization module.
Compared with the prior art, the invention has the beneficial effects that: according to the digital twin-based oxygen-enriched copper molten pool copper loss rate online prediction method, the influence of operation parameters of key equipment in the smelting process on the copper loss rate is considered, a digital twin model of the oxygen-enriched copper molten pool smelting process which can be dynamically interacted is established by utilizing a digital twin technology, the operation parameters are continuously optimized through the real-time prediction of the copper loss rate, the copper loss rate in the smelting process is within a target range, smelting operation is optimized, the copper loss in the whole smelting process is reduced, and the production efficiency and the enterprise benefit are improved. On the basis, the prediction system can control the loss rate of copper in the production process within a target range through the mutual matching of the modules, reduces the energy consumption and the production cost, and has wide application prospect.
Drawings
FIG. 1 is a flow chart of an online prediction method for copper loss rate of an oxygen-enriched copper molten pool based on digital twinning.
FIG. 2 is a digital twin model of an oxygen-enriched copper bath smelting process constructed in accordance with the present invention.
FIG. 3 is a digital twin-based online prediction system for copper loss rate of an oxygen-enriched copper bath according to the present invention.
FIG. 4 is a graph of predicted copper loss rates for the method and system of the present invention.
Detailed Description
For a better description of the objects, technical solutions and advantages of the present invention, the present invention will be further described with reference to the following specific examples.
Example 1
In the embodiment, the test of online prediction of the copper loss rate is carried out by using the oxygen-enriched copper molten pool smelting process in the actual industrial site. The grade of copper sulfide ore is 3%, the target copper loss rate is not more than 2%, 1 stage Flash Smelting Furnace (FSF flash smelting furnace) is arranged in the smelting link, 3 Pearce-Smith converters (PSC converters) are arranged in the smelting link, the smelting temperature of the two converters is between 1200 and 1400 ℃, and the smelting link is a key device for improving the copper content in melt.
An online prediction system for copper loss rate of an oxygen-enriched copper molten pool based on digital twin,
the data acquisition module comprises a data acquisition unit and a data preprocessing unit; the data acquisition unit acquires key parameters in the operation process of the smelting equipment in real time, such as oxygen enrichment concentration, air quantity, air pressure, furnace temperature, slag temperature and the like; the data preprocessing unit performs data preprocessing operations such as removing extreme values, filling missing values, normalizing and the like on the data acquired by the data acquisition unit.
The real-time prediction module comprises a feature extraction unit, an algorithm library unit and a visualization unit; the feature extraction unit receives the data from the data acquisition module, and then extracts features through methods such as principal component analysis, wavelet transformation or statistical features to reduce the dimension of the data and capture key information; the algorithm library unit comprises different prediction algorithms and models such as regression models, decision trees, neural network models and the like, and uses the feature data acquired from the feature extraction unit to apply appropriate algorithms and models for real-time prediction; the visualization unit visualizes the prediction result, so that a user can intuitively understand and analyze the prediction result. The process parameter optimization module comprises an optimization algorithm unit, a model verification unit and a feedback control unit; the optimization algorithm unit adopts an intelligent algorithm based on deep learning, and deeply fuses the collected real-time operation parameters into material flow and information flow, so as to finish accurate parameter optimization; the model verification unit inputs the optimized operation parameters to the flow simulator, the simulator calculates a predicted result of the smelting process under a given operation condition according to the input parameters, and the accuracy of the model is verified by comparing the predicted result with an actually observed result; the feedback control unit adjusts the operating state of the smelting equipment by using the verified optimization parameters.
The communication service module comprises a data transmission unit, a communication control unit and a data decoding unit; the data transmission unit packs the real-time parameters acquired by the data acquisition module to smelting equipment according to a unified data format and then transmits the real-time parameters to the corresponding modules; the communication control unit is used for constructing an end-to-end communication service model and specifically comprises an oxygen-enriched copper molten pool smelting equipment client, a data acquisition module server and a real-time prediction client; the data decoding unit receives the analog signals obtained from the sensor and converts the analog signals into digital quantities, so that a data source is provided for a subsequent real-time prediction and process parameter optimization module.
The data information of the smelting equipment comprises oxygen enrichment concentration, air quantity, air pressure, furnace temperature and slag temperature in the FSF flash smelting furnace, and oxygen enrichment concentration, air quantity, air pressure and slag blowing time in the PSC converter.
The online prediction method for the copper loss rate of the oxygen-enriched copper molten pool based on digital twin comprises the following steps:
s1: the online prediction system of the copper loss rate of the oxygen-enriched copper molten pool based on digital twinning is connected to smelting equipment in the smelting process of the oxygen-enriched copper molten pool.
S2: the construction of a digital twin model which comprises operation parameters, process parameters and quality indexes and can be interacted, calculated and optimized in the copper smelting process, wherein the grade of copper sulfide ore is 3 percent and the set target value is as follows: inputting the copper loss rate of <2% in the smelting process into a digital twin model; the operation parameters comprise blast volume, smelting time, slag blowing time and flux consumption; the process parameters comprise furnace temperature, oxygen enrichment concentration and wind pressure, and the quality indexes comprise copper loss rate.
S3: the digital twin model simulation obtains the initial operation parameters alpha of smelting equipment in each process link, including the oxygen enrichment concentration, the air quantity and the air pressure in the FSF flash smelting furnace, and the oxygen enrichment concentration, the air quantity, the air pressure and the slag blowing time in the PSC converter.
S4: and (3) conveying the initialized operation parameter alpha obtained in the step (S3) to smelting equipment through a communication service module in the system, and starting the operation of the smelting equipment according to the initialized operation parameter alpha.
S5: the data acquisition module in the system acquires real-time operation parameters beta in the operation process of smelting equipment, wherein the parameters comprise oxygen enrichment concentration, air quantity and air pressure in an FSF flash smelting furnace, and oxygen enrichment concentration, air quantity, air pressure and slag blowing time in a PSC converter.
S6: based on a real-time prediction module in the system, the real-time operation parameter beta of the smelting equipment, which is acquired by the data acquisition module, is extracted by a feature extraction unit to obtain features with high correlation with the copper loss rate, and then the real-time copper loss rate in the smelting process is predicted by a prediction algorithm and a model in an algorithm library unit.
S7: and comparing the predicted copper loss rate with a set target value, and judging whether the predicted copper loss rate is less than 2%.
S8: if the predicted copper loss rate is less than 2%, then judging whether the smelting process of the current smelting equipment is finished.
S9: if the predicted copper loss rate exceeds 2%, the parameter optimization module of the system optimizes the implementation operation parameter beta of the current smelting equipment until the predicted copper loss rate is less than 2%, and inputs the optimized operation parameter gamma into the smelting equipment to continue to operate.
S10: if the smelting process of the current smelting equipment is not completed, the equipment continues to operate, the actual operation parameter beta is obtained, the steps S6-9 are repeated, and the loss rate of copper is predicted. And if the smelting process of the current smelting equipment is finished, ending copper smelting in the period of time.
S11: according to the prediction result of the copper loss rate of the system, the online prediction of the copper loss rate of the oxygen-enriched copper molten pool can be realized, and the prediction result can be displayed in real time through a visualization unit of a real-time prediction module.
By observing the predicted result graph (figure 4) of the copper loss rate, the copper loss rate can be seen to continuously rise along with the continuous progress of the smelting process, when the system detects that the copper loss rate exceeds the target value by 2%, the system starts to optimize the operation parameters of smelting equipment, the copper loss rate starts to decline, and finally the copper loss rate is controlled within the range of 2%.
Compared with the traditional method, the method can predict the copper loss rate on line through smelting process parameters and operation parameters, can complete prediction without experience parameters, immediately makes a decision by a system to adjust, greatly reduces time lag, reduces the copper loss rate, and finally controls the copper loss within a target range. The method has the advantages that the prediction accuracy is higher, the lag in equipment parameter adjustment time is eliminated, and the method is not limited by the size of a molten pool, so that the loss of copper is reduced to the greatest extent.
Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the scope of the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted equally without departing from the spirit and scope of the technical solution of the present invention.
Claims (6)
1. The online prediction method for the copper loss rate of the oxygen-enriched copper molten pool based on digital twinning is characterized by comprising the following steps of:
(1) Constructing a digital twin model of the copper smelting process comprising operation parameters, process parameters and quality indexes based on the smelting process of the oxygen-enriched copper molten pool;
(2) Inputting the copper sulfide ore grade of 3% and the copper loss rate of less than 2% in the smelting process into a digital twin model, and obtaining the operation parameters alpha of each process link through initial simulation;
(3) The method comprises the steps that an initialization operation parameter alpha obtained through digital twin model simulation is transmitted to entity smelting equipment, the entity smelting equipment starts to operate according to the initialization operation parameter alpha, and a real-time operation parameter beta in the operation process of the smelting equipment is collected;
(4) Based on a real-time operation parameter beta of smelting equipment, predicting the copper loss rate in the smelting process through a digital twin model;
(5) Comparing the predicted copper loss rate with a set target value, and judging whether the predicted copper loss rate is less than 2%;
(6) If the predicted copper loss rate exceeds 2%, optimizing the operation parameters through a digital twin model, and inputting the optimized operation parameters gamma into smelting equipment to continue operation; if the predicted final copper loss rate is lower than 2%, then judging whether the copper making process of the current smelting equipment is finished;
(7) If the smelting copper is not finished, the equipment continues to operate, the actual operation parameter beta is obtained, and the steps (4) - (6) are repeated; and if the copper production is completed, ending copper smelting in the period of time.
2. The online prediction method for the copper loss rate of the oxygen-enriched copper molten pool based on digital twinning according to claim 1, wherein the operation parameters comprise blast volume, smelting time, slag blowing time and flux consumption; the process parameters comprise furnace temperature, oxygen enrichment concentration and wind pressure, and the quality indexes comprise copper loss rate.
3. The online prediction method of the copper loss rate of the oxygen-enriched copper molten pool based on digital twin according to claim 1, wherein the operation parameters alpha, the real-time operation parameters beta and the optimized operation parameters gamma comprise the furnace temperature, the flux consumption, the smelting time, the oxygen-enriched concentration, the air quantity and the air pressure in the FSF flash smelting furnace, and the furnace temperature, the flux consumption, the smelting time, the oxygen-enriched concentration, the air quantity, the air pressure and the slag blowing time in the PSC converter.
4. An online prediction system for copper loss rate of an oxygen-enriched copper molten pool based on digital twinning comprises a data acquisition module, a real-time prediction module, a process parameter optimization module and a communication service module; in the smelting process, the data acquisition module acquires real-time operation parameters beta in the smelting process, the communication service module transmits the acquired real-time operation parameters beta to the real-time prediction module, the current copper loss rate is predicted, if the copper loss rate does not meet the requirement, the process parameter optimization module optimizes the operation parameters according to the current working condition, and the optimized operation parameters gamma are transmitted to corresponding smelting equipment through the communication service module; the communication service module is used for data transmission among the data acquisition module, the real-time prediction module and the process parameter optimization module, so that real-time transmission of data of each module is ensured;
the real-time prediction module comprises a feature extraction unit, an algorithm library unit and a visualization unit; the feature extraction unit receives data from the data acquisition module, and then extracts features to reduce the dimension of the data and capture key information; the algorithm library unit comprises different prediction algorithms and models, and uses the feature data acquired from the feature extraction unit to apply proper algorithms and models to conduct real-time prediction; the visualization unit visualizes the prediction result, so that a user can intuitively understand and analyze the prediction result;
the process parameter optimization module comprises an optimization algorithm unit, a model verification unit and a feedback control unit; the optimization algorithm unit adopts an intelligent algorithm based on deep learning, and deeply fuses the collected real-time operation parameters into material flow and information flow, so as to finish accurate parameter optimization; the model verification unit inputs the optimized operation parameters to the flow simulator, the simulator calculates a predicted result of the smelting process under a given operation condition according to the input parameters, and the accuracy of the model is verified by comparing the predicted result with an actually observed result; the feedback control unit adjusts the operating state of the smelting equipment using the verified optimized operating parameters.
5. The online prediction system for the copper loss rate of the oxygen-enriched copper molten pool based on digital twinning according to claim 4, wherein the data acquisition module comprises a data acquisition unit and a data preprocessing unit; the data acquisition unit acquires parameters in the running process of the smelting equipment in real time; the data preprocessing unit removes extreme values, fills up missing values and performs normalization preprocessing on the parameters acquired by the data acquisition unit to obtain real-time operation parameters beta.
6. The online prediction system of the copper loss rate of the oxygen-enriched copper molten pool based on digital twinning according to claim 4, wherein the communication service module comprises a data transmission unit, a communication control unit and a data decoding unit; the data transmission unit packs the real-time parameters acquired by the data acquisition module to smelting equipment according to a unified data format and then transmits the real-time parameters to the corresponding modules; the communication control unit is used for constructing an end-to-end communication service model and specifically comprises an oxygen-enriched copper melting pool smelting equipment client, a data acquisition module server and a real-time prediction client; the data decoding unit receives the analog signals obtained from the sensor and converts the analog signals into digital quantities, so that a data source is provided for a subsequent real-time prediction and process parameter optimization module.
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