Method and device for smelting copper through self-heating of thiocarbon and chaos stirring enhancement

Abstract

The invention relates to the technical field of copper metallurgy processes, and discloses a method and device for strengthening copper smelting through thiocarbon self-heating and chaotic stirring, and the method comprises the following steps: monitoring the temperature of a furnace kiln in real time by adopting a non-contact measurement technology; adding of mineral aggregates is adjusted according to the furnace temperature monitored in real time, the proportion of the added mineral aggregates is input into a sulfur-oxygen matching program, and the minimum threshold value of the oxygen inlet amount is calculated; inputting the furnace temperature, the slag thickness, the copper matte thickness and the minimum oxygen inlet amount threshold value which are monitored in real time into a nonlinear chaotic flow calculation program, and calculating the injection speed, the oxygen supply concentration and the injection angle; and after the smelting process is carried out, the temperature of the furnace kiln is fed back in real time according to the adjustment of the injection speed, the oxygen concentration and the injection angle. The device comprises a furnace top area, a furnace body area, a hearth area, a furnace wall, a feed opening, an uptake flue, a secondary air blowing gun, a copper matte discharge opening, a side blowing spray gun, a slagging chamber flue and a slagging opening. According to the invention, the energy utilization rate, the production efficiency and the product quality in the smelting process can be improved.

Description

Technical Field

[0001] The invention relates to the technical field of copper metallurgy, and in particular to a method and device for strengthening copper smelting by self-heating and chaotic stirring with sulfur-substituted carbon. Background Art

[0002] As an important metal material, copper is widely used in many fields, such as electronics, building decoration, and industrial equipment manufacturing. Since 2020, my country's annual output has exceeded 10 million tons, of which more than 80% is obtained by pyrometallurgy. The pyrometallurgy process of copper includes molten pool smelting, copper matte blowing, pyrometallurgy and electrolytic refining. Among them, molten pool smelting is used to obtain matte and is an important intermediate link. In the traditional molten pool smelting process, although the heat released by the oxidation of sulfur can supplement part of the heat, it is still necessary to supplement pulverized coal, methane and other fuels to maintain the high temperature of the furnace. In addition, during the smelting process, there is a lack of accurate evaluation and calculation of the heat released by the oxidation of sulfur, which makes it impossible to accurately control the feed amount of pulverized coal, methane and other fuels, resulting in energy waste, aggravated carbon emissions, and increased smelting costs for enterprises. At the same time, traditional molten pool smelting also has problems such as insufficient stirring, low heat and mass transfer efficiency, and insufficient oxygen-sulfur reaction, resulting in insufficient utilization of the oxidation heat of sulfur.

[0003] Prior art 1, Chinese patent, application number: 202311540120.6 discloses a low-carbon copper smelting system and method for copper sulfide concentrate, including a smelting unit, a flue gas separation unit, an organic matter production unit, a flue gas sulfur recovery unit and a flue gas acid production unit, and recovers the flue gas discharged from the oxygen-enriched smelting furnace, the copper matte blowing converter and the pyro-refining anode furnace. The recovered flue gas is separated into carbon dioxide flue gas, high-concentration sulfur dioxide flue gas and low-concentration sulfur dioxide flue gas by the flue gas separation unit. The organic matter production unit uses the separated carbon dioxide flue gas to synthesize methane and methanol. The methane produced by the organic matter production unit is also combined with the low-concentration sulfur dioxide separated from the flue gas to prepare sulfur in the flue gas sulfur recovery unit. The high-concentration sulfur dioxide obtained by recovering the flue gas is used to prepare sulfuric acid in the flue gas acid production system. Although low-carbon smelting of copper concentrate has been achieved. However, there is a lack of professional stirring technical means, resulting in insufficient stirring and low heat and mass transfer efficiency.

[0004] Prior art 2, Chinese patent, application number: 202310843138.7 discloses a copper smelting method in a bottom-blowing continuous blowing furnace, including: continuously flowing hot copper matte into the bottom-blowing continuous blowing furnace, adding slag-making flux at the same time, and continuously blowing to form a slag layer and a copper layer, and also including: controlling the slag type of the bottom-blowing continuous blowing to be ferro-calcium slag, and controlling the CaO / Fe by mass in the ferro-calcium slag to 0.330.37, and controlling the mass content of ferroferric oxide in the ferro-calcium slag to 40%55%. Although the copper content of the obtained slag is low, reaching less than 10.5%; and the furnace lining has good slag hanging, which can extend the life of the original full-hot bottom-blowing continuous blowing furnace by more than 50%. However, due to its single process means, the oxygen-sulfur reaction is insufficient, resulting in low oxidation heat utilization efficiency.

[0005] Prior art three, Chinese patent, application number: 202310971730.5 discloses a high oxygen-enriched nonlinear enhanced carbon-free copper smelting method, comprising the following steps: adjusting the sulfur-copper ratio of the copper concentrate to 1.11.8 by ore blending, and then adjusting the Fe-SiO2 mass ratio to 1.02.0 by premixing with flux to obtain a premix; subjecting the premix to nonlinear enhanced smelting under oxygen-enriched air conditions to obtain matte, slag and flue gas; and during the nonlinear enhanced smelting process, no carbonaceous fuel is added. Although reasonable batching is carried out according to the differences in the components of various copper concentrates, the sulfur-copper ratio in the mixed copper concentrate is limited, which can cause sulfides to undergo a violent oxidation reaction with high oxygen-enriched air during the smelting process, releasing high-energy heat, thereby avoiding the use of carbonaceous fuels and carbon dioxide emissions during the smelting process. However, the lack of efficient stirring technology during raw material mixing and reaction leads to uneven mixing of raw materials, which reduces the degree of chemical reaction to a certain extent and increases energy consumption.

[0006] At present, the existing technologies 1, 2 and 3 have the problems of insufficient stirring, low heat and mass transfer efficiency, insufficient oxygen-sulfur reaction, and insufficient oxidation heat of sulfur element. Therefore, the present invention provides a method and device for strengthening copper smelting by self-heating of sulfur-carbon and chaotic stirring, which increases the sulfur content of the material, does not add additional fuel, and only uses the oxidation heat of sulfur to maintain the high temperature of the furnace to meet the smelting needs. Summary of the invention

[0007] The main purpose of the present invention is to provide a method and device for strengthening copper smelting by self-heating and chaotic stirring of sulfur-carbon, so as to solve the problems in the prior art of insufficient stirring, low heat and mass transfer efficiency, insufficient oxygen-sulfur reaction, and insufficient utilization of the oxidation heat of sulfur element.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A method for strengthening copper smelting by self-heating and chaotic stirring of sulfur-substituted carbon, the method comprising:

[0010] Carry out initial ore mix and use non-contact measurement to monitor the kiln temperature in real time;

[0011] Adjust the addition of ore materials according to the real-time monitored kiln temperature. When the kiln temperature is lower than the expected range, add sulfur-rich ore; when the kiln temperature is higher than the expected range, add sulfur-poor ore; input the added ore proportion into the sulfur-oxygen matching program to calculate the minimum threshold of oxygen intake;

[0012] The real-time monitored furnace temperature, slag thickness, copper matte thickness and the minimum threshold of oxygen intake are input into the nonlinear chaotic flow calculation program to calculate the injection speed, oxygen supply concentration and injection angle. After the smelting process, the injection speed, oxygen concentration and injection angle are adjusted and fed back to the furnace temperature in real time to achieve dynamic control.

[0013] As a further improvement of the present invention, the process of initial ore proportioning and real-time monitoring includes:

[0014] Conduct multi-dimensional data analysis on the physical, chemical and mineralogical properties of the ore, establish a correlation model between the ore and the expected smelting effect, and optimize the initial ore ratio; at the same time, receive feedback on the furnace temperature, and dynamically optimize the ratio strategy based on the feedback of different furnace problems;

[0015] According to the volume of the kiln, several thermal imaging cameras and infrared sensors are arranged around the kiln. The thermal imaging cameras are used to capture the temperature distribution on the surface and inside of the kiln; the infrared sensors are used to collect the temperature on the surface and inside of the kiln; several thermal imaging cameras are used to build a distributed temperature collection network, and the captured temperature data are aggregated to the nodes of the distributed temperature collection network;

[0016] The temperature data aggregated to the nodes are fused, and a temperature distribution diagram is generated based on the fused temperature data. The furnace temperature is compared with the expected range to obtain a comparison result between the furnace temperature and the expected range.

[0017] As a further improvement of the present invention, the process of optimizing the initial ore proportion includes:

[0018] Collect the physical properties of various mineral materials; use the chemical analysis method XRF to determine the element content in the mineral materials, as well as the valence and existence form of each element, and analyze the chemical composition of the mineral materials; use a scanning electron microscope to analyze the mineralogical characteristics of the mineral materials to obtain the type, content and crystal structure information of each mineral in the mineral materials;

[0019] Standardize the collected data on various physical properties, chemical composition and mineralogical characteristics; identify key features that have a significant impact on smelting results, and select and construct feature variables;

[0020] Based on the relationship between characteristic variables and smelting effects, create and optimize the correlation model to reflect the correlation between ore characteristics and smelting effects; use data to train the selected correlation model, verify it under multiple different ore materials and smelting conditions, and use cross-validation to evaluate the performance of the correlation model;

[0021] Determine the optimization objective function, including maximizing smelting output, improving metal recovery rate or reducing energy consumption; optimize the initial ore ratio through repeated iterations; conduct online learning and adjustment of the associated model based on real-time feedback data, and dynamically optimize the initial ore ratio strategy.

[0022] As a further improvement of the present invention, the process of selecting and constructing a feature vector includes:

[0023] After the data standardization process is completed, the characteristics of various mineral materials are visualized using histograms to obtain the preliminary relationship between the characteristics of different mineral materials in terms of particle size, chemical composition and mineral type and the metal recovery rate smelting effect, and identify preliminary key features; color coding is used to show the strength of the correlation between each mineral material characteristic and the smelting effect;

[0024] Combine multiple ore features, find those feature combinations that have a significant impact on the smelting effect through feature importance scoring, and further confirm the preliminary key features; after identifying the preliminary key features, introduce feature interaction term construction to capture possible nonlinear relationships, deeply explore the intrinsic connection between features, and construct the interaction term features of particle size and chemical composition;

[0025] A dynamic threshold is set, and the key features exceeding the dynamic threshold are retained; the screened key features are combined with the extracted interactive features to form a new feature variable set containing a feature vector framework, representing the physical, chemical and mineralogical properties of the ore, and clarifying the source, measurement method, influencing factors and theoretical basis of each selected key feature.

[0026] As a further improvement of the present invention, the process of using data to train the selected association model includes:

[0027] Select linear regression as the basic model from different types of machine learning models such as linear regression, decision tree, random forest and support vector machine, and provide training data for the basic model through different feature subsets; train each basic model independently, use the same training set and validation set, assign different weights to different data points, and evaluate the basic model with the same evaluation criteria;

[0028] The performance of the basic model is converted into a weight using a logical function, and the prediction results of all basic models are weighted averaged. According to the performance evaluation results of all basic models, the prediction value of each basic model is calculated and multiplied by its corresponding weight. All weighted prediction values ​​are summed to obtain the association model.

[0029] Considering the features and smelting effects as nodes, the feature network is constructed to analyze the connection strength between the features and identify the most critical feature combination. The association model is constructed using the collected feature variables, and the initial training of the association model is performed based on the training data. The Bayesian optimization algorithm is used to tune the hyperparameters of the association model.

[0030] As a further improvement of the present invention, the position confirmation process of the thermal imaging camera device and the infrared sensor includes:

[0031] Using hierarchical modeling, the furnace is divided into multiple functional areas, including the feed port, combustion zone, reaction zone, and airflow outlet area. Computer-aided design tools are used to build a high-precision geometric model to capture complex terrain and heat exchange characteristics. The thermal conductivity, specific heat capacity, and density material properties of each area in the furnace are input;

[0032] Design multiple scenarios for simulation according to different fuel types, combustion intensities, cooling methods and furnace operating conditions; use computational fluid dynamics software to conduct coupled analysis of fluid and thermal fields, use high grid accuracy and detailed time step sampling to obtain more accurate heat flux distribution and temperature field data, visualize the heat flux and temperature distribution of simulation results in the form of heat maps and streamlines, intuitively display the temperature and heat flux change trends in each area, and identify key heat sources and temperature gradients;

[0033] Based on the simulation results, cluster analysis is used to identify areas with significant temperature changes, with a focus on the inner wall of the furnace with high temperature and rapid temperature changes, high heat load areas and air flow intersections. Through the distribution characteristics of the heat flow data, a priority thermal measurement monitoring map is constructed to determine the monitoring locations.

[0034] As a further improvement of the present invention, the process of comparing the furnace temperature with the expected range includes:

[0035] When the monitored temperature is lower than the expected range, the low-temperature feedback mechanism is automatically activated; according to the current temperature state of the kiln, the sulfur-rich ore type is automatically selected from the ore warehouse, and the selected sulfur-rich ore is automatically weighed and transported. Different types of sulfur-rich ore are dynamically allocated according to their calorific value and physical properties;

[0036] Combined with low temperature information, the sulfur-oxygen matching program is started to adjust the oxygen supply while calculating the minimum threshold of oxygen intake; after adding sulfur-rich ore, the temperature change of the kiln is continuously monitored, and a feedback control mechanism is used to set the evaluation standard for temperature increase. Once the temperature reaches the expected range, the addition of sulfur-rich ore is stopped and the reaction adjustment is switched to the use of low-sulfur ore;

[0037] During the smelting process, a dynamically adjusted closed-loop system is formed. Through subsequent low-temperature feedback data, the ratio of sulfur-rich ore to sulfur-poor ore is continuously adjusted iteratively to optimize each reaction process.

[0038] As a further improvement of the present invention, the minimum threshold of oxygen intake is calculated, including:

[0039] Input materials include copper concentrate, slag-making agent and oxygen-enriched air:

[0040] ∑G 投入 =g1+g2+g3

[0041] g1, 2, 3: copper concentrate, slag-making agent and oxygen-enriched air, t / h;

[0042] The total amount of ore fed into the furnace and the proportion of each component are expressed as:

[0043]

[0044] Where: η 1，j Indicates the content ratio of component j in the ore fed into the furnace, -; g 1，1、1，2、1，3、1，4、1，5、1，6 Indicates the input amount of chalcocite, covellite, chalcopyrite, bornite, cuprite and tenorite, t / h; η 1，j，1、1，j，2、1，j，3、1，j，4、1，j，5、1，j，6 It represents the content proportion of component j in chalcocite, covellite, chalcopyrite, bornite, cuprite and tenorite, -;

[0045] According to the chemical composition and proportion of the materials in the ore entering the furnace, the proportion of copper, iron, sulfur and oxygen involved in the smelting reaction is calculated:

[0046] g 1，Cu =η 1，Cu2S ·g1(Mr Cu2S / 2Mr Cu )+η 2，CuS ·g2(Mr CuS / Mr Cu )+η 3，CuFeS2 ·g3(Mr CuFeS2 / Mr Cu )+η 4，Cu5FeS4 ·g4(Mr Cu5FeS4 / 5Mr Cu )+η 5，Cu2O ·g5(Mr Cu2O / 2MrCu )+η 6，Cuo ·g6(Mr Cuo / Mr Cu )

[0047] g 1，Fe =η 3，CuFeS2 ·g3(Mr CuFeS2 / Mr Fe )+η 4，Cu5FeS4 ·g4(Mr Cu5FeS4 / Mr Fe )

[0048] g 1，S =η 1，Cu2S ·g1(Mr Cu2S / Mr S )+η 2，CuS ·g2(Mr Cus / Mr S )+η 3，CuFeS2 ·g3(Mr CuFeS2 / 2Mr S )+η 4，Cu5FeS4 ·g4(MrC u5FeS4 / 4Mr S )

[0049] g 1，O =η 5，Cu2O ·g5(Mr Cu2O / Mr o )+η 6，CuO ·g6(Mr CuO / Mr o )

[0050] In the formula, Mr j Indicates the relative atomic mass of element or component j;

[0051] The output materials in the chaotic stirring enhanced copper smelting process are divided into copper slag, copper matte, smoke and flue gas:

[0052] ∑G 产出 =g4+g5+g6+g7

[0053] g4, 5, 6, 7 represent the output of copper slag, copper matte, smoke and flue gas, t / h; let η l，j is the content ratio of element and component j in the product;

[0054] For elemental sulfur, the content of S in smelting output copper slag, copper matte, and smoke is From the sulfur element balance, we know that the sulfur content in the flue gas is: 1000g 1，S t / h, the amount of oxygen used by sulfur element before and after the reaction is:

[0055]

[0056] The mass of oxygen in the input substance is 1000g 1，O kg, according to the oxygen conservation law, the theoretical oxygen demand per hour for smelting is:

[0057]

[0058] As a further improvement of the present invention, the nonlinear chaotic flow calculation program comprises:

[0059] The database of the core indicators of the injection effect as they change with different smelting state parameters and injection parameters is calculated using computational fluid dynamics technology;

[0060] The mathematical model of nonlinear chaotic flow enhanced stirring is constructed based on the database, which can effectively reflect the relationship between smelting state parameters, injection parameters and core indicators;

[0061] Through the established nonlinear chaotic flow enhanced stirring mathematical model, the injection parameters are calculated according to the smelting state parameters monitored in real time during the smelting process.

[0062] To achieve the above object, the present invention also provides the following technical solutions:

[0063] A device for strengthening copper smelting by self-heating and chaotic stirring with sulfur-carbon, which is applied to the method for strengthening copper smelting by self-heating and chaotic stirring with sulfur-carbon, wherein the device for strengthening copper smelting by self-heating and chaotic stirring with sulfur-carbon is composed of a furnace top area, a furnace body area, and a furnace hearth area from top to bottom;

[0064] The furnace top area includes the furnace wall, the material discharge port, the ascending flue, and the secondary air blowing gun. Several secondary air blowing guns are installed on both sides of the lower part of the furnace wall, several material discharge ports are installed on the upper part of the furnace wall, and the ascending flue is installed in the middle of the top of the furnace wall; the material discharge port is used to put in materials, the ascending flue discharges high-temperature flue gas, and the waste heat boiler is connected to the downstream;

[0065] The furnace body area includes side-blowing lances, which are assembled with copper water jackets and arranged on the side walls of the furnace body for spraying oxygen-enriched air;

[0066] The hearth area includes a copper matte discharge outlet, a slag discharge chamber flue, and a slag discharge port. The copper matte discharge outlet is arranged at the front end of the hearth area, and the slag discharge port is arranged at the front end of the hearth area. The slag discharge chamber flue is installed at the upper end of the slag discharge port. The outer shell of the hearth area is welded from steel plates, and its bottom and side walls are laid with high-temperature resistant magnesia bricks, and the side walls are installed with copper water jackets. The copper matte discharge outlet and the slag discharge port are arranged on both sides of the hearth area respectively.

[0067] The initial ore proportioning and real-time temperature monitoring of the present invention are carried out according to the preset smelting requirements to ensure that the chemical composition of the ore can provide the required elements (such as sulfur) to support the reaction in the subsequent smelting process; the non-contact measurement technology is used to monitor the furnace temperature in real time, and the temperature data can be quickly obtained to avoid the delay or error that may be caused by the traditional monitoring method. Significance: Through reasonable ore proportioning, the smelting reaction conditions can be optimized to ensure that the product quality and output meet the expectations; real-time monitoring provides the possibility for subsequent dynamic adjustment and enhances the automation level of the smelting process. Temperature regulation and oxygen intake calculation, according to real-time temperature feedback, can timely adjust the ore addition method: - Add sulfur-rich ore at low temperature to increase the temperature; - Add sulfur-poor ore at high temperature to control and reduce the temperature; After the ore proportioning, the sulfur-oxygen matching program is used to calculate the minimum threshold of oxygen intake to ensure the complete oxidation of sulfur, which can provide the required heat. Significance: Dynamic adjustment based on temperature feedback can effectively reduce the negative impact of temperature fluctuations and ensure that the smelting process is more stable; through precise oxygen matching, the heat release of the oxidation reaction can be optimized and the energy utilization efficiency of smelting can be improved. Chaotic flow calculation and dynamic control, real-time monitoring of furnace temperature, slag thickness, copper matte thickness and the minimum threshold of oxygen intake are input into the nonlinear chaotic flow calculation program to accurately calculate the injection speed, oxygen supply concentration and injection angle to achieve ideal reaction conditions; during the smelting process, by dynamically monitoring the changes in injection speed, oxygen concentration and injection angle, real-time feedback is given to the furnace temperature to achieve precise control. Significance: Through chaotic flow calculation, the best operating parameters can be found in a complex and dynamic environment, greatly improving the efficiency and stability of the smelting process; dynamic feedback control means that the operating parameters can be adjusted immediately during the smelting process, improving the system's adaptive ability and providing a guarantee for ensuring the reliability and safety of the copper smelting process. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 A schematic flow chart of the steps of an embodiment of the method for strengthening copper smelting by self-heating and chaotic stirring of sulfur-substituted carbon according to the present invention;

[0069] Figure 2 It is a schematic diagram of an embodiment of the method for strengthening copper smelting by self-heating and chaotic stirring of sulfur-substituted carbon in the present invention;

[0070] Figure 3 This is a schematic flow chart of the steps of initial ore proportioning and real-time monitoring of an embodiment of the method for strengthening copper smelting by self-heating with sulfur-substituted carbon and chaotic stirring of the present invention;

[0071] Figure 4 A schematic flow chart of the steps of comparing the furnace temperature with the expected range in one embodiment of the method for strengthening copper smelting by self-heating with sulfur-substituted carbon and chaotic stirring of the present invention;

[0072] Figure 5A schematic flow chart of the steps of a nonlinear chaotic flow calculation program of an embodiment of the method for strengthening copper smelting by self-heating and chaotic stirring of sulfur-substituted carbon according to the present invention;

[0073] Figure 6 It is a schematic diagram of the functional modules of the side blowing of one embodiment of the device for strengthening copper smelting by self-heating and chaotic stirring of sulfur-substituted carbon according to the present invention;

[0074] Figure 7 It is a schematic diagram of the functional modules of bottom blowing of an embodiment of the device for strengthening copper smelting by self-heating with sulfur-substituted carbon and chaotic stirring of the present invention;

[0075] Figure 8 It is a schematic diagram of the functional modules of top blowing of an embodiment of the device for strengthening copper smelting by self-heating and chaotic stirring of sulfur-substituted carbon according to the present invention;

[0076] Fig. 9 It is a structural schematic diagram of an embodiment of an electronic device of the present invention;

[0077] Fig.10 It is a schematic structural diagram of an embodiment of the storage medium of the present invention. DETAILED DESCRIPTION

[0078] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0079] The terms "first", "second" and "third" in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined as "first", "second" and "third" can explicitly or implicitly include at least one of the features. In the description of the present invention, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. All directional indications (such as up, down, left, right, front, back...) in the embodiments of the present invention are only used to explain the relative position relationship, movement, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication also changes accordingly. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0080] Reference to an "embodiment" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiment may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0081] like Figure 1 As shown, this embodiment provides an embodiment of a method for strengthening copper smelting by self-heating and chaotic stirring of sulfur-substituted carbon. In this embodiment, the method for strengthening copper smelting by self-heating and chaotic stirring of sulfur-substituted carbon specifically includes the following steps:

[0082] Step S1: Perform initial ore proportioning and use non-contact measurement to monitor the furnace temperature in real time;

[0083] Step S2: adjusting the addition of ore materials according to the real-time monitored kiln temperature; when the kiln temperature is lower than the expected range, adding sulfur-rich ore; when the kiln temperature is higher than the expected range, adding sulfur-poor ore; inputting the added ore proportion into the sulfur-oxygen matching program to calculate the minimum threshold of oxygen intake;

[0084] Step S3: The real-time monitored furnace temperature, slag thickness, copper matte thickness and minimum threshold of oxygen intake are input into the nonlinear chaotic flow calculation program to calculate the injection speed, oxygen supply concentration and injection angle; after the smelting process, the injection speed, oxygen concentration and injection angle are adjusted and fed back to the furnace temperature in real time to achieve dynamic control.

[0085] Preferably, in step S1 of this embodiment, the initial ore proportioning and real-time temperature monitoring are carried out according to the preset smelting requirements to ensure that the chemical composition of the ore can provide the required elements (such as sulfur) to support the reaction in the subsequent smelting process; the non-contact measurement technology is used to monitor the furnace temperature in real time, and the temperature data can be quickly obtained to avoid the delay or error that may be caused by the traditional monitoring method. Significance: Through reasonable ore proportioning, the smelting reaction conditions can be optimized to ensure that the product quality and output meet expectations; real-time monitoring provides the possibility for subsequent dynamic adjustment and enhances the automation level of the smelting process. Step S2 temperature adjustment and oxygen intake calculation, according to real-time temperature feedback, can timely adjust the ore addition method: - Add sulfur-rich ore at low temperature to increase the temperature; - Add sulfur-poor ore at high temperature to control and reduce the temperature; After the ore proportioning, the sulfur-oxygen matching program is used to calculate the minimum threshold of oxygen intake to ensure the complete oxidation of sulfur, which can provide the required heat. Significance: Dynamic regulation based on temperature feedback can effectively reduce the negative impact of temperature fluctuations and ensure a more stable smelting process; through precise oxygen matching, it can optimize the heat release of the oxidation reaction and improve the energy utilization efficiency of smelting. Step S3 chaotic flow calculation and dynamic control, real-time monitoring of furnace temperature, slag thickness, copper matte thickness and the minimum threshold of oxygen intake are input into the nonlinear chaotic flow calculation program to accurately calculate the injection speed, oxygen supply concentration and injection angle to achieve ideal reaction conditions; during the smelting process, by dynamically monitoring the changes in injection speed, oxygen concentration and injection angle, real-time feedback is given to the furnace temperature to achieve precise control. Significance: Through chaotic flow calculation, the best operating parameters can be found in a complex and dynamic environment, greatly improving the efficiency and stability of the smelting process; dynamic feedback control means that the operating parameters can be adjusted in real time during the smelting process, which improves the system's adaptability and provides a guarantee for ensuring the reliability and safety of the copper smelting process (for specific principles, please refer to the attached Figure 2 ).

[0086] In order to achieve material and energy matching during the smelting process, this embodiment requires initial ore blending so that the sulfur content therein basically meets the heat generation requirements. During the smelting process, the furnace temperature is monitored in real time by non-contact measurement. When the furnace temperature is lower than the expected temperature, sulfur-rich ore is added to increase the furnace temperature by oxidation of sulfur; when the furnace temperature is higher than the expected temperature, sulfur-poor ore is added to cool the furnace. Subsequently, the added ore ratio is input into the sulfur-oxygen matching program, and the minimum threshold of oxygen intake is calculated to ensure that the oxygen-rich side blowing can provide an oxidizing atmosphere to meet the needs of timely oxidation and heat release of sulfur. Next, the furnace temperature, slag thickness, copper matte thickness and the minimum threshold of oxygen intake monitored in real time during the smelting process are input into the nonlinear chaotic flow calculation program to obtain the optimal injection speed, oxygen supply concentration and injection angle. After smelting for a period of time, the adjustment of the injection speed, oxygen concentration and injection angle will eventually be fed back to the furnace temperature, and then the next round of control will be carried out.

[0087] In summary, this embodiment forms an efficient and intelligent copper smelting system through three steps: by optimizing the initial ore ratio and real-time monitoring, ensuring the appropriate reaction conditions; then gradually optimizing the smelting process with the help of dynamic adjustment to improve the reaction efficiency; finally, realizing real-time control through chaotic flow calculation to ensure the reliability and safety of the copper smelting process; it helps to improve the energy utilization, production efficiency and product quality of the smelting process, and provides a scientific basis for industrial copper smelting. The implementation of this embodiment depends on the timeliness of the oxidation of sulfur in the material. Only the rapid oxidation and heat release of sulfur can maintain the high temperature of the kiln. Therefore, another key point of the present invention is to improve the heat transfer and mass transfer efficiency through the nonlinear chaos enhanced stirring method to ensure that sulfur and oxygen are fully in contact, react and release heat. Based on this, the problems of high fuel consumption, high smelting cost and high carbon emission pressure in traditional molten pool smelting are solved.

[0088] Furthermore, if Figure 3 As shown, the process of initial ore proportioning and real-time monitoring in step S1 specifically includes the following steps:

[0089] Step S11: Perform multi-dimensional data analysis on the physical, chemical and mineralogical properties of the ore, establish a correlation model between the ore and the expected smelting effect, and optimize the initial ore ratio; at the same time, receive feedback on the furnace temperature, and dynamically optimize the ratio strategy according to the feedback of different furnace problems;

[0090] Step S12: Arrange a number of thermal imaging cameras and infrared sensors around the furnace according to the volume of the furnace, wherein the thermal imaging cameras are used to capture the temperature distribution on the surface and inside of the furnace; the infrared sensors are used to collect the temperature on the surface and inside of the furnace; construct a distributed temperature collection network with the thermal imaging cameras, and aggregate the captured temperature data to the nodes of the distributed temperature collection network;

[0091] Step S13: The temperature data aggregated to the node are subjected to data fusion, and a temperature distribution diagram is generated based on the fused temperature data, and the furnace temperature is compared with the expected range to obtain a comparison result between the furnace temperature and the expected range.

[0092] Preferably, in step S11 of this embodiment, the analysis of mineral material characteristics and dynamic optimization of proportioning can more accurately predict the influence of different mineral material combinations on the smelting effect by comprehensively analyzing the physical, chemical and mineralogical characteristics of the mineral materials and establishing a correlation model; the relationship between the mineral materials and the smelting effect can be quantified by using statistical and machine learning algorithms; the mineral material proportioning can be dynamically adjusted according to the furnace temperature and other feedback data to ensure that the best proportioning scheme can be achieved under different operating conditions (such as temperature fluctuations, changes in mineral material characteristics). Significance: Through scientific and reasonable mineral material proportioning, the efficiency of smelting and the extraction rate of copper metal can be significantly improved, and energy consumption and material waste can be reduced; the dynamic optimization strategy enables the smelting process to adapt to different furnace states, which helps to improve the stability of production and greatly reduce operational risks. Step S12 thermal imaging camera equipment and infrared sensor layout, by arranging multiple thermal imaging camera equipment and infrared sensors around the furnace, comprehensive real-time temperature monitoring of the furnace surface and interior is achieved, and temperature information is obtained from multiple angles; by building a distributed temperature acquisition network, the data of each temperature monitoring point is effectively integrated to ensure the comprehensiveness and accuracy of temperature information. Significance: The distributed monitoring network improves the real-time and reliability of data acquisition, helps to detect temperature anomalies in time, and prevents equipment failure and reduced smelting efficiency caused by excessively high or low temperatures; comprehensive collection of temperature data provides a solid foundation for subsequent data analysis and decision-making, and promotes the intelligent management of the smelting process. Step S13 Data fusion and temperature distribution map generation, fusion analysis of the collected scattered temperature data, can eliminate the measurement errors that may occur in a single sensor, and provide more accurate temperature data; with the help of data processing methods, a temperature distribution map of the kiln is generated, and the temperature distribution is presented in a visual way to improve the intuitiveness and operability of temperature monitoring. Significance: By comparing with the expected range, the deviation of the kiln temperature can be discovered in time and corresponding adjustment measures can be taken to achieve accurate temperature control and ensure that the smelting process is carried out within the optimal temperature range; providing detailed temperature distribution information provides a scientific basis for the decision-making of operators and management, promotes the formulation and adjustment of production plans, and further improves the safety and efficiency of smelting.

[0093] In summary, this embodiment not only realizes the scientific optimization of the initial ore ratio, but also ensures the real-time monitoring and dynamic adjustment of the smelting process by building a comprehensive temperature monitoring system. It will promote the development of intelligent, low-energy and high-efficiency smelting process, and lay a solid foundation for achieving efficient and green copper smelting.

[0094] Furthermore, the process of optimizing the initial mineral material ratio in step S11 specifically includes the following steps:

[0095] Step S111: collect the physical properties of various mineral materials; use the chemical analysis method XRF to determine the element content in the mineral material, as well as the valence and existence form of each element, and perform chemical composition analysis on the mineral material; use a scanning electron microscope to analyze the mineralogical characteristics of the mineral material to obtain information such as the type, content and crystal structure of each mineral in the mineral material; wherein the physical properties include particle size distribution, density and shape, etc., which are obtained by screening analysis, specific gravity measurement and image analysis;

[0096] Step S112: standardize the collected data of various physical properties, chemical composition and mineralogical characteristics; identify key features that have a significant impact on the smelting effect, select and construct feature variables;

[0097] Step S113: creating and optimizing a correlation model based on the relationship between the characteristic variables and the smelting effect, reflecting the correlation between the ore characteristics and the smelting effect; using the data to train the selected correlation model, verifying it under a plurality of different ore materials and smelting conditions, and using cross-validation to evaluate the performance of the correlation model;

[0098] Step S114: Determine the optimization objective function, including maximizing smelting output, improving metal recovery rate or reducing energy consumption, etc.; optimize the initial ore ratio through repeated iterations; conduct online learning and adjustment of the association model based on real-time feedback data, and dynamically optimize the initial ore ratio strategy;

[0099] Among them, the expression of the optimization objective function is:

[0100] Z=w1·Z1+w2·Z2-w3·Z3

[0101]

[0102] Where w1, w2, and w3 are weight coefficients of each objective, ensuring that the objective function can balance the relationship between different objectives; P(i) is the efficiency of extracting metal from ore i, X(i) is the supply of ore; R(j) is the amount of metal extracted from ore j, Y(j) is the content of the ore, and T(k) is the total amount of all ore; E(l) is the consumption of each energy source, and Z(l) is the coefficient or unit price related to the energy source.

[0103] Preferably, in step S111 of this embodiment, the data collection of mineral material characteristics is carried out to conduct a comprehensive and systematic analysis of the physical properties, chemical composition and mineralogical characteristics of the mineral material, and to construct a complete mineral material characteristic database; the XRF technology can be used to quantitatively analyze the existence of various elements in the mineral material, including valence and morphology; the scanning electron microscope provides microstructural information, and the physical properties of particle size distribution, density and shape provide basic data for subsequent smelting calculations. Significance: Provide necessary data support for the establishment and optimization of the correlation model to ensure scientific decision-making in the smelting process; through high-precision analysis methods, ensure the accuracy of mineral material characteristics, and lay the foundation for accurate prediction of smelting effects. Step S112 data standardization and feature engineering, the specific standardization process eliminates the deviation caused by different measurement methods and units, so that various types of data can be compared and analyzed under a unified standard; through data analysis tools (such as principal component analysis, etc.), identify the key features that have a significant impact on the smelting effect, and construct a feature variable library. Significance: Improve the availability and comparability of data to ensure that subsequent analysis can be carried out effectively; the identification of key features helps to further improve the accuracy and relevance of the model, making the optimization process more targeted. Step S113: Creation and optimization of the association model. Using the relationship between characteristic variables and smelting effects, a statistical or machine learning model is created to form a framework that can quantitatively predict smelting results. The model is trained with data from a variety of mineral materials and operating conditions, and the performance of the model is evaluated using cross-validation technology to ensure that it has good generalization capabilities. Significance: It can scientifically reveal the relationship between mineral material characteristics and smelting effects, making the entire smelting process more scientifically based. Through accurate predictions, the technical risks faced in the smelting process are reduced, supporting the scientificity and rationality of production decisions. Step S114: Optimization of the objective function and dynamic optimization strategy. The determination of the optimization objective function gives the entire mineral material ratio optimization process a clear direction, and can be optimized in a targeted manner, such as maximizing smelting output or reducing energy consumption. By dynamically adjusting the association model and combining real-time feedback data, the ratio strategy is continuously optimized to ensure that it can adapt to the ever-changing needs of the smelting process. Significance: Clear optimization goals and constantly adjusted strategies can significantly improve smelting efficiency, enhance product quality and resource utilization, and reduce unnecessary waste of materials and energy; dynamic optimization ensures the system's adaptability under different smelting conditions, provides support for operational flexibility and stability, and promotes the intelligent development of the smelting process.

[0104] In summary, this embodiment, through the refinement and analysis of each step of the initial ore ratio optimization, through scientific ore characteristic analysis, data processing, model construction and dynamic optimization, not only improves the efficiency, reliability and safety of the smelting process, but also promotes the progress of the entire metallurgical industry in terms of intelligence and greening. In future metallurgical production, this series of steps will provide an important foundation and guarantee for achieving low-carbon, efficient and sustainable production goals.

[0105] Furthermore, the process of selecting and constructing the feature vector in step S112 specifically includes the following steps:

[0106] Step S1121: After the data standardization process is completed, the characteristics of various mineral materials are visualized using histograms to obtain the preliminary relationship between different mineral material characteristics such as particle size, chemical composition and mineral type and smelting effects such as metal recovery rate, and identify preliminary key features; color coding is used to display the correlation strength between various mineral material characteristics and smelting effects;

[0107] Step S1122: Combine multiple ore features, find those feature combinations that have a significant impact on the smelting effect through feature importance scoring, and further confirm the preliminary key features; after identifying the preliminary key features, introduce feature interaction term construction to capture possible nonlinear relationships and deeply explore the intrinsic connections between features, such as constructing interaction term features of particle size and chemical composition;

[0108] Step S1123: Set a dynamic threshold, and the key features exceeding the dynamic threshold are retained; combine the screened key features with the extracted interactive features to form a new feature variable set containing a feature vector framework, representing the physical, chemical and mineralogical properties of the ore, and clarifying the source, measurement method, influencing factors and theoretical basis of each selected key feature.

[0109] Preferably, in step S1121 of this embodiment, data visualization and preliminary key feature identification are performed to visualize the mineral material characteristics through histograms, and to intuitively display the relationship between different mineral material characteristics (such as particle size, chemical composition, mineral type) and smelting effects (such as metal recovery rate); color coding is used to display the strength of the correlation between the characteristics and the smelting effect, providing a quantitative basis for subsequent analysis. Significance: It helps to quickly identify potential key features, guide subsequent data processing, and improve the efficiency of feature selection; through visualization technology, it enhances the understanding of data distribution and relationships, lays the foundation for subsequent in-depth analysis, and helps the decision-making process to be more efficient and accurate. Step S1122 Feature combination and nonlinear relationship mining, combines and evaluates features through a feature importance scoring system, identifies feature combinations that have a significant impact on smelting effects; constructs interactive features, captures nonlinear relationships between multiple features, deeply explores the intrinsic connections between features, and expands the richness of feature expression. Significance: By combining and constructing interactive features, the model's expressiveness of complex relationships is improved, the representativeness and effectiveness of the selected features are ensured, and the predictive ability of geological and mineral features on smelting effects is enhanced; in the combination of different features, the information of the data is utilized to the maximum extent, the accuracy of the subsequent model is improved, and a guarantee is provided for reducing model errors. Step S1123 Dynamic threshold setting and feature variable set construction, by setting a dynamic threshold mechanism, effectively screen out unimportant features, and ensure that the retained features have a significant impact on the smelting effect; integrate the selected key features and interactive features to construct a feature variable set containing multiple characteristics of the ore, providing a perfect foundation for downstream model training. Significance: The setting of dynamic thresholds not only improves the flexibility of feature selection, but also makes each element in the feature set undergo rigorous testing to ensure its effectiveness in subsequent analysis, thereby improving the performance of the model; by clarifying the source, measurement method, influencing factors and theoretical basis of each feature, it can provide traceability and interpretability for feature selection, and enhance the reliability and transparency of the model.

[0110] In summary, the above three steps of this embodiment complement each other in the process of feature selection and construction, and jointly promote the efficiency and accuracy of smelting process data analysis. The foundation is laid through data visualization and preliminary feature identification, feature combination and nonlinear relationship mining deepen the understanding, and a complete feature expression is formed through dynamic threshold and feature variable set construction. It not only improves the predictive ability and scientificity of the model, but also provides a solid data foundation for the subsequent optimization of smelting effects, making the improvement of smelting process more scientific and precise, and ultimately achieving efficient utilization of resources and sustainable development.

[0111] Furthermore, the process of using data to train the selected association model in step S113 specifically includes the following steps:

[0112] Step S1131: Select linear regression as the basic model from different types of machine learning models such as linear regression, decision tree, random forest and support vector machine, and provide training data for the basic model through different feature subsets; train each basic model independently, use the same training set and validation set, assign different weights to different data points, and evaluate the basic model with the same evaluation criteria;

[0113] Step S1132: Use a logical function to convert the performance of the basic model into a weight, perform weighted average on the prediction results of all basic models, calculate the prediction value of each basic model multiplied by its corresponding weight according to the performance evaluation results of all basic models, sum up all weighted prediction values, and obtain an associated model;

[0114] Step S1133: Consider the features and smelting effects as nodes, build a feature network, analyze the connection strength between features, and identify the most critical feature combination; use the collected feature variables to build an association model, perform preliminary training of the association model based on the training data, and use the Bayesian optimization algorithm to tune the hyperparameters of the association model.

[0115] Preferably, in step S1131 of this embodiment, the selection and independent training of the basic model, by selecting linear regression as the basic model, using its simplicity and interpretability, establishes a preliminary understanding of the linear relationship between features and smelting effects; by providing training data for the basic model through different feature subsets, it can capture the performance of each feature under different conditions, avoid overfitting and improve the generalization ability of the model; assign different weights to different data points to ensure that the model can focus on important data points (such as samples appearing under specific smelting conditions) and enhance the sensitivity of the model to the importance of samples. Significance: Provide a solid foundation for subsequent model training, by selecting appropriate models and weighting strategies, the learning of the model is both scientific and flexible; by ensuring the diversity and pertinence of the model, it can efficiently capture the relationship between smelting effects and mineral characteristics, and provide a reliable baseline for subsequent evaluation. Step S1132 weighted average and model integration, convert the performance of the basic model into weights, and ensure that the model with better performance in the integration process contributes more to the final prediction results; by weighted averaging the prediction results of all basic models to form a fusion effect, it can effectively reduce the prediction error caused by the deviation of a single model. Significance: To achieve effective integration of multiple models, take advantage of ensemble learning, and use the prediction results of different models to achieve higher accuracy and robustness; through the application of logical functions, the weight of the model can be adjusted in time to reflect the performance of the model under different conditions, ensuring the optimal performance of the final model. Step S1133: Feature network construction and hyperparameter tuning. By constructing a feature network to view the connection strength between each feature, it helps to identify the most critical features, optimize feature selection, and then build a more accurate model; preliminary training combined with the collected feature variables can provide sufficient background knowledge for the model, so that it can better reflect the relationship between ore characteristics and smelting effects; using the Bayesian optimization algorithm to tune hyperparameters, it can efficiently find the optimal solution in the high-dimensional parameter space and reduce the error of the model. Significance: Through the construction of the feature network, we can deeply understand the relationship between features, provide support for targeted feature selection in the process of ore optimization, and ensure the scientific nature of subsequent analysis; through hyperparameter tuning, we can ensure the optimal performance of the model, so that the associated model can more effectively cope with different smelting conditions in application, improve metal recovery rate or reduce energy consumption.

[0116] In summary, through the above steps, the entire training process of this embodiment not only provides an interpretable basic model, but also improves the accuracy of the final prediction by integrating the advantages of each model. In addition, the implementation of each step provides a landing basis for subsequent dynamic optimization, real-time feedback and online self-learning, making the optimization of ore ratio more intelligent and efficient, and ultimately improving the economic benefits and environmental friendliness of the smelting process.

[0117] Furthermore, the position confirmation process of the thermal imaging camera device and the infrared sensor in step S12 specifically includes the following steps:

[0118] Step S121: using hierarchical modeling, the furnace is divided into multiple functional areas such as the feed port, combustion area, reaction area, and airflow outlet area, and a high-precision geometric model is constructed using computer-aided design tools to capture complex terrain and heat exchange characteristics; inputting material properties such as thermal conductivity, specific heat capacity, and density of each area in the furnace;

[0119] Step S122: Design multiple scenarios for simulation according to different furnace operating conditions such as fuel type, combustion intensity, cooling method, etc.; use computational fluid dynamics software to perform coupled analysis of fluid and thermal field, use high grid accuracy and detailed time step sampling to obtain more accurate heat flux distribution and temperature field data, visualize the heat flux and temperature distribution of the simulation results in the form of heat maps and streamlines, intuitively display the temperature and heat flux change trends of each region, and identify key heat sources and temperature gradients;

[0120] Step S123: Based on the simulation results, cluster analysis is used to identify areas with significant temperature changes, with a focus on the inner wall of the furnace with high temperature and rapid temperature changes, high heat load areas and air flow intersections. Based on the distribution characteristics of the heat flow data, a priority thermal measurement monitoring map is constructed to determine the monitoring locations.

[0121] Preferably, in step S121 of this embodiment, hierarchical modeling and material input, by dividing the kiln into multiple functional areas (such as feed port, combustion zone, reaction zone, airflow outlet, etc.), the complex terrain and heat exchange characteristics inside the kiln are accurately captured, so that the subsequent heat flow and temperature simulation can be more accurate; the material properties such as thermal conductivity, specific heat capacity and density of each area in the kiln are input to ensure that the heat transfer characteristics of each area are accurately reflected, providing a real physical basis for the implementation of the CFD model. The significance achieved: The completion makes the modeling of the kiln more in line with the actual situation, and can reflect the thermal physical behavior during actual operation in the calculation model; the thermal environment of the kiln is determined, ensuring that the subsequent fluid dynamics and thermal field analysis have reliable basic data. Step S122 Multi-scenario simulation and visualization analysis, using CFD software to perform coupled simulation of fluid and thermal field, can reflect the interaction between different airflows and temperature fields inside the furnace, and provide more comprehensive data for analysis; by improving the resolution and time resolution of the calculation model, more accurate heat flow and temperature distribution data are obtained, reflecting more microscopic thermal dynamic changes; presenting simulation results in the form of heat maps and streamlines, making complex heat flow and temperature field information more intuitive and easy to understand. Significance achieved: Intuitive visualization helps to quickly identify key heat sources and temperature gradients under different working conditions, and provide a scientific basis for subsequent sensor layout; through data visualization analysis, it can help decision makers better understand the thermal dynamics of the furnace, and provide support for equipment monitoring and fault identification. Step S123 Cluster analysis and monitoring map construction, using cluster analysis, identify areas with significant temperature changes, and determine high temperature areas and locations with sharp temperature changes (such as the inner wall of the furnace, high heat load areas and airflow intersections) as key monitoring areas; through the distribution characteristics of heat flow data, construct priority thermal measurement monitoring maps to intuitively display the monitoring importance and priority of each area. Significance achieved: Clarify the priority monitoring locations to make the monitoring plan more efficient, avoid waste of resources, and improve the monitoring efficiency of the equipment; focus on monitoring areas with significant temperature changes to capture potential failure risks in a timely manner and improve the safety of furnace operation. At the same time, through continuous data collection and analysis, it helps to optimize overall production performance.

[0122] In summary, the three steps of this embodiment together constitute a complete set of thermal imaging camera equipment and infrared sensor position confirmation process, ensuring that through scientific simulation and analysis methods, combined with high-precision input data, data-driven monitoring solutions and efficient operation management are ultimately achieved. This not only improves the coverage and accuracy of monitoring, but also helps to improve the safety and operating efficiency of the kiln, thereby improving overall work efficiency.

[0123] Furthermore, if Figure 4 As shown, the process of comparing the kiln temperature with the expected range in step S2 specifically includes the following steps:

[0124] Step S21: When the monitored temperature is lower than the set expected range, the low temperature feedback mechanism is automatically activated; according to the current temperature state of the kiln, the sulfur-rich ore type is automatically selected from the ore warehouse, the selected sulfur-rich ore is automatically weighed and transported, and different types of sulfur-rich ore are dynamically allocated according to their calorific value and physical properties;

[0125] Step S22: Combined with the low temperature information, the sulfur-oxygen matching program is started, and the oxygen supply is adjusted while calculating the minimum threshold of the oxygen intake; after the sulfur-rich ore is added, the change of the furnace temperature is continuously monitored, and a feedback control mechanism is adopted to set the evaluation standard of the temperature increase. Once the temperature reaches the expected range, the addition of the sulfur-rich ore is stopped, and the reaction adjustment of the use of the low-sulfur ore is switched;

[0126] Step S23: During the smelting process, a dynamically adjusted closed-loop system is formed. Through subsequent low-temperature feedback data, the ratio of sulfur-rich ore and sulfur-poor ore is continuously adjusted iteratively to optimize each reaction process.

[0127] Preferably, in step S21 of this embodiment, the low-temperature feedback mechanism is activated and the dynamic allocation of sulfur-rich ore is carried out. The monitoring system monitors the temperature of the kiln in real time, so that when the temperature is lower than the set range (preset), the feedback mechanism is automatically started to ensure rapid response, and different smelting requirements are met by automatically selecting the type of sulfur-rich ore; according to the calorific value and physical properties of the ore, dynamic allocation is implemented to achieve the best smelting effect. This allocation can enhance the reaction activity in the furnace and accelerate the temperature rise. The significance achieved: by adding sulfur-rich ore in time, the self-heating reaction can be effectively utilized to increase the temperature of the kiln and reduce the reduction of smelting efficiency caused by low temperature; through accurate ore ratio and dynamic allocation, unnecessary resource waste in the smelting process is reduced, and the overall economy is improved. Step S22 sulfur-oxygen matching program and temperature monitoring, through the sulfur-oxygen matching program, the minimum threshold of the oxygen intake is accurately calculated, and the oxygen supply is dynamically adjusted according to the current state of the kiln to ensure sufficient oxygen in the reaction process; after the sulfur-rich ore is added, the temperature change of the kiln is continuously monitored, and the evaluation standard of the temperature increase is set. Once the temperature returns to the expected range, the operation process is adjusted in time. Significance achieved: Through precise oxygen control, non-ideal reactions caused by insufficient oxygen supply are avoided to ensure the quality and safety of smelting; real-time monitoring and feedback mechanisms realize refined temperature management, making the entire smelting process more flexible and controllable in temperature control. Step S23 Dynamic adjustment and closed-loop system formation, build a closed-loop control system based on real-time monitoring data, and quickly adjust the ratio of sulfur-rich ore and sulfur-poor ore through continuous analysis of low-temperature feedback data to achieve adaptive optimization of the smelting process; during the iterative adjustment process, the smelting operation can be continuously improved based on historical data and feedback from new states, so that the efficiency of each reaction can be improved. Significance achieved: With the formation of a closed-loop system, the copper smelting process has the ability to learn and improve, and can be continuously optimized in actual operation, and achieve optimal reaction conditions through dynamic adaptation; by optimizing the proportion of ore and reaction conditions, it helps to reduce energy consumption and emissions, and promote the copper smelting process to be more environmentally friendly and sustainable.

[0128] In summary, this embodiment has unique technical effects and far-reaching significance in the process of comparing the furnace temperature with the expected range. Through real-time monitoring, dynamic allocation, precise control and closed-loop optimization, not only the smelting efficiency is improved, but also the safety and stability of the reaction are ensured. The intelligent operation mode provides strong technical support for the modernization and sustainable development of the metallurgical industry.

[0129] Furthermore, according to the law of conservation of elements, the process of calculating the minimum threshold of oxygen intake in step S2 shows that the input and output of oxygen flow remain unchanged in each link of copper smelting. Therefore, by calculating the difference in oxygen content in the incoming ore and the output material (copper slag, copper matte, smoke and flue gas), the minimum threshold of theoretical smelting oxygen demand can be analyzed, which specifically includes:

[0130] The input materials involved in the entire chaotic stirring enhanced copper smelting process can be mainly divided into copper concentrate, slag-making agent (it is worth noting that the slag-making process does not require the participation of oxygen) and oxygen-enriched air:

[0131] ∑G 投入 =g1+g2+g3

[0132] g1, 2, 3: copper concentrate, slag-making agent and oxygen-enriched air, t / h;

[0133] For chaotic stirring enhanced copper smelting, oxygen will initially exist in the furnace charge in the form of mainly oxidized copper ore. Specifically, the furnace charge can be mainly divided into two categories: copper sulfide ore and oxidized copper ore. Among them, the copper sulfide ore mainly includes Cu2S (chalcocite), CuS (covellite), CuFeS2 (chalcopyrite) and Cu5FeS4 (bornite), and the oxidized copper ore mainly includes Cu2O (copperite) and CuO (copper ore). Therefore, the total charge amount of the furnace charge and the proportion of each component can be expressed as:

[0134]

[0135] Where: η 1，j Indicates the content ratio of component j in the ore fed into the furnace, -; g 1，1、1，2、1，3、1，4、1，5、1，6 Indicates the input amount of chalcocite, covellite, chalcopyrite, bornite, cuprite and tenorite, t / h; η 1，j，1、1 , j，2、1，j，3、1，j，4、1，j，5、1，j，6 It represents the content proportion of component j in chalcocite, covellite, chalcopyrite, bornite, cuprite and tenorite, -;

[0136] According to the chemical composition and proportion of the materials in the ore, the proportion of copper, iron, sulfur and oxygen, the main elements involved in the smelting reaction, can be calculated:

[0137] g 1，Cu =η 1，Cu2S ·g1(Mr Cu2S / 2Mr Cu )+η 2，CuS ·g2(Mr CuS / Mr Cu )+η 3，CuFeS2 ·g3(Mr CuFeS2 / Mr Cu )+η 4，Cu5FeS4 ·g4(Mr Cu5FeS4 / 5Mr Cu )+η 5，Cu2O ·g5(Mr Cu2O / 2Mr Cu )+η 6，CuO ·g6(Mr CuO / Mr Cu )

[0138] g 1，Fe =η 3，CuFeS2 ·g3(Mr CuFeS2 / Mr Fe )+η 4，Cu5FeS4 ·g4(Mr Cu5FeS4 / Mr Fe )

[0139] g 1，S =η 1，Cu2S ·g1(Mr Cu2S / Mr S )+η 2，CuS ·g2(Mr CuS / Mr S )+η 3，CuFeS2 ·g3(Mr CuFeS2 / 2Mr S )+η 4，Cu5FeS4 ·g4(Mr Cu5FeS4 / 4Mr S )

[0140] g 1，O =η 5，Cu2O ·g5(Mr Cu2O / Mr o )+η 6，CuO ·g6(Mrc uO / Mr o )

[0141] In the formula, Mr j Indicates the relative atomic mass of element or component j;

[0142] The output materials involved in the chaotic stirring enhanced copper smelting process can be mainly divided into copper slag, copper matte, smoke and flue gas:

[0143] ∑G 产出 =g4+g5+g6+g7

[0144] g4, 5, 6, 7 represent the output of copper slag, copper matte, smoke and flue gas, t / h; let η l，j is the content ratio of element and component j in the product;

[0145] For chaotic stirring enhanced copper smelting, oxygen will finally exist in the flue gas, copper slag and copper matte mainly in the form of SO2 and FeO combined with sulfur and iron respectively;

[0146] For elemental sulfur, the content of S in smelting output copper slag, copper matte, and smoke is From the sulfur element balance, we know that the sulfur content in the flue gas is: 1000g 1，St / h. Since most of the sulfur in the flue gas exists in the form of SO2, the amount of oxygen used by sulfur element before and after the reaction can be obtained as follows:

[0147]

[0148] For the oxygen consumption of the Fe element in chaotic stirring copper smelting, it is assumed that the Fe element in the smelting reaction output is mainly in the form of FeO. The iron element is conserved before and after the smelting reaction, and the mass is 1000g 1，Fe kg, it can be known that the oxygen content in the smelting product FeO element accounts for 1000g 1，Fe Mr. O / Mr Fe kg;

[0149] The mass of oxygen in the input substance is 1000g 1，O kg. According to the conservation of oxygen, the theoretical oxygen demand per hour for smelting is:

[0150]

[0151] Preferably, the theoretical oxygen demand of this embodiment is accurately calculated, and the theoretical oxygen demand in the copper smelting process can be calculated by accurately analyzing the oxygen element in the input and output substances; the calculation is based on the law of conservation of elements, which ensures the mass balance of all chemical components participating in the reaction before and after the reaction; it not only provides the theoretically required oxygen amount, but also takes into account the possible loss and conversion in the actual reaction. Improve the efficiency of the smelting process. By understanding the specific amount of oxygen required for the redox reaction of each ore, the supply of oxygen-enriched air can be more accurately allocated, thereby ensuring the continuous and efficient reaction in the furnace. Predictive analysis can greatly reduce the reaction delay and efficiency reduction in the smelting process caused by insufficient oxygen, and ultimately improve production efficiency. Optimize resource consumption. During the smelting process, the theoretical oxygen demand is calculated, so that the operator can flexibly adjust the oxygen input according to actual needs, avoiding the waste of resources caused by excessive or insufficient oxygen, helping to reduce operating costs, and reducing environmental impact to a certain extent. The realization of refined control process, through the analysis of the content of each element in the smelting reaction, enables the fine control of oxygen supply in the production process of copper smelting; through the automation system, based on the real-time monitoring data feedback, the required amount of oxygen is dynamically adjusted to ensure the stability and safety of the furnace operation. Improve product quality, through the calculation of theoretical oxygen demand, the conditions of smelting reaction can be further optimized, the unqualified products caused by secondary reactions can be reduced, and the quality of the final copper product can be improved; accurate oxygen supply not only ensures the complete chemical reaction, but also reduces the content of harmful substances (such as unreacted sulfides or other impurities) in the final product. Data-driven process optimization realizes the quantitative evaluation of the smelting process and forms data-driven process optimization, which provides a theoretical basis and reference basis for adjustments and improvements in the production process, so that the entire smelting process can be continuously optimized with the help of analytical data.

[0152] In summary, this embodiment not only enhances the understanding of oxygen demand in theory, but also achieves the goal of making the smelting process more efficient, safer and more environmentally friendly in practice. By rationally allocating resources, improving production efficiency and product quality, and optimizing the operation process, a scientific, reasonable and efficient copper smelting process is finally formed.

[0153] Furthermore, if Figure 5 As shown, the process of the nonlinear chaotic flow calculation program in step S3 specifically includes the following steps:

[0154] Step S31: obtaining a database of core indicators of injection (side blowing, bottom blowing and top blowing) changing with different smelting state parameters and injection parameters by using computational fluid dynamics technology;

[0155] Step S32: constructing a mathematical model of nonlinear chaotic flow enhanced stirring according to the database, which can effectively reflect the relationship between smelting state parameters, injection parameters and core indicators;

[0156] Step S33: The injection parameters are calculated based on the established nonlinear chaotic flow enhanced stirring mathematical model and the smelting state parameters monitored in real time during the smelting process.

[0157] Preferably, in step S31 of this embodiment, computational fluid dynamics technology and database construction, through computational fluid dynamics (CFD) technology, the influence of injection (side blowing, bottom blowing and top blowing) on ​​the flow characteristics of the melt can be deeply analyzed, and the core indicators under different smelting state parameters and injection parameters can be obtained. The core indicators may include the temperature, velocity distribution, mixing uniformity, etc. of the melt; a database containing data under various smelting states and injection conditions is established to provide a basis for subsequent modeling and analysis. The significance achieved: the data provides the necessary experimental and simulation basis for the subsequent model construction, ensuring the accuracy and applicability of the model; by analyzing the core indicators under different conditions, the key factors affecting the smelting efficiency and product quality can be identified, thereby providing a scientific basis for optimizing the smelting process. Step S32 is the construction of a mathematical model for nonlinear chaotic flow enhanced stirring. A mathematical model is constructed based on the database, so that it can reflect the relationship between smelting state parameters, injection parameters and core indicators from the perspective of nonlinear and chaotic flow, and can reveal complex flow mechanisms and reaction processes; through this model, a quantitative description can be provided for complex physical phenomena, which is convenient for in-depth analysis and understanding of fluid behavior under different operating conditions. Significance achieved: A mathematical model that can reflect the flow mechanism provides a more accurate tool to replace empirical formulas and simple linear models to optimize operating conditions and improve the overall efficiency of the smelting process; it not only deepens the understanding of chaotic flow processes in theory, but can also be directly applied to actual production, providing theoretical support for noise and uncertainty. Step S33 real-time monitoring and injection parameter calculation, by real-time monitoring of smelting state parameters, and using nonlinear chaotic flow models to calculate injection parameters, the entire process can be dynamically adapted, and the changes in the state of the reaction furnace can be timely reacted to, and the injection parameters can be automatically adjusted to adapt to new smelting conditions; it can provide more accurate injection parameters and provide effective guidance information for operators to adjust the production process. Significance achieved: By dynamically adjusting the injection parameters, the uniformity and reaction rate of melt stirring can be effectively improved, energy consumption can be reduced, and the metal recovery rate can be increased, and the operating efficiency of the entire smelting process can be improved; the real-time monitoring and adjustment method reduces the production risks that may be caused by improper operation, and ensures the safety and stability of the overall production line; it provides technical support for the implementation of intelligent management of smelting processes, making the production process more intelligent and automated, and improving the scientificity and flexibility of operations.

[0158] In summary, each step of the nonlinear chaotic flow calculation program in this embodiment has important technical effects and far-reaching significance in the copper smelting process. It not only enhances the understanding and control of the smelting process, but also promotes the optimization and improvement of the smelting process, as well as the feasibility of realizing intelligent management, bringing higher efficiency and flexibility to modern metallurgical production.

[0159] The technical problem solved by this embodiment is that the oxygen-enriched side-blowing stirring molten pool is a typical nonlinear chaotic process. The core indicators for measuring the side-blowing effect (material non-uniformity, temperature non-uniformity, speed non-uniformity, stirring dead zone) and smelting state parameters (slag layer thickness, copper matte thickness, furnace temperature) and injection parameters (injection speed, oxygen-enriched concentration, injection angle) show a significant nonlinear relationship, and the influence of multiple parameters cannot be evaluated by conventional mathematical modeling methods.

[0160] In this embodiment, CFD database is constructed, and computational fluid dynamics is a discipline that solves the fluid mechanics control equations by mathematical calculation methods to obtain discrete quantitative descriptions of flow fields and phase interfaces, and uses this to predict the fluid motion behavior in the calculation area. In the present invention, in addition to solving conventional mass, momentum, energy conservation, turbulence, and component equations, the VOF method is also required to capture and reconstruct the gas-liquid interface;

[0161] The VOF model is suitable for simulating a variety of immiscible fluids, including stratified flow and free surface flow. The VOF model assumes that there is no interpenetration between multiphase fluids. For each additional phase in the model, it is necessary to introduce an additional phase volume fraction. In each control volume, the sum of the volume fractions of all phases is 1. The phase interface between the multiphases is tracked by solving the continuity equation of the multiphase volume fraction. For the qth phase, this equation has the following form:

[0162]

[0163] In the formula, α q is the volume fraction of the qth phase; ρ q is the density of the qth phase; is the velocity of the fluid; S αq is the source phase;

[0164] In the VOF model, the velocity field is obtained by solving a single momentum equation for the entire region. The velocity field is shared by all phases as a result of the calculation. The momentum equation depends on ρ and μ based on the volume fractions of all phases in the control volume:

[0165]

[0166] Where ρ is the density of the fluid; is the velocity of the fluid; μ is the viscosity of the fluid; is the volume force;

[0167] In the VOF model, the energy equation is expressed as follows:

[0168]

[0169] Where Eq is calculated by the specific heat capacity of the qth phase and the shared temperature T; k eff is effective heat conduction; source term S h Includes radiation and other volumetric heat sources;

[0170] Turbulence model equation, the k-ε model was proposed by Launder and Spalding. The model introduces two unknown quantities: turbulent kinetic energy k and turbulent dissipation rate ε. The transport equations corresponding to the two unknown quantities are:

[0171]

[0172] The calculation process sets the boundary conditions and physical parameters of each phase of the simulation according to the operating parameters of the oxygen-enriched side-blowing furnace. The specific steps of solving the problem through numerical calculation are as follows:

[0173] (1) Taking the oxygen-enriched side-blown furnace as the research object, a proportional three-dimensional computational domain model was established according to the actual size;

[0174] (2) The furnace model is meshed to obtain a hexahedral structured mesh for capturing the computational fluid domain. The area near the nozzle and the melt region requires special encryption to accurately capture the free liquid surface morphology and velocity.

[0175] (3) The gas-liquid interface is set as a free interface, the solid wall is regarded as a no-slip boundary, and the boundary layer near the wall is treated with a standard wall function. The inlet and outlet boundary conditions and initial conditions are set according to the actual industrial situation.

[0176] (4) Setting the VOF model, the surface tension between the mixed gas and liquid phases and the ke turbulence model;

[0177] (5) Extract and analyze material non-uniformity, velocity non-uniformity, temperature non-uniformity and stirring dead zone;

[0178] By repeating the above steps, a database of different slag layer thicknesses, copper matte thicknesses, furnace temperatures, injection velocities, oxygen enrichment levels and injection angles can be calculated.

[0179] The construction of nonlinear chaotic mathematical model includes input parameters and measurement indicators, where the input parameters include the smelting state parameters fed back in real time by the smelting monitoring system and the spray parameters of the spray gun, as follows:

[0180] Smelting state parameters: slag layer thickness h s , copper matte thickness hc , furnace temperature T;

[0181] Injection parameters: injection speed v, oxygen enrichment level x, injection angle d;

[0182] Metric: Material heterogeneity U m , speed unevenness U v , Temperature non-uniformity U T , stirring dead zone a;

[0183] The following explains how the metrics are calculated:

[0184] Stirring dead zone: the volume of the area where the melt velocity is less than 0.1m / s;

[0185] Material inhomogeneity: The inhomogeneity of a substance A is measured by the gradient A' of its concentration in a certain direction. Its expressions in the three directions of x, y and z are:

[0186]

[0187] By integrating and summing the heterogeneity components of material A in three directions in the melt region, the total heterogeneity of material A can be obtained:

[0188] U m =∫ V (A′ x +A′ y +A′ z )dV

[0189] Temperature non-uniformity: The non-uniformity of temperature T is measured by T', and its expressions in the three directions of xyz are:

[0190]

[0191] By integrating and summing the inhomogeneity components of temperature T in three directions in the melt region, the total inhomogeneity of temperature T can be obtained:

[0192] U T =∫ V (T′ x +T′ y +T′ z )dV

[0193] Velocity non-uniformity: The melt velocity V is a vector, containing three components, Vx, Vy, and Vz. Its non-uniformity in the xyz direction can be expressed as:

[0194]

[0195] The total unevenness of velocity v is expressed as:

[0196] U v =∫ V (V′ x +V′ y +V′ z )dV

[0197] After determining the above input and output parameters, a set of coupled differential equations are established to describe the dynamic behavior of the system:

[0198] 1. Dynamic changes of injection speed v:

[0199] 2. Dynamic changes of oxygen enrichment x:

[0200] 3. Dynamic changes of spray gun spacing d:

[0201] 4. Dynamic changes of the spray angle θ:

[0202] 5. Slag layer thickness h s Dynamic changes:

[0203] 6. Copper matte thickness h c Dynamic changes:

[0204] 7. Dynamic changes of furnace temperature T:

[0205]

[0206] U m =f m (v,x,d,θ,h s ,h c ,T)

[0207] U v =f v (v,x,d,θ,h s ,h c ,T)

[0208] U T =f T (v,x,d,θ,h s ,h c ,T)

[0209] a=f a (v,x,d,θ,h s ,h c ,T)

[0210] In these equations, r v ,r x,r d ,r θ ,r hs ,r hc ,r T is the growth rate parameter of each parameter itself, and α1, α2, ..., η6 are constants that affect the interaction between the parameters. m ,f v ,f T ,f a is a function of the complex relationship between the metric and the parameters. The current model can be used to study the interaction between different parameters and their impact on the system behavior and the metric. By adjusting the parameters and initial conditions, the dynamic behavior of the system can be observed, including possible periodicity or chaos.

[0211] like Figure 6 As shown, this embodiment also provides an embodiment of a device for strengthening copper smelting by self-heating and chaotic stirring of sulfur-substituted carbon. In this embodiment, the device for strengthening copper smelting by self-heating and chaotic stirring of sulfur-substituted carbon is applied to the method for strengthening copper smelting by self-heating and chaotic stirring of sulfur-substituted carbon in the above embodiment. The device for strengthening copper smelting by self-heating and chaotic stirring of sulfur-substituted carbon includes a furnace top area 1, a furnace body area 2, a furnace cylinder area 3, a furnace wall 4, a discharge port 5, an ascending flue 6, a secondary air blowing gun 7, a copper matte discharge port 8, a side blowing gun 9, a slag discharge chamber flue 10, and a slag discharge port 11;

[0212] The device is composed of furnace top area 1, furnace body area 2, and furnace hearth area 3 from top to bottom;

[0213] The furnace top area 1 includes a furnace wall 4, a material discharge port 5, an ascending flue 6, and a secondary air blowing gun 7. A plurality of secondary air blowing guns 7 are installed on both sides below the furnace wall 4, a plurality of material discharge ports 5 are installed above the furnace wall 4, and an ascending flue 6 is installed in the middle of the top of the furnace wall 4; the material discharge port 5 is used to put in materials, the ascending flue 6 discharges high-temperature flue gas, and a waste heat boiler is connected downstream;

[0214] The furnace body area 2 includes a side-blowing lance 9, which is composed of a copper water jacket. An oxygen-enriched side-blowing lance (side-blowing lance 9) is arranged on the side wall of the furnace body to spray oxygen-enriched air to form an oxidizing atmosphere, and also to stir the melt, enhance heat and mass transfer, and accelerate chemical reactions.

[0215] The furnace area 3 includes a copper matte discharge port 8, a slag discharge chamber flue 10, and a slag discharge port 11. The copper matte discharge port 8 is arranged at the front end of the furnace area 3, and the slag discharge port 11 is arranged at the front end of the furnace area 3. The slag discharge chamber flue 10 is installed at the upper end of the slag discharge port 11. The shell of the furnace area 3 is welded by steel plates, and its bottom and side walls are built with high-temperature resistant magnesia bricks, and the side walls are installed with copper water jackets to prevent high-temperature corrosion damage. The copper matte discharge port 8 and the slag discharge port 11 are arranged on both sides of the furnace area 3.

[0216] Preferably, the working principle of this embodiment is to introduce thiocarbon (or similar reducing agent) to carry out autothermal reaction at high temperature, so that the copper ions in the copper ore are reduced to generate metallic copper; in this process, the device maintains a uniform high-temperature environment, and promotes the full mixing of the melt through chaotic stirring to ensure the uniformity and efficiency of the reaction. The top area 1 is used for material delivery and high-temperature flue gas discharge; the furnace body area 2 introduces oxygen-enriched air through a side-blowing lance to assist combustion and enhance the stirring of the melt; the furnace cylinder area 3 processes molten copper and slag, quickly discharges the generated copper and waste slag, and maintains the stability of the furnace state.

[0217] Technical effects and significance of each structure The feeding port in the furnace top area 1 is used to put in raw materials to ensure the quantitative and stable supply of raw materials; the ascending flue is responsible for the discharge of high-temperature flue gas, effectively removes waste gas and reduces the pressure in the furnace; the secondary air blowing gun introduces additional air to increase the oxygen concentration in the furnace and enhance the oxidizing property and temperature of the reaction. Significance: It ensures the supply of materials and the stability of the furnace environment, creating good conditions for subsequent reactions. The furnace body area 2 contains a side-blowing spray gun, which is composed of a copper water jacket. The side-blowing spray gun achieves the mixing of the melt, enhances heat and mass transfer, and increases the reaction rate by spraying oxygen-enriched air; forming an oxidizing atmosphere, which helps to effectively remove impurities and optimize copper extraction; Significance: Through chaotic stirring, the metal recovery rate and reaction efficiency are improved, making the smelting process more coordinated and stable, while reducing energy consumption. The furnace area 3 includes the copper matte discharge port, the slag discharge chamber flue, and the slag discharge port. The furnace shell is made of high-temperature resistant materials. The design of the copper matte discharge port and the slag discharge port ensures the rapid discharge of molten copper and slag, avoiding the impact of excessive impurities or solid matter on subsequent operations; the use of high-temperature resistant materials ensures the safety and durability of the furnace and extends the service life of the equipment. Significance: Effectively handle molten products, improve the overall operating efficiency of copper smelting, reduce the risk of environmental pollution, and provide a reliable foundation for subsequent smelting processes.

[0218] In summary, this embodiment achieves efficient copper smelting by combining gas dynamics and fluid mechanics, with sulfide carbon self-heating and chaotic flow stirring as the core. This not only improves smelting efficiency and ensures a high copper recovery rate, but also reduces energy consumption and environmental impact. By optimizing the design and function of each structure, the device can play an important role in modern smelting and provide a solution for sustainable development and intelligent manufacturing.

[0219] The oxygen-enriched side-blowing furnace type of this embodiment is the basis for satisfying the self-heating of sulfide ore oxidation, because only when the heat release rate of sulfur element oxidation is greater than the heat supply of material melting and the heat dissipation of flue gas and water jacket can the furnace temperature be maintained and the smooth smelting be ensured. The oxygen-enriched side-blowing furnace has a natural advantage in this regard. Its internal slag layer is deep, and the side-blowing blast can fully stir the melt, and form a gas-liquid-solid three-phase coexistence zone with the oxygen-enriched injection air intake, greatly increasing the contact area of ​​slag, material and gas phase, and realizing the rapid heating-melting-contact-reaction-heat release process of sulfide ore.

[0220] This embodiment can adjust the feed ratio of sulfur-rich ore and sulfur-poor ore according to the real-time feedback of the furnace temperature during the smelting process. Based on the oxygen-enriched side-blowing furnace type and nonlinear enhanced stirring technology, the heat transfer, mass transfer and chemical reaction rate in the furnace melt are greatly improved, so that the sulfur element is rapidly oxidized and releases heat, and finally replaces carbon with sulfur, that is, the furnace temperature can be maintained without adding additional fuel to meet the smelting conditions. This method can effectively solve the problems of high fuel consumption, high smelting cost and high carbon emission pressure in traditional molten pool smelting, and help enterprises achieve energy conservation, emission reduction, cost reduction and efficiency improvement.

[0221] Furthermore, if Figure 7 As shown, the device for strengthening copper smelting by self-heating and chaotic stirring with sulfur-carbon provided in this embodiment can be replaced by another structure. Figure 6 The side blowing is replaced by bottom blowing, which is specifically described as follows: it includes a flue port 12, an auxiliary burner port 13, a slag discharge port 14, a detection port 15, a first feeding port 16, a main burner port 17, a metal discharge port 18, and a first spray gun 19;

[0222] The flue opening 12 is connected to the ascending flue 6, and an auxiliary burner opening 13 is installed on the right side of the flue opening 12, and a slag discharge opening 14 is installed at the lower end of the auxiliary burner opening 13, and a detection opening 15 and a first feeding opening 16 are installed on the left side of the flue opening 12 in turn, and a main burner opening 17 is installed on the opposite side of the auxiliary burner opening 13, and a metal discharge opening 18 and a plurality of first spray guns 19 are installed at the bottom of the flue opening 12.

[0223] Preferably, this embodiment realizes top blowing, and fuel is input through the main burner port 17 and the auxiliary burner port 13 for combustion, so as to heat the reaction zone and promote the reaction of thiocarbon and copper ore; the heat generated by combustion makes the reactants reach the required high temperature in the furnace, promoting chemical reaction. The copper in the ore is reduced to a metallic state. At the same time, the ascending flue 6 is responsible for guiding the flue gas generated in the furnace out of the furnace to avoid the accumulation of harmful gases. The first spray gun 19 in the device is used to spray gas or liquid into the molten metal, so that chaotic stirring can be achieved, the molten metal is continuously stirred, the smelting reactants are uniformly mixed, and the reaction efficiency is improved; the chaotic stirring technology can enhance the metal extraction rate, reduce the smelting time, and improve the production efficiency; the first feeding port 16 is configured to feed copper ore and other raw materials into the device, while the slag discharge port 14 and the metal liquid discharge port 18 are used to discharge the slag generated during the smelting process and obtain the metal liquid; the detection port 15 is used to monitor the temperature, gas composition and other parameters in the furnace, which is convenient for adjusting the reaction conditions and ensuring the safety and efficiency of the smelting process.

[0224] This embodiment improves the copper extraction rate through the comprehensive utilization of self-heating and chaotic stirring, so that the copper in the raw material is more fully reduced and the waste of resources is reduced; the combustion of thiocarbon triggers a self-heating reaction, which reduces the external energy consumption, and the efficient heat exchange and airflow design maximize the thermal energy utilization rate; the flue and flue port mentioned in the device design can effectively discharge harmful gases and protect the working environment and the surrounding ecology; through the monitoring detection port 15, the situation in the furnace can be understood in real time, and the operation can be adjusted in time to avoid safety hazards. This technology realizes the efficient utilization of ore and fuel in the copper smelting process and reduces the dependence on natural resources; the combination of self-heating and chaotic stirring technology provides a new idea for improving the copper smelting process and promotes scientific research and technological progress in the field of metallurgy; by improving smelting efficiency, reducing waste emissions, and optimizing resource utilization, it meets the requirements of sustainable development of modern industry and creates conditions for realizing environmentally friendly metallurgy; through technological innovation, it improves production efficiency and reduces operating costs, thereby improving the overall economic benefits of the metallurgical industry.

[0225] This embodiment combines the technology of thiocarbon self-heating with chaotic stirring, which not only improves the copper smelting efficiency and metal recovery rate, but also has important environmental protection and resource utilization benefits; it has important practical significance for improving the overall level and economic benefits of the metal smelting industry.

[0226] Furthermore, if Figure 8 As shown, the device for strengthening copper smelting by self-heating and chaotic stirring with sulfur-carbon provided in this embodiment can be replaced by another structure. Figure 6 The side blowing is replaced by top blowing, which is specifically described as follows: comprising a smoke outlet 20, a second feeding port 21, a slag protection layer 22, a second lance 23, a melt outlet 24, and a refractory lining 25;

[0227] The outer side of the slag protection layer 22 is a refractory lining 25 , a second spray gun 23 is arranged inside the slag protection layer 22 , a second feeding port 21 is opened in the refractory lining 25 , and a melt outlet 24 is arranged on the left side of the refractory lining 25 .

[0228] Preferably, the second spray gun 23 of this embodiment is located inside the slag protection layer 22, and can spray gas or other substances into the melt to promote chaotic stirring. This stirring enhances the uniformity of the melt, allows the reactants to be fully mixed, and improves the reaction efficiency and metal extraction rate. The role of the slag protection layer section 22 in the furnace is to protect the refractory lining 25 from being corroded by the flux and the melt, extend the service life of the lining, and reduce heat loss; the design of the refractory lining 25 ensures the stability and safety of the furnace body at high temperatures, while providing thermal conductivity to ensure efficient heat conduction; the second feed port 21 is used to feed auxiliary materials or new reactants into the furnace so as to replenish raw materials in time during the smelting process; the melt outlet 24 is used to discharge the molten metal after the smelting is completed, which is convenient for subsequent casting or further processing; the smoke outlet 20 guides the exhaust gas generated in the furnace out of the furnace, which helps to protect the environment, prevent harmful gases from accumulating in the furnace, and reduce the impact on operators.

[0229] This embodiment combines the chaotic stirring and self-heating effects of the spray gun to achieve a more complete reaction, significantly improving the copper recovery rate and smelting speed; the design of the refractory lining and slag protection layer ensures the stability of the furnace body during long-term high-temperature operation and reduces the risk of corrosion caused by high-temperature materials; the self-heating reaction reduces the need for external heating and optimizes energy utilization, while the flue gas outlet improves environmental safety and reduces pollution to the external environment; additional feeding ports, spray guns and other designs provide technical flexibility, allowing operators to adjust raw materials and operating conditions during the smelting process according to actual needs; by improving smelting efficiency and metal recovery rate, waste is reduced, the recycling of copper resources is promoted, and a contribution is made to sustainable development; the design and application of this device represents the progress of modern metallurgical technology, integrating self-heating, chaotic stirring and advanced materials science, and provides a reference for other metal smelting processes; efficient high-temperature processes and material utilization reduce production costs, thereby improving the economic benefits of the metallurgical industry and enhancing market competitiveness.

[0230] In summary, this embodiment effectively improves the efficiency and safety of copper smelting through advanced technologies such as sulfur-substituted carbon self-heating and chaotic stirring, and promotes the modernization of smelting technology. Its design is not only of practical value, but also of great practical significance in terms of environmental protection and sustainable utilization of resources.

[0231] like Fig. 9As shown, this embodiment provides an embodiment of an electronic device. In this embodiment, the electronic device includes a processor and a memory coupled to the processor.

[0232] The memory stores program instructions for implementing the method of strengthening copper smelting by self-heating and chaotic stirring of sulfur-substituted carbon according to any of the above embodiments.

[0233] The processor is used to execute the program instructions stored in the memory to perform copper smelting by self-heating and chaotic stirring with sulfur-substituted carbon.

[0234] The processor may also be referred to as a CPU (Central Processing Unit). The processor may be an integrated circuit chip with signal processing capabilities. The processor may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0235] Further, Fig.10 This is a schematic diagram of the structure of a storage medium of an embodiment of the present application. The storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0236] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0237] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of software functional units. The above is only an implementation mode of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly used in other related technical fields, is also included in the patent protection scope of the present invention.

[0238] The above specific implementations of the invention are only examples, and the present invention is not limited to the above specific implementations. For those skilled in the art, any equivalent modification or substitution of the invention is also within the scope of the present invention. Therefore, equal transformations, modifications, improvements, etc. made without departing from the spirit and principle of the present invention should be included in the scope of the present invention.

Claims

1. A method for strengthening copper smelting by self-heating and chaotic stirring of sulfur-substituted carbon, characterized in that: The method of strengthening copper smelting by self-heating and chaotic stirring with sulfur-carbon comprises: Carry out initial ore mix and use non-contact measurement to monitor the kiln temperature in real time; Adjust the addition of ore materials according to the real-time monitored kiln temperature. When the kiln temperature is lower than the expected range, add sulfur-rich ore; when the kiln temperature is higher than the expected range, add sulfur-poor ore; input the added ore proportion into the sulfur-oxygen matching program to calculate the minimum threshold of oxygen intake; The real-time monitored furnace temperature, slag thickness, copper matte thickness and the minimum threshold of oxygen intake are input into the nonlinear chaotic flow calculation program to calculate the injection speed, oxygen supply concentration and injection angle. After the smelting process, the injection speed, oxygen concentration and injection angle are adjusted and fed back to the furnace temperature in real time to achieve dynamic control.

2. The method for strengthening copper smelting by self-heating and chaotic stirring of sulfur-substituted carbon according to claim 1, characterized in that: The process of initial ore mix and real-time monitoring includes: Conduct multi-dimensional data analysis on the physical, chemical and mineralogical properties of the ore, establish a correlation model between the ore and the expected smelting effect, and optimize the initial ore ratio; at the same time, receive feedback on the furnace temperature, and dynamically optimize the ratio strategy based on the feedback of different furnace problems; According to the volume of the kiln, several thermal imaging cameras and infrared sensors are arranged around the kiln. The thermal imaging cameras are used to capture the temperature distribution on the surface and inside of the kiln; the infrared sensors are used to collect the temperature on the surface and inside of the kiln; several thermal imaging cameras are used to build a distributed temperature collection network, and the captured temperature data are aggregated to the nodes of the distributed temperature collection network; The temperature data aggregated to the nodes are fused, and a temperature distribution diagram is generated based on the fused temperature data. The furnace temperature is compared with the expected range to obtain a comparison result between the furnace temperature and the expected range.

3. The method for strengthening copper smelting by self-heating and chaotic stirring of sulfur-substituted carbon according to claim 2, characterized in that: The process of optimizing the initial ore mix includes: Collect the physical properties of various mineral materials; use the chemical analysis method XRF to determine the element content in the mineral materials, as well as the valence and existence form of each element, and analyze the chemical composition of the mineral materials; use a scanning electron microscope to analyze the mineralogical characteristics of the mineral materials to obtain the type, content and crystal structure information of each mineral in the mineral materials; Standardize the collected data on various physical properties, chemical composition and mineralogical characteristics; identify key features that have a significant impact on smelting results, and select and construct characteristic variables; Based on the relationship between characteristic variables and smelting effects, create and optimize the correlation model to reflect the correlation between ore characteristics and smelting effects; use data to train the selected correlation model, verify it under multiple different ore materials and smelting conditions, and use cross-validation to evaluate the performance of the correlation model; Determine the optimization objective function, including maximizing smelting output, improving metal recovery rate or reducing energy consumption; optimize the initial ore ratio through repeated iterations; conduct online learning and adjustment of the associated model based on real-time feedback data, and dynamically optimize the initial ore ratio strategy.

4. The method for strengthening copper smelting by self-heating and chaotic stirring of sulfur-substituted carbon according to claim 3, characterized in that: The process of selecting and constructing feature vectors includes: After the data standardization process is completed, the characteristics of various mineral materials are visualized using histograms to obtain the preliminary relationship between the characteristics of different mineral materials in terms of particle size, chemical composition and mineral type and the metal recovery rate smelting effect, and identify preliminary key features; color coding is used to show the strength of the correlation between each mineral material characteristic and the smelting effect; Combine multiple ore features, find those feature combinations that have a significant impact on the smelting effect through feature importance scoring, and further confirm the preliminary key features; after identifying the preliminary key features, introduce feature interaction term construction to capture possible nonlinear relationships, deeply explore the intrinsic connection between features, and construct the interaction term features of particle size and chemical composition; A dynamic threshold is set, and the key features exceeding the dynamic threshold are retained; the screened key features are combined with the extracted interactive features to form a new feature variable set containing a feature vector framework, representing the physical, chemical and mineralogical properties of the ore, and clarifying the source, measurement method, influencing factors and theoretical basis of each selected key feature.

5. The method for strengthening copper smelting by self-heating and chaotic stirring of sulfur-substituted carbon according to claim 3, characterized in that: The process of training the selected association model using data includes: Select linear regression as the basic model from different types of machine learning models such as linear regression, decision tree, random forest and support vector machine, and provide training data for the basic model through different feature subsets; train each basic model independently, use the same training set and validation set, assign different weights to different data points, and evaluate the basic model with the same evaluation criteria; The performance of the basic model is converted into a weight using a logical function, and the prediction results of all basic models are weighted averaged. According to the performance evaluation results of all basic models, the prediction value of each basic model is calculated and multiplied by its corresponding weight. All weighted prediction values ​​are summed to obtain the association model. Considering the features and smelting effects as nodes, the feature network is constructed to analyze the connection strength between the features and identify the most critical feature combination. The association model is constructed using the collected feature variables, and the initial training of the association model is performed based on the training data. The Bayesian optimization algorithm is used to tune the hyperparameters of the association model.

6. The method for strengthening copper smelting by self-heating and chaotic stirring of sulfur-substituted carbon according to claim 2, characterized in that: The process of confirming the location of thermal imaging cameras and infrared sensors includes: Using hierarchical modeling, the furnace is divided into multiple functional areas, including the feed port, combustion zone, reaction zone, and airflow outlet area. Computer-aided design tools are used to build a high-precision geometric model to capture complex terrain and heat exchange characteristics. The thermal conductivity, specific heat capacity, and density material properties of each area in the furnace are input; Design multiple scenarios for simulation according to different fuel types, combustion intensities, cooling methods and furnace operating conditions; use computational fluid dynamics software to conduct coupled analysis of fluid and thermal fields, use high grid accuracy and detailed time step sampling to obtain more accurate heat flux distribution and temperature field data, visualize the heat flux and temperature distribution of simulation results in the form of heat maps and streamlines, intuitively display the temperature and heat flux change trends in each area, and identify key heat sources and temperature gradients; Based on the simulation results, cluster analysis is used to identify areas with significant temperature changes, with a focus on the inner wall of the furnace with high temperature and rapid temperature changes, high heat load areas and air flow intersections. Through the distribution characteristics of the heat flow data, a priority thermal measurement monitoring map is constructed to determine the monitoring locations.

7. The method for strengthening copper smelting by self-heating and chaotic stirring of sulfur-substituted carbon according to claim 1, characterized in that: The process of comparing the furnace temperature to the expected range includes: When the monitored temperature is lower than the expected range, the low-temperature feedback mechanism is automatically activated; according to the current temperature state of the kiln, the sulfur-rich ore type is automatically selected from the ore warehouse, and the selected sulfur-rich ore is automatically weighed and transported. Different types of sulfur-rich ore are dynamically allocated according to their calorific value and physical properties; Combined with low temperature information, the sulfur-oxygen matching program is started to adjust the oxygen supply while calculating the minimum threshold of oxygen intake; after adding sulfur-rich ore, the temperature change of the kiln is continuously monitored, and a feedback control mechanism is used to set the evaluation standard for temperature increase. Once the temperature reaches the expected range, the addition of sulfur-rich ore is stopped and the reaction adjustment is switched to the use of low-sulfur ore; During the smelting process, a dynamically adjusted closed-loop system is formed. Through subsequent low-temperature feedback data, the ratio of sulfur-rich ore to sulfur-poor ore is continuously adjusted iteratively to optimize each reaction process.

8. The method for strengthening copper smelting by self-heating and chaotic stirring of sulfur-substituted carbon according to claim 1, characterized in that: Calculate the minimum threshold for oxygen intake, including: Input materials include copper concentrate, slag-making agent and oxygen-enriched air: ∑G 投入 =g1 + g2 + g3 g1, 2, 3: copper concentrate, slag-making agent and oxygen-enriched air, t / h; The total amount of ore fed into the furnace and the proportion of each component are expressed as: Where: η 1，j Indicates the content ratio of component j in the ore fed into the furnace, -; g 1，1、1，2、1，3、1，4、1，5、1，6 Indicates the input amount of chalcocite, covellite, chalcopyrite, bornite, cuprite and tenorite, t / h; η 1，j，1、1，j,2、1，j,3、1，j，4、1，j,5、1，j，6 It represents the content proportion of component j in chalcocite, covellite, chalcopyrite, bornite, cuprite and tenorite, -; According to the chemical composition and proportion of the materials in the ore entering the furnace, the proportion of copper, iron, sulfur and oxygen involved in the smelting reaction is calculated: g 1，Cu =η 1，Cu2S ·g1(Mr Cu2S / 2Mr Cu )+η 2,CuS ·g2(Mr CuS / Mr Cu )+η 3,CuFeS2 ·g3(Mr CuFeS2 / Mr Cu )+η 4,Cu5FeS4 ·g4(Mr Cu5FeS4 / 5Mr Cu )+η 5,Cu2O ·g5(Mr Cu2O / 2Mr Cu )+η 6,CuO ·g6(Mr CuO / Mr Cu ) g 1，Fe =η 3，CuFeS2 ·g3(Mr CuFeS2 / Mr Fe )+η 4，Cu5FeS4 ·g4(Mr Cu5FeS4 / Mr Fe ) g 1，S =η 1，Cu2S ·g1(Mr Cu2S / Mr S )+η 2，CuS ·g2(Mr CuS / Mr S )+η 3，CuFeS2 ·g3(Mr CuFeS2 / 2Mr S )+η 4，Cu5FeS4 ·g4(Mr Cu5FeS4 / 4Mr S ) g 1，O =η 5，Cu2O ·g5(Mr Cu2O / Mr O )+η 6，CuO ·g6(Mr CuO / Mr O ) In the formula, Mr j Indicates the relative atomic mass of element or component j; The output materials in the chaotic stirring enhanced copper smelting process are divided into copper slag, copper matte, smoke and flue gas: ∑G 产出 =g4+g5+g6+g7 g 4、5、6、7 The fractions represent the output of copper slag, copper matte, smoke dust and flue gas, t / h; let η l，j is the content ratio of element and component j in product l; For elemental sulfur, the content of S in smelting output copper slag, copper matte, and smoke is From the sulfur element balance, we know that the sulfur content in the flue gas is: 1000g1, S t / h, and the amount of oxygen used by the sulfur element before and after the reaction is: The mass of oxygen in the input substance is 1000g1, O kg, according to the oxygen conservation law, the theoretical oxygen demand per hour for smelting is:

9. The method for strengthening copper smelting by self-heating and chaotic stirring of sulfur-substituted carbon according to claim 1, characterized in that: Nonlinear chaotic flow calculation program, including: The database of the core indicators of the injection effect as they change with different smelting state parameters and injection parameters is calculated using computational fluid dynamics technology; The mathematical model of nonlinear chaotic flow enhanced stirring is constructed based on the database, which can effectively reflect the relationship between smelting state parameters, injection parameters and core indicators; Through the established nonlinear chaotic flow enhanced stirring mathematical model, the injection parameters are calculated according to the smelting state parameters monitored in real time during the smelting process.

10. A device for strengthening copper smelting by self-heating and chaotic stirring of sulfur-substituted carbon, which is applied to the method for strengthening copper smelting by self-heating and chaotic stirring of sulfur-substituted carbon as claimed in any one of claims 1 to 9, characterized in that: The device for strengthening copper smelting by self-heating with sulfur-carbon and chaotic stirring comprises a furnace top area, a furnace body area, and a furnace hearth area from top to bottom; The furnace top area includes the furnace wall, the material discharge port, the ascending flue, and the secondary air blowing gun. Several secondary air blowing guns are installed on both sides of the lower part of the furnace wall, several material discharge ports are installed on the upper part of the furnace wall, and the ascending flue is installed in the middle of the top of the furnace wall; the material discharge port is used to put in materials, the ascending flue discharges high-temperature flue gas, and the waste heat boiler is connected to the downstream; The furnace body area includes side-blowing lances, which are assembled with copper water jackets and arranged on the side walls of the furnace body for spraying oxygen-enriched air; The hearth area includes a copper matte discharge outlet, a slag discharge chamber flue, and a slag discharge port. The copper matte discharge outlet is arranged at the front end of the hearth area, and the slag discharge port is arranged at the front end of the hearth area. The slag discharge chamber flue is installed at the upper end of the slag discharge port. The outer shell of the hearth area is welded from steel plates, and its bottom and side walls are laid with high-temperature resistant magnesia bricks, and the side walls are installed with copper water jackets. The copper matte discharge outlet and the slag discharge port are arranged on both sides of the hearth area respectively.

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