Smelting optimization method and device of copper-containing concentrate

Abstract

Provided are smelting optimization methods and devices of copper-containing concentrate. The smelting optimization method includes: obtaining a water content and physical parameter of copper-containing concentrate powder; in response to the water content meeting a first preset condition, mixing the copper-containing concentrate powder with oxygen-rich gas to obtain a mixture, spraying the mixture into a smelting furnace by a nozzle; determining a first production parameter for producing matte based on the physical parameter, an oxygen content of the oxygen-rich gas and a gas temperature of the oxygen-rich gas; determining a second production parameter for producing matte by processing the physical parameter, the oxygen content of the oxygen-rich gas and the gas temperature of the oxygen-rich gas using a prediction model, wherein the prediction model is a machine learning model; and determining a target production parameter based on the first production parameter and the second production parameter.

What is claimed is: 
     
         1 . A smelting optimization method of copper-containing concentrate, which is executed by at least one processing device, comprising:
 obtaining a water content and physical parameter of copper-containing concentrate powder;   in response to the water content meeting a first preset condition, mixing the copper-containing concentrate powder with oxygen-rich gas to obtain a mixture, spraying the mixture into a smelting furnace by a nozzle;   determining a first production parameter for producing matte based on the physical parameter, an oxygen content of the oxygen-rich gas and a gas temperature of the oxygen-rich gas;   determining a second production parameter for producing matte by processing the physical parameter, the oxygen content of the oxygen-rich gas and the gas temperature of the oxygen-rich gas using a prediction model, wherein the prediction model is a machine learning model; and   determining a target production parameter based on the first production parameter and the second production parameter.   
     
     
         2 . The smelting optimization method according to  claim 1 , wherein the determining a first production parameter for producing matte based on the physical parameter, the oxygen content of the oxygen-rich gas and the gas temperature of the oxygen-rich gas comprises:
 determining a target vector based on the physical parameter, the oxygen content of the oxygen-rich gas and the gas temperature of the oxygen-rich gas;   retrieving in a vector database based on the target vector to determine a matching vector and a production parameter corresponding to the matching vector; and   determining the production parameter corresponding to the matching vector as the first production parameter.   
     
     
         3 . The smelting optimization method according to  claim 1 , further comprising:
 obtaining a fracture surface image of hot matte based on an image recognition device;   determining a measured matte grade through a grade determination model based on the fracture surface image of hot matte, wherein the grade determination model is a machine learning model; and   updating the target production parameter based on the measured matte grade.   
     
     
         4 . The smelting optimization method according to  claim 3 , wherein the grade determination model comprises an image extraction layer, a correction layer and a judgment layer;
 an input of the image extraction layer includes the fracture surface image of the hot matte, and an output includes a fracture surface feature;   an input of the correction layer includes the fracture surface feature, a difference between a matte tapping time and an image acquisition time, and an output includes a corrected fracture surface feature; and   an input of the judgment layer includes the corrected fracture surface feature, and an output includes the measured matte grade.   
     
     
         5 . The smelting optimization method according to  claim 3 , further comprising:
 judging whether the measured matte grade, a measured matte temperature and a measured slag iron-silicon ratio meet a second preset condition; and   in response to the judgement that the measured matte grade, the measured matte temperature and the measured slag iron-silicon ratio do not meet the second preset condition, updating a frequency of obtaining the water content and a frequency of obtaining the physical parameter.   
     
     
         6 . The smelting optimization method according to  claim 5 , wherein the updating the frequency of obtaining the water content and the frequency of obtaining the physical parameter comprises:
 updating the frequency of obtaining the water content and the frequency of obtaining the physical parameter based on a detected difference value and a number of occurrences of abnormal differences; wherein   the detected difference value includes at least one of a difference between the measured matte temperature and a preset matte temperature, a difference between the measured matte grade and a preset matte grade, and a difference between the measured slag iron-silicon ratio and a preset slag iron-silicon ratio;   the number of occurrences of the abnormal differences includes a number of times the difference between the measured matte temperature and the preset matte temperature does not meet the first threshold, a number of times the difference between the measured matte grade and the preset matte grade does not meet the second threshold, and a difference between the measured slag iron-silicon ratio and the preset slag iron-silicon ratio does not meet the third threshold.   
     
     
         7 . The smelting optimization method according to  claim 5 , further comprising:
 judging whether the measured matte grade, the measured matte temperature and the measured slag iron-silicon ratio meet a third preset condition; and   updating the target production parameter in response to the judgement that the measured matte grade, the measured matte temperature and the measured slag iron-silicon ratio do not meet the third preset condition.   
     
     
         8 . The smelting optimization method according to  claim 1 , further comprising:
 determining a first update parameter based on a measured value and a preset value, wherein the measured value comprises a measured matte temperature, a measured matte grade and a measured slag iron-silicon ratio, and the preset value comprises a preset matte temperature, a preset matte grade and a preset slag iron-silicon ratio; and   updating the target production parameter based on the first update parameter.   
     
     
         9 . The smelting optimization method according to  claim 8 , wherein the determining a first update parameter based on the measured value and the preset value comprises:
 determining at least one group of candidate production parameters based on the target production parameter;   determining at least one predicted value based on the at least one group of candidate production parameters, wherein the predicted value comprises a predicted matte temperature, a predicted matte grade and a predicted slag iron-silicon ratio; and   determining the first update parameter based on the at least one predicted value and a symmetrical value, wherein the symmetrical value and the measured value are symmetrical based on the predicted value, and the symmetrical value includes a symmetrical matte temperature, a symmetrical matte grade and a symmetrical slag iron-silicon ratio.   
     
     
         10 . The smelting optimization method according to  claim 9 , wherein the updating the target production parameter based on the first update parameter comprises:
 determining a second update parameter based on the first production parameter and the first update parameter; and   updating the target production parameter based on the second update parameter.   
     
     
         11 . The smelting optimization method according to  claim 8 , wherein the measured matte grade is determined based on a grade determination model. 
     
     
         12 . A smelting optimization system for copper-containing concentrate, comprising:
 at least one storage medium comprising an instruction set for smelting optimization of copper-containing concentrate;   at least one processor in communication with the at least one storage medium, wherein when executing the instruction set, the at least one processor is configured to:   obtain a water content and physical parameter of copper-containing concentrate powder;   in response to the water content meeting a first preset condition, mix the copper-containing concentrate powder with oxygen-rich gas to obtain a mixture, spray the mixture into a smelting furnace by a nozzle;   determine a first production parameter for producing matte based on the physical parameter, an oxygen content of the oxygen-rich gas and a gas temperature of the oxygen-rich gas;   determine a second production parameter for producing matte by processing the physical parameter, the oxygen content of the oxygen-rich gas and the gas temperature of the oxygen-rich gas using a prediction model, wherein the prediction model is a machine learning model; and   determine a target production parameter based on the first production parameter and the second production parameter.   
     
     
         13 . The smelting optimization system according to  claim 12 , wherein the determining a first production parameter for producing matte based on the physical parameter, the oxygen content of the oxygen-rich gas and the gas temperature of the oxygen-rich gas comprises:
 determining a target vector based on the physical parameter, the oxygen content of the oxygen-rich gas and the gas temperature of the oxygen-rich gas;   retrieving in a vector database based on the target vector to determine a matching vector and a production parameter corresponding to the matching vector; and   
       determining the production parameter corresponding to the matching vector as the first production parameter. 
     
     
         14 . The smelting optimization system of  claim 12 , wherein the at least one processor is further configured to:
 obtain a fracture surface image of hot matte based on an image recognition device;   determine a measured matte grade through a grade determination model based on the fracture surface image of hot matte, wherein the grade determination model is a machine learning model; and   update the target production parameter based on the measured matte grade.   
     
     
         15 . The smelting optimization system according to  claim 14 , wherein the grade determination model comprises an image extraction layer, a correction layer and a judgment layer;
 an input of the image extraction layer includes the fracture surface image of the hot matte, and an output includes a fracture surface feature;   an input of the correction layer includes the fracture surface feature, a difference between a matte tapping time and an image acquisition time, and an output includes a corrected fracture surface feature; and   an input of the judgment layer includes the corrected fracture surface feature, and an output includes the measured matte grade.   
     
     
         16 . The smelting optimization system of  claim 14 , wherein the at least one processor is further configured to:
 judge whether the measured matte grade, a measured matte temperature and a measured slag iron-silicon ratio meet a second preset condition; and   in response to the judgement that the measured matte grade, the measured matte temperature and the measured slag iron-silicon ratio do not meet the second preset condition, update a frequency of obtaining the water content and a frequency of obtaining the physical parameter.   
     
     
         17 . The smelting optimization system according to  claim 16 , wherein the updating the frequency of obtaining the water content and the frequency of obtaining the physical parameter comprises:
 updating the frequency of obtaining the water content and the frequency of obtaining the physical parameter based on a detected difference value and a number of occurrences of abnormal differences; wherein   the detected difference value includes at least one of a difference between the measured matte temperature and a preset matte temperature, a difference between the measured matte grade and a preset matte grade, and a difference between the measured slag iron-silicon ratio and a preset slag iron-silicon ratio;   the number of occurrences of the abnormal differences includes a number of times the difference between the measured matte temperature and the preset matte temperature does not meet the first threshold, a number of times the difference between the measured matte grade and the preset matte grade does not meet the second threshold, and a difference between the measured slag iron-silicon ratio and the preset slag iron-silicon ratio does not meet the third threshold.   
     
     
         18 . The smelting optimization system of  claim 16 , wherein the at least one processor is further configured to:
 judge whether the measured matte grade, the measured matte temperature and the measured slag iron-silicon ratio meet a third preset condition; and   update the target production parameter in response to the judgement that the measured matte grade, the measured matte temperature and the measured slag iron-silicon ratio do not meet the third preset condition.   
     
     
         19 . A non-transitory computer-readable storage medium storing computer instructions, and after the computer reads the computer instructions in the storage medium, the computer implements the smelting optimization method of copper-containing concentrate according to  claim 1 . 
     
     
         20 . A smelting optimization device for copper-containing concentrate, comprising a water content monitoring device, a detection device, a conveying device, a smelting furnace and a processing device; wherein
 the water content monitoring device is used for obtaining a water content of copper-containing concentrate powder;   the detection device is used for obtaining a physical parameter of the copper-containing concentrate powder;   the conveying device is used for, in response to the water content meeting a first preset condition, mixing the copper-containing concentrate powder with oxygen-rich gas to obtain a mixture, spraying the mixture into a smelting furnace by a nozzle;   the smelting furnace is used for smelting the mixture; and   the processing device is used for:
 determining a first production parameter for producing matte based on the physical parameter, an oxygen content of the oxygen-rich gas and a gas temperature of the oxygen-rich gas; 
 determining a second production parameter for producing matte by processing the physical parameter, the oxygen content of the oxygen-rich gas and the gas temperature of the oxygen-rich gas using a prediction model, wherein the prediction model is a machine learning model; and 
 determining a target production parameter based on the first production parameter and the second production parameter.