A method and device for strengthening copper smelting by carbon sulfide self-heating and chaotic stirring

Abstract

The present invention relates to the technical field of copper metallurgy processes, and discloses a method and device for strengthening copper smelting with thiocarbon self-heating and chaotic stirring. The method includes using non-contact measurement technology to monitor the furnace temperature in real time; adjusting the addition of ore materials according to the furnace temperature monitored in real time, inputting the proportion of the added ore materials into a sulfur-oxygen matching program, and calculating the lowest threshold of the oxygen input amount; inputting the furnace temperature, slag thickness, matte thickness, and the lowest threshold of the oxygen input amount monitored in real time into a non-linear chaotic flow calculation program, and calculating the injection speed, oxygen supply concentration, and injection angle; after the smelting process is carried out, adjusting the injection speed, oxygen concentration, and injection angle, and feeding them back to the furnace temperature in real time. The device includes a furnace top area, a furnace body area, a hearth area, a furnace wall, a charging opening, a rising flue, a secondary air blowing lance, a matte discharge opening, a side blowing lance, a slag discharge chamber flue, and a slag discharge opening. The present invention helps to improve the energy utilization rate, production efficiency, and product quality of the smelting process.

Description

Technical Field

[0001] The present invention relates to the technical field of copper metallurgy processes, and in particular, to a method and device for strengthening copper smelting with carbon disulfide autothermal and chaotic stirring. Background Art

[0002] Copper, as an important metal material, is widely used in many fields, such as electronics and electrical engineering, architectural decoration, industrial equipment manufacturing, etc. Since 2020, China's annual refined copper output has exceeded 10 million tons, of which more than 80% is obtained by pyrometallurgical methods. The pyrometallurgical process of copper includes bath smelting, copper matte converting, fire refining, and electrolytic refining. Among them, bath smelting is used to obtain copper matte and is an important intermediate link. In the traditional bath smelting process, although the oxidation heat release of sulfur elements can supplement part of the heat, it is still necessary to supplement fuels such as pulverized coal and methane to maintain the high temperature state of the furnace. And during the smelting process, the lack of accurate evaluation and calculation of the oxidation heat release of sulfur elements leads to the inability to accurately control the feeding amount of fuels such as pulverized coal and methane, resulting in energy waste, increased carbon emissions, and increased smelting costs for enterprises. At the same time, traditional bath smelting also has problems such as insufficient stirring, low heat and mass transfer efficiency, and insufficient oxygen-sulfur reaction, resulting in the oxidation heat of sulfur elements not being fully utilized.

[0003] Prior Art One, a 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 sulfuric acid production unit. The flue gas discharged from the oxygen-enriched smelting furnace, copper matte converting converter, and fire-refining anode furnace is recovered. The recovered flue gas is separated by the flue gas separation unit into carbon dioxide flue gas, high-concentration sulfur dioxide flue gas, and low-concentration sulfur dioxide flue gas. 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 also reacts with the low-concentration sulfur dioxide separated from the flue gas to prepare sulfur in the flue gas sulfur recovery unit, and the high-concentration sulfur dioxide obtained from the recovered flue gas is used to prepare sulfuric acid in the flue gas sulfuric acid production system. Although low-carbon smelting of copper concentrate is achieved, there is a lack of professional stirring technical means, resulting in insufficient stirring, low heat and mass transfer efficiency.

[0004] Prior Art II, a Chinese patent with application number 202310843138.7, discloses a copper smelting method using a bottom-blown continuous smelting furnace, which includes: continuously flowing hot copper matte into the bottom-blown continuous smelting furnace, while adding slag-making flux, and conducting continuous smelting to form a slag layer and a copper layer. It also includes: controlling the slag type of the bottom-blown continuous smelting to be iron-calcium slag, and controlling the CaO / Fe by mass in the iron-calcium slag to be 0.33 - 0.37, and controlling the mass content of magnetite in the iron-calcium slag to be 40% - 55%. Although the obtained slag has a low copper content index, reaching below 10.5%; and the furnace lining has good slag adhesion, which can extend the service life of the original fully hot-state bottom-blown continuous smelting furnace by more than 50%. However, due to the single process means, the oxygen-sulfur reaction is not sufficient, resulting in a low oxidation heat utilization efficiency.

[0005] Prior Art III, a Chinese patent with application number 202310971730.5, discloses a high-oxygen-enriched non-linear intensified carbon-free copper smelting method, which includes the following steps: by ore blending, adjusting the sulfur-copper ratio of copper concentrate to 1.1 - 1.8, and then by premixing with flux, adjusting the mass ratio of Fe to SiO2 to 1.0 - 2.0 to obtain a premixed material; subjecting the premixed material to non-linear intensified smelting under oxygen-enriched air conditions to obtain copper matte, slag, and flue gas; during the non-linear intensified smelting process, no carbonaceous fuel is added. Although reasonable batching is carried out according to the differences in the components of various copper concentrates, and the sulfur-copper ratio in the mixed copper concentrate is limited, it can cause a violent oxidation reaction between sulfides and high-oxygen-enriched air during smelting, releasing high-energy heat, thus avoiding the use of carbonaceous fuel and carbon dioxide emissions during the smelting process. However, the lack of an efficient stirring technical means during raw material mixing and reaction leads to uneven raw material mixing, reducing the degree of chemical reaction to a certain extent and increasing energy consumption.

[0006] Currently, Prior Art I, Prior Art II, and Prior Art III have problems such as insufficient stirring, low heat and mass transfer efficiency, and insufficient oxygen-sulfur reaction, resulting in the oxidation heat of sulfur elements not being fully utilized. Therefore, the present invention provides a method and device for strengthening copper smelting with thiocarbon self-heating and chaotic stirring. By increasing the sulfur content of the material, no additional fuel is added, and only the oxidation heat release of sulfur is used to maintain the high-temperature state of the furnace, meeting the smelting requirements. Summary of the Invention

[0007] The main object of the present invention is to provide a method and device for strengthening copper smelting with thiocarbon self-heating and chaotic stirring to solve the problems in the prior art such as insufficient stirring, low heat and mass transfer efficiency, and insufficient oxygen-sulfur reaction, resulting in the oxidation heat of sulfur elements not being fully utilized.

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

[0009] A method for strengthening copper smelting with carbon disulfide self-heating and chaotic stirring. The method for strengthening copper smelting with carbon disulfide self-heating and chaotic stirring includes:

[0010] Conduct an initial ore mixture ratio, and use non-contact measurement to monitor the furnace temperature in real time;

[0011] Adjust the addition of ore according to the real-time monitored furnace temperature. When the furnace temperature is lower than the expected range, add sulfur-rich ore; when the furnace temperature is higher than the expected range, add sulfur-poor ore; input the added ore mixture ratio into the sulfur-oxygen matching program to calculate the lowest threshold of the oxygen input amount;

[0012] Input the real-time monitored furnace temperature, slag thickness, matte thickness, and the lowest threshold of oxygen input amount into the non-linear chaotic flow calculation program to calculate the injection speed, oxygen supply concentration, and injection angle; after the smelting process starts, adjust according to the injection speed, oxygen concentration, and injection angle, and feedback to the furnace temperature in real time to achieve dynamic control.

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

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

[0015] Arrange a number of thermal imaging cameras and infrared sensors around the furnace according to the volume of the furnace. 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 acquisition network with a number of thermal imaging cameras, and summarize the captured temperature data to the nodes of the distributed temperature acquisition network;

[0016] Fuse the temperature data summarized to the nodes, generate a temperature distribution map according to the fused temperature data, compare the furnace temperature with the expected range, and obtain the 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 mixture ratio includes:

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

[0019] Standardize the data of various physical properties, chemical compositions, and mineralogical characteristics collected; identify the key characteristics that have a significant impact on the smelting effect, select and construct characteristic variables;

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

[0021] Determine the optimization objective function, including maximizing the smelting output, improving the metal recovery rate, or reducing the energy consumption; optimize the initial ore material ratio through repeated iteration; according to the real-time feedback data, perform online learning and adjustment on the correlation model, and dynamically optimize the initial ore material ratio strategy.

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

[0023] After the data standardization process, use histograms to visualize the characteristics of various ore materials, obtain the preliminary relationship between the characteristics of different ore materials such as particle size, chemical composition, and mineral type and the smelting effect of the metal recovery rate, and identify the preliminary key characteristics; use color coding to display the correlation strength between each ore material characteristic and the smelting effect;

[0024] Combine multiple ore material characteristics, find the characteristic combinations that jointly have a significant impact on the smelting effect through characteristic importance scoring, and further confirm the preliminary key characteristics; after identifying the preliminary key characteristics, introduce the construction of characteristic interaction terms to capture possible non-linear relationships, deeply explore the internal connections between the characteristics, and construct the interaction term characteristics of particle size and chemical composition;

[0025] Set a dynamic threshold, and the key characteristics exceeding this dynamic threshold are retained; combine the selected key characteristics with the extracted interaction characteristics to form a new set of characteristic variables including the characteristic vector framework, representing the physical, chemical, and mineral characteristics of the ore material, and clarify the source, measurement method, influencing factors, and theoretical basis of each selected key characteristic.

[0026] As a further improvement of the present invention, the process of using data to train the selected correlation 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; independently train each basic model, 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 base models is converted into weights using logical functions, and the prediction results of all base models are weighted and averaged. According to the performance evaluation results of all base models, the predicted value of each base model is multiplied by its corresponding weight, and all weighted predicted values are summed to obtain the associated model.

[0029] Regarding features and smelting effects as nodes, by constructing a feature network, analyzing the connection strength between features, and identifying the most critical feature combinations from them; using the collected feature variables to construct an associated model, conducting preliminary training of the associated model in combination with training data, and using the Bayesian optimization algorithm for hyperparameter tuning of the associated model.

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

[0031] Using hierarchical modeling, the furnace is divided into multiple functional areas such as the feed inlet, combustion zone, reaction zone, and gas flow outlet area. A high-precision geometric model is constructed using computer-aided design tools to capture complex terrain and heat exchange characteristics; the thermal conductivity, specific heat capacity, and density material properties of each area inside the furnace are input.

[0032] For different furnace operating conditions such as fuel types, combustion intensities, and cooling methods, multiple scenarios are designed for simulation; computational fluid dynamics software is used for coupled analysis of fluid and thermal fields. By using high grid accuracy and detailed time step sampling, more accurate heat flux distribution and temperature field data are obtained. The heat flux and temperature distribution of the simulation results are visually analyzed in the form of heat maps and streamlines to intuitively display the temperature and heat flux change trends in each area and identify key heat sources and temperature gradients.

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

[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 set expected range, the low-temperature feedback mechanism is automatically activated; based on the current temperature state of the furnace, the sulfur-rich ore material variety is automatically selected from the ore material warehouse, and the selected sulfur-rich ore material is automatically weighed and transported. Different varieties of sulfur-rich ore materials are dynamically allocated according to their calorific values and physical properties.

[0036] Combine with the low-temperature information to start the sulfur-oxygen matching program. While calculating the lowest threshold of the oxygen input amount, adjust the oxygen supply amount. After adding the sulfur-rich ore, continuously monitor the change of the furnace temperature, adopt a feedback control mechanism, set the evaluation criteria for the temperature increase, and once the temperature reaches the expected range, stop adding the sulfur-rich ore and switch to using the low-sulfur ore for reaction adjustment;

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

[0038] As a further improvement of the present invention, calculating the lowest threshold of the oxygen input amount includes:

[0039] The input substances are divided into copper concentrate, slag-forming agent, and oxygen-enriched air:

[0040] ∑G 投入 = g1 + g2 + g3

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

[0042] The total feeding amount of the furnace charge and the proportion of each component are expressed as:

[0043]

[0044] In the formula: η 1,j Represents the proportion of the content of component j in the furnace charge, -; g 1,1、1,2、1,3、1,4、1,5、1,6 Represents the feeding 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 Represents the proportion of the content of component j in chalcocite, covellite, chalcopyrite, bornite, cuprite, and tenorite, -;

[0045] According to the chemical composition and component proportion of the substances in the furnace charge, calculate the proportion of the contents of the elements copper, iron, sulfur, and oxygen participating in the smelting reaction:

[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 Cu6eS2 / 2Mr S ) + η 4,Cu5FeS4 ·g4(Mr Cu5FeS4 / 4Mr S )

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

[0050] where Mr j represents the relative atomic mass of element or component j;

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

[0052] ∑G 产出 = g4 + g5 + g6 + g7

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

[0054] For elemental sulfur, in the copper slag, copper matte and dust of the smelting output, the content of S is From the sulfur element balance, the sulfur content in the flue gas is: 1000g 1,S t / h, and the oxygen consumption of sulfur element before and after the reaction is:

[0055]

[0056] The mass of oxygen element in the input material is 1000 g 1,O kg. According to the conservation of oxygen element, the theoretical oxygen demand for smelting per hour is:

[0057]

[0058] As a further improvement of the present invention, the non-linear chaotic flow calculation program includes:

[0059] A database obtained by calculating the core indexes of the injection effect with the change of different smelting state parameters and injection parameters through computational fluid dynamics technology;

[0060] Construct a mathematical model of non-linear chaotic flow enhanced stirring according to the database, which can effectively reflect the relationship between smelting state parameters, injection parameters and core indexes;

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

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

[0063] A device for strengthening copper smelting with carbon disulfide self-heating and chaotic stirring, which is applied to the method for strengthening copper smelting with carbon disulfide self-heating and chaotic stirring. The device for strengthening copper smelting with carbon disulfide self-heating and chaotic stirring is successively a furnace top area, a furnace body area, and a hearth area from top to bottom;

[0064] The furnace top area includes a furnace wall, a charging port, a rising flue, and a secondary air blowing lance. A number of secondary air blowing lances are installed on both sides below the furnace wall, a number of charging ports are installed above the furnace wall, and a rising flue is installed at the middle position of the top of the furnace wall; the charging port is used to input materials, and the rising flue discharges high-temperature flue gas, and a waste heat boiler is connected downstream;

[0065] The furnace body area includes side blowing lances, which are composed of copper water jackets. Side blowing lances are arranged on the side wall of the furnace body for blowing oxygen-rich air;

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

[0067] Initial ore proportioning and real-time temperature monitoring of the present invention. According to the preset smelting requirements, initial ore proportioning is carried out to ensure that the chemical composition of the ore can provide the required elements (such as sulfur) during the subsequent smelting process to support the reaction; non-contact measurement technology is used to monitor the furnace temperature in real time, which can quickly obtain temperature data and avoid delays or errors that may be brought by traditional monitoring methods. 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. Temperature adjustment and oxygen input calculation. According to the real-time temperature feedback, the addition method of the ore can be adjusted in a timely manner: - 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, a sulfur-oxygen matching program is used to calculate the minimum threshold of the oxygen input to ensure the complete oxidation of sulfur and provide the required heat. Significance: The dynamic adjustment based on temperature feedback can effectively reduce the negative impact brought by 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. The furnace temperature, slag thickness, matte thickness and the minimum threshold of oxygen input monitored in real time are input into a non-linear 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 the injection speed, oxygen concentration and injection angle, it is fed back to the furnace temperature in real time 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, enhancing the system's adaptive ability and providing guarantee for ensuring the reliability and safety of the copper smelting process. Description of the Drawings

[0068] Figure 1 It is a schematic flow chart of the steps of an embodiment of the method for strengthening copper smelting with carbon disulfide self-heating and chaotic stirring according to the present invention;

[0069] Figure 2 It is a schematic diagram of the principle of an embodiment of the method for strengthening copper smelting with carbon disulfide self-heating and chaotic stirring according to the present invention;

[0070] Figure 3 It 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 with carbon disulfide self-heating and chaotic stirring according to the present invention;

[0071] Figure 4 It is a schematic flow chart of the steps of comparing the furnace temperature with the expected range of an embodiment of the method for strengthening copper smelting with carbon disulfide self-heating and chaotic stirring according to the present invention;

[0072] Figure 5Schematic diagram of the step flow of a non-linear chaotic flow calculation program for an embodiment of the method for strengthening copper smelting by carbon sulfide self-heating and chaotic stirring according to the present invention;

[0073] Figure 6 Schematic diagram of the side-blowing functional module of an embodiment of the device for strengthening copper smelting by carbon sulfide self-heating and chaotic stirring according to the present invention;

[0074] Figure 7 Schematic diagram of the bottom-blowing functional module of an embodiment of the device for strengthening copper smelting by carbon sulfide self-heating and chaotic stirring according to the present invention;

[0075] Figure 8 Schematic diagram of the top-blowing functional module of an embodiment of the device for strengthening copper smelting by carbon sulfide self-heating and chaotic stirring according to the present invention;

[0076] Figure 9 Schematic diagram of the structure of an embodiment of the electronic device according to the present invention;

[0077] Figure 10 Schematic diagram of the structure of an embodiment of the storage medium according to the present invention. Detailed implementation manners

[0078] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope 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 specifying the quantity of the indicated technical features. Thus, the features defined with "first", "second", and "third" may explicitly or implicitly include at least one of such features. In the description of the present invention, the meaning of "a plurality" is at least two, such as two, three, etc., unless otherwise 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 positional relationship and movement conditions between components in a specific posture (as shown in the drawings). If the specific posture changes, the directional indications will also change accordingly. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. 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 may optionally further include steps or units not listed, or may optionally further include other steps or units inherent to these processes, methods, products, or devices.

[0080] References to "embodiments" in this specification mean that a particular feature, structure, or characteristic described in connection with the embodiments can be included in at least one embodiment of the invention. The phrase appears in various places in the specification and does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive of other embodiments. Those skilled in the art will explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

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

[0082] Step S1: Perform an initial ore mixture ratio and monitor the furnace temperature in real time using non-contact measurement;

[0083] Step S2: Adjust the addition of ore according to the furnace temperature monitored in real time. When the furnace temperature is lower than the expected range, add sulfur-rich ore; when the furnace temperature is higher than the expected range, add sulfur-poor ore; input the added ore mixture ratio into the sulfur-oxygen matching program to calculate the minimum threshold of the oxygen input amount;

[0084] Step S3: Input the furnace temperature, slag thickness, matte thickness, and minimum threshold of the oxygen input amount monitored in real time into the non-linear chaotic flow calculation program to calculate the injection speed, oxygen supply concentration, and injection angle; after the smelting process starts, adjust the injection speed, oxygen concentration, and injection angle in real time and feedback them to the furnace temperature to achieve dynamic control.

[0085] Preferably, in step S1 of this embodiment, the initial ore mixture ratio and real-time temperature monitoring are carried out. According to the preset smelting requirements, the initial ore mixture ratio is determined to ensure that the chemical composition of the ore can provide the required elements (such as sulfur) to support the reaction during the subsequent smelting process; the non-contact measurement technology is used to monitor the furnace temperature in real time, which can quickly obtain temperature data and avoid the delay or error that may be brought by traditional monitoring methods. Significance: By reasonable ore mixture ratio, 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. In step S2, temperature adjustment and oxygen input calculation are carried out. According to the real-time temperature feedback, the addition method of the ore can be adjusted in time: - When the temperature is low, sulfur-rich ore is added to increase the temperature; - When the temperature is high, sulfur-poor ore is added to control and reduce the temperature; after the ore mixture ratio is determined, the sulfur-oxygen matching program is used to calculate the minimum threshold of the oxygen input to ensure the complete oxidation of sulfur and provide the required heat. Significance: The dynamic adjustment based on temperature feedback can effectively reduce the negative impact brought by temperature fluctuations and ensure that the smelting process is more stable; through accurate oxygen matching, the heat release of the oxidation reaction can be optimized and the energy utilization efficiency of smelting can be improved. In step S3, chaotic flow calculation and dynamic control are carried out. The furnace temperature, slag thickness, matte thickness and the minimum threshold of oxygen input monitored in real time are input into the non-linear 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 of the injection speed, oxygen concentration and injection angle, the real-time feedback is sent 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 adaptability and providing guarantee for ensuring the reliability and safety of the copper smelting process (for the specific principle, please refer to Appendix Figure 2 ).

[0086] In order to achieve the matching of substances and energy during the smelting process in this embodiment, initial ore blending is required to make the sulfur element content basically meet the heat production 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 through the oxidation of sulfur elements; when the furnace temperature is higher than the expected temperature, sulfur-poor ore is added to cool down the furnace. Subsequently, the added ore mixture ratio is input into the sulfur-oxygen matching program, and the minimum threshold of the oxygen input is calculated to ensure that the oxygen-enriched side blowing can provide an oxidizing atmosphere and meet the demand for the timely oxidation and heat release of sulfur elements. Next, the furnace temperature, slag thickness, matte thickness and the minimum threshold of oxygen input monitored in real time during the smelting process are input into the non-linear chaotic flow calculation program to obtain the best injection speed, oxygen supply concentration and injection angle. After the smelting has been carried out for a period of time, the adjustment of the injection speed, oxygen concentration and injection angle will ultimately be reflected in the furnace temperature, and then the next round of control is carried out.

[0087] In summary, this embodiment forms an efficient and intelligent copper smelting system through three steps: ensuring suitable reaction conditions through optimized initial ore mixture ratio and real-time monitoring; then gradually optimizing the smelting process with dynamic adjustment to improve reaction efficiency; and finally achieving real-time control through chaotic flow calculation to ensure the reliability and safety of the copper smelting process, which helps to improve the energy utilization rate, production efficiency and product quality of the smelting process, and provides a scientific basis for industrial copper smelting. The realization of this embodiment depends on the timeliness of sulfur element oxidation in the material. Only when sulfur element oxidizes and releases heat quickly can the high temperature of the furnace be maintained. Therefore, another key point of the present invention is to improve the heat and mass transfer efficiency through the non-linear chaotic enhanced stirring method to ensure sufficient contact, reaction and heat release between sulfur element and oxygen. Based on this, problems such as large fuel consumption, high smelting cost and high carbon emission pressure in traditional bath smelting are solved.

[0088] Further, as Figure 3 shown, the process of the initial ore mixture ratio and real-time monitoring in step S1 specifically includes the following steps:

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

[0090] Step S12: Arrange several thermal imaging cameras and infrared sensors around the furnace according to the volume of the furnace. 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 acquisition network with several thermal imaging cameras, and summarize the captured temperature data to the nodes of the distributed temperature acquisition network;

[0091] Step S13: Perform data fusion on the temperature data summarized to the nodes, generate a temperature distribution map according to the fused temperature data, compare the furnace temperature with the expected range, and obtain the comparison result between the furnace temperature and the expected range.

[0092] Preferably, in step S11 of this embodiment, the analysis of ore characteristics and dynamic optimization of the ratio is carried out. By comprehensively analyzing the physical, chemical, and mineralogical characteristics of the ore and establishing a correlation model, the influence of different ore combinations on the smelting effect can be predicted more accurately; by using statistical and machine learning algorithms, the relationship between the ore and the smelting effect can be quantified; the ore ratio can be dynamically adjusted according to the furnace temperature and other feedback data to ensure the best ratio scheme under different operating conditions (such as temperature fluctuations and changes in ore characteristics). Significance: Through scientific and reasonable ore ratio, 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 production stability and greatly reduce the operation risk. In step S12, the layout of thermal imaging cameras and infrared sensors is carried out. By arranging multiple thermal imaging cameras and infrared sensors around the furnace, the comprehensive real-time temperature monitoring of the furnace surface and interior can be realized, and temperature information can be obtained from multiple angles; by constructing a distributed temperature acquisition network, the data of each temperature monitoring point can be effectively integrated to ensure the comprehensiveness and accuracy of the temperature information. Significance: The distributed monitoring network improves the real-time performance and reliability of data acquisition, which helps to detect temperature anomalies in a timely manner and prevent equipment failures and the decline of smelting efficiency caused by too high or too low temperatures; comprehensively collecting temperature data provides a solid foundation for subsequent data analysis and decision-making, and promotes the intelligent management of the smelting process. In step S13, data fusion and generation of temperature distribution map are carried out. By fusing and analyzing the scattered temperature data collected, the measurement errors that may occur in a single sensor can be eliminated, and more accurate temperature data can be provided; by means of data processing methods, the temperature distribution map of the furnace 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 furnace temperature can be detected in a timely manner and corresponding adjustment measures can be taken to achieve precise temperature control and ensure that the smelting process is carried out within the best 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 constructing a comprehensive temperature monitoring system. It will promote the development of the smelting process towards intelligence, low energy consumption, and high efficiency, and lay a solid foundation for realizing efficient and green copper smelting.

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

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

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

[0097] Step S113: According to the relationship between the characteristic variables and the smelting effect, create and optimize an association model to reflect the correlation between the ore material characteristics and the smelting effect; use the data to train the selected association model, verify it under multiple different ore materials and smelting conditions, and evaluate the performance of the association model using cross-validation;

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

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

[0100] Z = w1·Z1 + w2·Z2 - w3·Z3

[0101]

[0102] In the formula, w1, w2, and w3 are the 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 material i, X(i) is the supply amount of the ore material; R(j) is the amount of metal extracted from ore material j, Y(j) is the content of the ore material, T(k) is the total amount of all ore materials; E(l) is the consumption amount of each type of energy, and Z(l) is the coefficient or unit price related to this energy.

[0103] Preferably, in step S111 of collecting aggregate characteristic data in this embodiment, a comprehensive systematic analysis is carried out on the physical properties, chemical compositions and mineralogical characteristics of the aggregates to construct a complete aggregate characteristic database; the XRF technology can be used to quantitatively analyze the existence status of various elements in the aggregates, including valence states and forms; the scanning electron microscope provides microstructure information, and the physical properties such as particle size distribution, density and shape provide basic data for subsequent smelting calculations. Significance: It provides 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, it ensures the accuracy of aggregate characteristics and lays a foundation for accurately predicting the smelting effect. In step S112 of data standardization and feature engineering, specific standardization processing eliminates the deviations caused by different measurement methods and units, enabling various types of data to be compared and analyzed under a unified standard; through data analysis tools (such as principal component analysis, etc.), the key features that have a significant impact on the smelting effect are identified to construct a feature variable library. Significance: It improves the usability and comparability of the data to ensure the effective conduct of subsequent analysis; the identification of key features helps to further improve the accuracy and relevance of the model, making the optimization process more targeted. In step S113 of creating and optimizing the correlation model, using the relationship between the feature variables and the smelting effect, a statistical or machine learning model is created to form a framework capable of quantitatively predicting the smelting results; through the data of various aggregates and operating conditions for training, the cross-validation technology is used to evaluate the performance of the model to ensure its good generalization ability. Significance: It can scientifically reveal the relationship between aggregate characteristics and smelting effects, making the entire smelting process more scientifically based; through accurate prediction, it reduces the technical risks faced in the smelting process and supports the scientificity and rationality of production decisions. In step S114 of optimizing the objective function and dynamic optimization strategy, the determination of the optimization objective function gives a clear direction to the entire aggregate proportion optimization process, enabling targeted optimization, such as maximizing the smelting output or reducing energy consumption; by dynamically adjusting the correlation model and combining real-time feedback data, the proportioning strategy is continuously optimized to ensure that it can adapt to the changing requirements of the smelting process. Significance: The clear optimization objective and continuously adjusted strategy can significantly improve the smelting efficiency, enhance the product quality and resource utilization rate, and reduce unnecessary material and energy waste; dynamic optimization ensures the adaptive ability of the system under different smelting conditions, provides support for the flexibility and stability of operations, and at the same time promotes the development of the smelting process towards intelligence.

[0104] In summary, through the refinement and analysis of each step of the initial aggregate proportion optimization in this embodiment, through scientific aggregate characteristic analysis, data processing, model construction and dynamic optimization, not only the efficiency, reliability and safety of the smelting process are improved, but also the progress of the entire metallurgical industry in terms of intelligence and greenness is promoted. In future metallurgical production, this series of steps will provide an important foundation and guarantee for achieving the production goals of low-carbon, high-efficiency and sustainable development.

[0105] Further, 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, use a histogram to visualize various ore material characteristics, obtain the preliminary relationships between different ore material characteristics such as particle size, chemical composition, and mineral type and smelting effects such as metal recovery rate, and identify the preliminary key features; use color coding to display the correlation strength between each ore material characteristic and the smelting effect;

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

[0108] Step S1123: Set a dynamic threshold, and the key features exceeding this dynamic threshold are retained; combine the selected key features with the extracted interaction features to form a new feature variable set containing the feature vector framework, representing the physical, chemical, and mineral characteristics of the ore material, and clarify the source, measurement method, influencing factors, and their theoretical basis of each selected key feature.

[0109] Preferably, in step S1121 of this embodiment, data visualization and preliminary key feature identification are carried out. The characteristics of ore materials are visualized through a histogram, intuitively showing the relationship between different ore material characteristics (such as particle size, chemical composition, mineral type) and smelting effects (such as metal recovery rate); the color coding is used to display the correlation strength between features and smelting effects, providing a quantitative basis for subsequent analysis. Significance: It helps to quickly identify potential key features, guides subsequent data processing, and improves the efficiency of feature selection; through visualization technology, it enhances the understanding of data distribution and relationships, lays a foundation for subsequent in-depth analysis, and helps the decision-making process to be more efficient and accurate. In step S1122, feature combination and non-linear relationship mining are carried out. The features are combined and evaluated through a feature importance scoring system to identify feature combinations that have a significant impact on smelting effects; interactive features are constructed to capture non-linear relationships between multiple features, deeply explore the internal connections between features, and expand the richness of feature expression. Significance: Through the construction of combined and interactive features, the expressiveness of the model for complex relationships is improved, ensuring the representativeness and effectiveness of the selected features, thereby enhancing the prediction ability of geological ore features for smelting effects; it enables the maximum utilization of data information in the combination of different features, improves the accuracy of subsequent models, and provides a guarantee for reducing model errors. In step S1123, dynamic threshold setting and feature variable set construction are carried out. By setting a dynamic threshold mechanism, unimportant features are effectively screened out, ensuring that the remaining features all have a significant impact on smelting effects; the selected key features and interactive features are integrated to construct a feature variable set containing various characteristics of ore materials, providing a perfect basis for downstream model training. Significance: The setting of the dynamic threshold not only improves the flexibility of feature selection, but also makes each element in the feature set undergo rigorous inspection 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, traceability and interpretability can be provided for feature selection, enhancing the reliability and transparency of the model.

[0110] In summary, the above three steps in this embodiment complement each other in the process of feature selection and construction, jointly promoting the efficiency and accuracy of data analysis in the smelting process. The data visualization and preliminary feature identification lay the foundation, the feature combination and non-linear relationship mining deepen the understanding, and the dynamic threshold and feature variable set construction form a complete feature expression. It not only improves the prediction ability and scientific nature of the model, but also provides a solid data foundation for the subsequent optimization of smelting effects, making the improvement of smelting technology more scientific and precise, and ultimately realizing the efficient utilization of resources and sustainable development.

[0111] Further, the process of training the selected association model with data 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; independently train each basic model, 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 logistic function to convert the performance of the basic model into weights, perform a weighted average of the prediction results of all basic models, calculate the predicted value of each basic model multiplied by its corresponding weight according to the performance evaluation results of all basic models, sum up all the weighted predicted values to obtain an association model;

[0114] Step S1133: Regard features and smelting effects as nodes, analyze the connection strength between features by constructing a feature network, and identify the most critical feature combinations from them; use the collected feature variables to construct an association model, perform preliminary training of the association model in combination with the training data, and use the Bayesian optimization algorithm to optimize the hyperparameters of the association model.

[0115] Preferably, for the selection and independent training of the basic model in step S1131 of this embodiment, by selecting linear regression as the basic model and leveraging its simplicity and interpretability, a preliminary understanding of the linear relationship between features and smelting effects is established; by using different feature subsets to provide training data for the basic model, the performance of each feature under different conditions can be captured, overfitting can be avoided, and the generalization ability of the model can be improved; different weights are assigned to different data points to ensure that the model can focus on important data points (such as samples that appear under specific smelting conditions), enhancing the sensitivity of the model to the importance of samples. Significance: It provides a solid foundation for subsequent model training. By selecting an appropriate model and weighting strategy, the learning of the model is both scientific and flexible; by ensuring the diversity and pertinence of the model, the relationship between smelting effects and ore characteristics can be efficiently captured, providing a reliable baseline for subsequent evaluation. In step S1132, weighted averaging and model integration are performed. The performance of the basic model is converted into weights to ensure that models with better performance contribute more to the final prediction result during the integration process; by performing weighted averaging on the prediction results of all basic models to form a combined effect, the prediction error caused by the bias of a single model can be effectively reduced. Significance: It realizes the effective integration of multiple models, leveraging the advantages of ensemble learning and using the prediction results of different models to achieve higher accuracy and robustness; through the application of a logical function, the weights of the model can be adjusted in a timely manner to reflect the performance of the model under different conditions, ensuring the optimization of the performance of the final model. In step S1133, feature network construction and hyperparameter tuning are carried out. By constructing a feature network to view the connection strength between features, it helps to identify the most critical features, optimize feature selection, and then construct a more accurate model; combined with the collected feature variables for preliminary training, it can provide sufficient background knowledge for the model, thus better reflecting the relationship between ore characteristics and smelting effects; using the Bayesian optimization algorithm to tune hyperparameters can efficiently find the optimal solution in a high-dimensional parameter space and reduce the error of the model. Significance: Through the construction of the feature network, an in-depth understanding of the mutual relationship between features is achieved, providing support for targeted feature selection during the ore optimization process and ensuring the scientific nature of subsequent analysis; through hyperparameter tuning, the optimal performance of the model is ensured, enabling the associated model to more effectively handle different smelting conditions in application, improving the metal recovery rate or reducing energy consumption.

[0116] In summary, through the above steps in this embodiment, the entire training process 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 practical foundation for subsequent dynamic optimization, real-time feedback, and online self-learning, making the optimization of ore proportion more intelligent and efficient, and ultimately enhancing 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: Adopt hierarchical modeling, divide the furnace into multiple functional areas such as the feed inlet, combustion zone, reaction zone, and gas flow outlet area, use computer-aided design tools to construct a high-precision geometric model, capture complex terrain and heat exchange characteristics; input material properties such as the 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, and cooling method; use computational fluid dynamics software for coupled analysis of fluid and thermal fields, utilize high grid accuracy and detailed time step sampling to obtain more accurate heat flux distribution and temperature field data, and visualize the heat flux and temperature distribution of the simulation results in the form of heat maps and streamlines to intuitively display the temperature and heat flux change trends in each area and identify key heat sources and temperature gradients.

[0120] Step S123: According to the simulation results, use cluster analysis to identify areas with significant temperature changes, focus on considering the positions of the inner wall of the furnace with high temperature and rapid temperature changes, high heat load areas, and gas flow intersections, and construct a priority thermal measurement monitoring map through the distribution characteristics of heat flux data to determine the monitoring positions.

[0121] Preferably, in step S121 of hierarchical modeling and material input in this embodiment, by dividing the furnace into multiple functional regions (such as the feed inlet, combustion zone, reaction zone, gas flow outlet, etc.), the complex terrain and heat exchange characteristics inside the furnace are accurately captured, enabling more accurate subsequent heat flow and temperature simulations; the material properties such as thermal conductivity, specific heat capacity, and density of each region inside the furnace are input to ensure that the heat transfer characteristics of each region are accurately reflected, providing a real physical basis for the implementation of the CFD model. The achieved significance: The modeling of the furnace is made more in line with the actual situation, and the thermophysical behavior during actual operation can be reflected in the computational model; the thermal environment of the furnace is determined to ensure reliable basic data for subsequent fluid dynamics and thermal field analysis. In step S122 of multi-scenario simulation and visualization analysis, the coupled simulation of fluid and thermal field is carried out using CFD software, which can reflect the interaction of different gas flows and temperature fields inside the furnace, providing more comprehensive data for analysis; by improving the resolution and time resolution of the computational model, more accurate heat flow and temperature distribution data are obtained, reflecting more microscopic thermal dynamic changes; the simulation results are presented in the form of heat maps and streamlines, making the complex heat flow and temperature field information more intuitive and understandable. The achieved significance: Intuitive visualization helps to quickly identify key heat sources and temperature gradients under different working conditions, providing a scientific basis for subsequent sensor layout; through the visual analysis of data, it can help decision-makers better understand the thermal dynamics of the furnace, providing support for equipment monitoring and fault identification. In step S123 of cluster analysis and monitoring map construction, cluster analysis is used to identify regions with significant temperature changes, and high-temperature regions and positions with sharp temperature changes (such as the inner wall of the furnace, high heat load regions, and gas flow intersections) are determined as key monitoring regions; a priority thermal measurement monitoring map is constructed based on the distribution characteristics of heat flow data, intuitively showing the monitoring importance and priority of each region. The achieved significance: Clearly defining the priority monitoring positions makes the monitoring plan more efficient, avoiding resource waste and improving the monitoring efficiency of the equipment; key monitoring of regions with significant temperature changes can promptly capture potential fault risks, improving the operating safety of the furnace. At the same time, through continuous data collection and analysis, it helps to optimize the overall production performance.

[0122] In summary, the three steps of this embodiment together constitute a complete process for confirming the positions of the thermal imaging camera equipment and infrared sensors, ensuring that through scientific simulation and analysis methods, combined with high-precision input data, a data-driven monitoring plan and efficient operation management are ultimately achieved. This not only improves the monitoring coverage and accuracy, helps to enhance the safety and operating efficiency of the furnace, but also improves the overall work efficiency.

[0123] Furthermore, as Figure 4 shown, the process of comparing the furnace 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, automatically activate the low-temperature feedback mechanism; according to the current temperature state of the furnace, automatically select the sulfur-rich ore variety from the ore warehouse, automatically weigh and convey the selected sulfur-rich ore, and dynamically allocate different varieties of sulfur-rich ore according to their calorific value and physical properties;

[0125] Step S22: Combine the low-temperature information, start the sulfur-oxygen matching program, calculate the minimum threshold of the oxygen input while adjusting the oxygen supply; after adding the sulfur-rich ore, continuously monitor the change of the furnace temperature, adopt the feedback control mechanism, set the evaluation standard for temperature rise, and once the temperature reaches the expected range, stop adding the sulfur-rich ore and switch to the reaction adjustment of using low-sulfur ore;

[0126] Step S23: During the smelting process, form a dynamically adjustable closed-loop system, and continuously iterate and adjust the ratio of sulfur-rich ore and sulfur-poor ore through subsequent low-temperature feedback data to optimize each reaction process.

[0127] Preferably, in step S21 of this embodiment, the low-temperature feedback mechanism activates and dynamically allocates sulfur-rich ore materials. The monitoring system monitors the temperature of the furnace in real time. When the temperature is lower than the set range (preset), the feedback mechanism is automatically activated to ensure a quick response. By automatically selecting the variety of sulfur-rich ore materials, different smelting requirements are met; according to the calorific value and physical properties of the ore materials, 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 materials in a timely manner, the autothermal reaction can be effectively utilized to increase the temperature of the furnace and reduce the reduction of smelting efficiency caused by low temperature; through precise ore material ratio and dynamic allocation, unnecessary resource waste in the smelting process is reduced, and the overall economy is improved. In step S22, the sulfur-oxygen matching program and temperature monitoring are carried out. Through the sulfur-oxygen matching program, the minimum threshold of the oxygen input amount is accurately calculated, and the oxygen supply is dynamically adjusted according to the current state of the furnace to ensure sufficient oxygen in the reaction process; after the sulfur-rich ore materials are added, the temperature change of the furnace is continuously monitored, and the evaluation standard for temperature rise is set. Once the temperature returns to the expected range, the operation process is adjusted in a timely manner. The significance achieved: Through precise oxygen control, non-ideal reactions caused by insufficient oxygen supply are avoided, ensuring the quality and safety of smelting; real-time monitoring and feedback mechanism realize the refined management of temperature, making the entire smelting process more flexible and controllable in temperature control. In step S23, dynamic adjustment and closed-loop system formation are carried out. A closed-loop control system based on real-time monitoring data is constructed. Through continuous analysis of the low-temperature feedback data, the ratio of sulfur-rich ore and sulfur-poor ore is quickly adjusted to achieve adaptive optimization of the smelting process; during the iterative adjustment process, the smelting operation can be continuously improved according to historical data and feedback of new states, so that the efficiency of each reaction is improved. The significance achieved: With the formation of the closed-loop system, the copper smelting process has the ability to learn and improve, can be continuously optimized in actual operation, and achieves the best reaction conditions through dynamic adaptation; by optimizing the ore material ratio and reaction conditions, it helps to reduce energy consumption and emissions, and promotes the copper smelting process to be more environmentally friendly and sustainable.

[0128] In summary, this embodiment has its unique technical effects and profound significance in the comparison process between the furnace temperature and 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, in the process of calculating the minimum threshold of the oxygen input amount in step S2, according to the law of conservation of elements, it can be known that the input and output amounts of oxygen flow remain unchanged in each link of copper smelting. Therefore, by calculating the difference in the oxygen element content in the furnace-charged ore materials and the output substances (copper slag, copper matte, dust, and flue gas), the minimum threshold of the theoretically required oxygen for smelting can be analyzed, specifically including:

[0130] In the entire chaotic stirring enhanced copper smelting process, the input substances involved can be mainly divided into copper concentrate, slag-forming agent (it should be noted that oxygen is not involved in the slag-forming process), and oxygen-enriched air:

[0131] ∑G 投入 = g1 + g2 + g3

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

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

[0134]

[0135] In the formula: η 1,j Represents the proportion of component j in the furnace charge, -; g 1,1、1,2、1,3、1,4、1,5、1,6 Represents the feed rate 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 Represents the proportion of component j in chalcocite, covellite, chalcopyrite, bornite, cuprite, and tenorite, -;

[0136] According to the chemical composition and component proportion of the substances in the furnace charge, the proportion of the main elements participating in the smelting reaction, namely copper, iron, sulfur, and oxygen, 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(Mr CuO / Mr O )

[0141] Where Mr j represents 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, fume and flue gas:

[0143] ∑G 产出 = g4 + g5 + g6 + g7

[0144] g4, 5, 6, 7 respectively represent the output amounts of copper slag, copper matte, fume and flue gas, t / h; Let η l,j be the content ratio of element and component j in product l;

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

[0146] For elemental sulfur, in the copper slag, copper matte and fume of the smelting products, the content of S is From the sulfur element balance, the sulfur content in the flue gas is: 1000g 1,S t / h. Since most of the S in the flue gas exists in the form of SO2, the oxygen consumption of sulfur element before and after the reaction can be obtained as:

[0147]

[0148] Regarding the oxygen consumption for strengthening the Fe element in copper smelting by chaotic stirring, it is assumed that the Fe element in the substances produced by the smelting reaction mainly exists in the form of FeO. And the iron element is conserved before and after the smelting reaction, with a mass of 1000 g 1,Fe kg. It can be known that the oxygen content in the FeO element of the smelting product accounts for 1000 g 1,Fe ·Mr O / Mr Fe kg;

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

[0150]

[0151] Preferably, for the accurate calculation of the theoretical oxygen demand in this embodiment, through the accurate analysis of oxygen elements in the input and output substances, the theoretical oxygen demand in the copper smelting process can be calculated; the calculation is based on the law of conservation of elements, ensuring 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 possible losses and conversions in the actual reaction. Improve the efficiency of the smelting process. By understanding the specific oxygen amounts required for the redox reactions of each ore material, the supply of oxygen-enriched air can be more accurately allocated, thereby ensuring the continuous and efficient progress of the 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. Optimization of resource consumption. During the smelting process, calculating the theoretical oxygen demand enables operators to flexibly adjust the oxygen input according to actual needs, avoiding resource waste caused by excessive or insufficient oxygen, helping to reduce operating costs, and also reducing environmental impacts to a certain extent. Realization of the refined control process. Through the analysis of the content of each element in the smelting reaction, fine control of oxygen supply can be achieved during the copper smelting production process; it is carried out through an automated system, and based on the data feedback of real-time monitoring, the required oxygen amount is dynamically adjusted to ensure the stability and safety of the operation in the furnace. Improve the product quality. Through the calculation of the theoretical oxygen demand, the conditions of the smelting reaction can be further optimized, reducing unqualified products caused by secondary reactions and improving the quality of the final copper product; accurate oxygen supply not only ensures the complete progress of the 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, forms data-driven process optimization, provides a theoretical basis and reference basis for the adjustment and improvement in the production process, and enables the entire smelting process to be continuously iteratively optimized by analyzing data.

[0152] In summary, this embodiment not only enhances the understanding of the oxygen element demand theoretically, but also achieves the goals of more efficient, safer and more environmentally friendly smelting process in practice. By reasonably 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, as Figure 5 shown, the process of the non-linear chaotic flow calculation program in step S3 specifically includes the following steps:

[0154] Step S31: Calculate the database of the core indexes of blowing (side blowing, bottom blowing and top blowing) changing with different smelting state parameters and blowing parameters through computational fluid dynamics technology;

[0155] Step S32: Construct a mathematical model for enhancing stirring with non-linear chaotic flow based on the database, which can effectively reflect the relationship among smelting state parameters, injection parameters, and core indicators;

[0156] Step S33: Through the established mathematical model for enhancing stirring with non-linear chaotic flow, calculate the injection parameters based on the smelting state parameters monitored in real time during the smelting process.

[0157] Preferably, for the calculation fluid dynamics technology and database construction in step S31 of this embodiment, through the computational fluid dynamics (CFD) technology, the influence of injection (side blowing, bottom blowing, and top blowing) on the melt flow characteristics 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 of the melt, velocity distribution, mixing uniformity, etc.; establish a database containing data under various smelting states and injection conditions to provide a basis for subsequent modeling and analysis. Significance achieved: The data provides the necessary experimental and simulation basis for subsequent model construction, ensuring the accuracy and applicability of the model; by analyzing the core indicators under different conditions, the key factors affecting smelting efficiency and product quality can be identified, thus providing a scientific basis for optimizing the smelting process. For the construction of the mathematical model for enhancing stirring with non-linear chaotic flow in step S32, construct a mathematical model based on the database so that it can reflect the relationship among smelting state parameters, injection parameters, and core indicators from the perspectives of non-linearity and chaotic flow, and can reveal complex flow mechanisms and reaction processes; through this model, a quantitative description of complex physical phenomena can be provided, facilitating in-depth analysis and understanding of the fluid behavior under different operating conditions. Significance achieved: A mathematical model that can reflect the flow mechanism provides a more accurate tool for replacing 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 the chaotic flow process theoretically but also can be directly applied to actual production to provide theoretical support for noise and uncertainty. For the real-time monitoring and injection parameter calculation in step S33, by monitoring the smelting state parameters in real time and calculating the injection parameters using the non-linear chaotic flow model, the whole process can achieve dynamic adaptation, timely reflect the changes in the furnace state, and automatically adjust the injection parameters to adapt to new smelting conditions; it can provide more accurate injection parameters and effective guiding 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, metal recovery rate can be increased, and the operating efficiency of the whole smelting process can be improved; the real-time monitoring and adjustment method reduce the production risks that may be caused by improper operation, ensuring the safety and stability of the overall production line; it provides technical support for the intelligent management of the smelting process, making the production process more intelligent and automated, and enhancing the scientificity and flexibility of operation.

[0158] In summary, each step of the non-linear 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 bath is a typical non-linear chaotic process, and there are significant non-linear relationships among the core indexes for measuring the side-blowing effect (material non-uniformity, temperature non-uniformity, velocity non-uniformity, stirring dead zone), smelting state parameters (slag layer thickness, matte thickness, furnace temperature), and injection parameters (injection speed, oxygen enrichment concentration, injection angle), and it is impossible to evaluate the influence of multiple parameters through conventional mathematical modeling methods.

[0160] In this embodiment, for the construction of the CFD database, computational fluid dynamics is a discipline that solves the control equations of fluid mechanics through mathematical calculations, obtains discrete quantitative descriptions of the flow field and phase interfaces, and predicts the fluid motion behavior in the calculation region based on this. In the present invention, in addition to solving the conventional conservation equations of mass, momentum, energy, 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 various immiscible fluids, including stratified flows and free surface flows. The VOF model assumes that there is no mutual penetration between multiphase fluids. For each additional phase added to the model, an additional volume fraction of the phase needs to be introduced. In each control volume, the sum of the volume fractions of all phases is 1. For the phase interface between multiple phases, it is tracked by solving the continuity equation of the multiphase volume fraction. For the q-th phase, this equation has the following form:

[0162]

[0163] In the formula, α q is the volume fraction of the q-th phase; ρ q is the density of the q-th 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 throughout the region, and at the same time, the velocity field, as the calculation result, is shared by each phase. The momentum equation depends on ρ and μ obtained based on the volume fractions of all phases within the control volume:

[0165]

[0166] In the formula, ρ is the density of the fluid; is the velocity of the fluid; μ is the viscosity of the fluid; is the body force;

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

[0168]

[0169] where Eq is calculated through the specific heat capacity of the q-th phase and the shared temperature T; k eff is the effective thermal conductivity; the source term S h includes radiation and other volumetric heat sources;

[0170] For the turbulence model equation, the k-ε model was proposed by Launder and Spalding. This model introduces two unknowns: the turbulent kinetic energy k and the turbulent dissipation rate ε. The transport equations corresponding to the two unknowns are respectively:

[0171]

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

[0173] (1) Take the oxygen-enriched side-blown furnace as the research object and establish a geometrically similar three-dimensional calculation domain model according to the actual size;

[0174] (2) Mesh the furnace model to obtain a hexahedral structured mesh for capturing the computational fluid domain. Special encryption is required near the nozzle and in the melt region to accurately capture the free liquid surface morphology and velocity;

[0175] (3) Set the gas-liquid interface as a free interface, consider the solid wall as a no-slip boundary, and use the standard wall function in the boundary layer near the wall; Set the inlet and outlet boundary conditions and initial conditions according to the actual industrial situation;

[0176] (4) Set the surface tension between the mixed gas and liquid phases in the VOF model and the k-ε turbulence model;

[0177] (5) Extract and analyze the material inhomogeneity, velocity inhomogeneity, temperature inhomogeneity, and stirring dead zone;

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

[0179] Construction of the non-linear chaotic mathematical model, including input parameters and measurement indicators. The input parameters include the smelting state parameters and lance injection parameters fed back in real time by the smelting monitoring system, as follows:

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

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

[0182] Measurement index: material non-uniformity U m , speed non-uniformity U v , temperature non-uniformity U T , stirring dead zone a;

[0183] The following describes the calculation method of the measurement index:

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

[0185] Material non-uniformity: The non-uniformity of a certain 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 respectively:

[0186]

[0187] Integrate and sum the non-uniformity components of substance A in the three directions in the melt region to obtain the total non-uniformity of substance A:

[0188]

[0189] Temperature non-uniformity: The non-uniformity of temperature T is measured by T'. Its expressions in the three directions of x, y, and z are respectively:

[0190]

[0191] Integrate and sum the non-uniformity components of temperature T in the three directions in the melt region to obtain the total non-uniformity of temperature T:

[0192]

[0193] Speed non-uniformity: The melt velocity V is a vector, including components in three directions, Vx, Vy, and Vz. Its non-uniformity in the x, y, and z directions can be expressed as:

[0194]

[0195] The total non-uniformity of speed v is expressed as:

[0196]

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

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

[0199] 2. Dynamic change of oxygen enrichment amount x:

[0200] 3. Dynamic change of lance spacing d:

[0201] 4. Dynamic change of injection angle θ:

[0202] 5. Dynamic change of slag layer thickness h s :

[0203] 6. Dynamic change of matte thickness h c :

[0204] 7. Dynamic change 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 parameters. f m , f v , f T , f aIt is a function that measures the complex relationship between indicators and parameters. The current model can be used to study the interactions between different parameters and their effects on system behavior and measurement indicators. By adjusting the parameters and initial conditions, the dynamic behavior of the system can be observed, including possible periodicity or chaos.

[0211] As Figure 6 shown, this embodiment also provides an embodiment of a device for strengthening copper smelting with thiocarbon self-heating and chaotic stirring. In this embodiment, the device for strengthening copper smelting with thiocarbon self-heating and chaotic stirring is applied to the method for strengthening copper smelting with thiocarbon self-heating and chaotic stirring in the above embodiment. The device for strengthening copper smelting with thiocarbon self-heating and chaotic stirring includes a furnace top area 1, a furnace body area 2, a hearth area 3, a furnace wall 4, a charging port 5, a rising flue 6, a secondary air blowing lance 7, a matte tapping hole 8, a side blowing lance 9, a slag discharge chamber flue 10, and a slag tapping port 11;

[0212] Among them, the device is successively the furnace top area 1, the furnace body area 2, and the hearth area 3 from top to bottom;

[0213] The furnace top area 1 includes the furnace wall 4, the charging port 5, the rising flue 6, and the secondary air blowing lance 7. A number of secondary air blowing lances 7 are installed on both sides below the furnace wall 4, a number of charging ports 5 are installed above the furnace wall 4, and the rising flue 6 is installed at the middle position of the top of the furnace wall 4; the charging port 5 is used to feed materials, and the rising flue 6 discharges high-temperature flue gas, and a waste heat boiler is connected downstream;

[0214] The furnace body area 2 includes the side blowing lance 9, which is composed of copper water jackets. Oxygen-enriched side blowing lances (side blowing lance 9) are arranged on the side wall of the furnace body, which are used to blow oxygen-enriched air. While forming an oxidizing atmosphere, they also play a role in stirring the melt, strengthening heat and mass transfer, and accelerating chemical reactions;

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

[0216] Preferably, the working principle of this embodiment is to introduce carbon sulfide (or a similar reducing agent) to carry out an autothermal reaction at high temperature, so that the copper ions in the copper ore are reduced to generate metallic copper; during 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 for material feeding and the discharge of high-temperature flue gas; the shaft area 2 introduces oxygen-rich air through a side-blown lance to assist combustion and enhance the stirring of the melt at the same time; the hearth area 3 processes the molten copper and slag, quickly discharges the generated copper and waste slag, and maintains the stability of the furnace internal state.

[0217] The technical effects and significance of each structure: The feeding port in the top area 1 is used for feeding raw materials to ensure the quantitative and stable supply of raw materials; the upcomer is responsible for the discharge of high-temperature flue gas, effectively removing waste gas and reducing the furnace internal pressure; the secondary air blowing lance introduces additional air to increase the oxygen concentration in the furnace, enhancing the oxidizing property and temperature of the reaction. Significance: It ensures the supply of materials and the stability of the furnace internal environment, creating good conditions for subsequent reactions. The shaft area 2 includes side-blown lances, which are composed of copper water jackets. The side-blown lances blow oxygen-rich air to achieve the mixing of the melt, enhance heat and mass transfer, and increase the reaction rate; form an oxidizing atmosphere, which helps to effectively remove impurities and optimize copper extraction; Significance: Through chaotic stirring, it improves the metal recovery rate and reaction efficiency, makes the smelting process more coordinated and stable, and reduces energy consumption at the same time. The hearth area 3 includes a matte discharge port, a slag discharge chamber flue, and a slag discharge port. The hearth shell is made of high-temperature resistant materials. The design of the matte discharge port and the slag discharge port ensures the rapid discharge of molten copper and slag, avoiding the influence of excessive impurities or solid substances on subsequent operations; the use of high-temperature resistant materials ensures the safety and durability of the hearth and extends the service life of the equipment. Significance: It effectively processes the molten products, improves the overall operation efficiency of copper smelting, reduces the risk of environmental pollution, and provides a reliable basis for subsequent smelting processes.

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

[0219] The oxygen-enriched side-blown furnace type in this embodiment is the basis for meeting the self-heating of sulfide ores during oxidation. Because only when the oxidation heat release rate of sulfur elements is greater than the heat supply for material melting and the heat dissipation of the flue gas / water jacket can the furnace temperature be maintained, ensuring the smooth progress of smelting. The oxygen-enriched side-blown furnace has natural advantages in this regard. It has a relatively deep slag layer inside, and the side-blown air can fully stir the melt, forming a gas-liquid-solid three-phase coexistence zone with the oxygen-enriched injection air, greatly increasing the contact area between the molten slag, materials, and gas phase, and realizing the rapid heating-melting-contact-reaction-heat release process of sulfide ores.

[0220] In this embodiment, the feed ratio of sulfur-rich ores and sulfur-poor ores can be adjusted in real time according to the furnace temperature feedback during the smelting process. Based on the oxygen-enriched side-blown furnace type and the non-linear enhanced stirring technology, the heat transfer, mass transfer, and chemical reaction rates in the furnace melt are greatly increased, enabling the rapid oxidation and heat release of sulfur elements. Eventually, carbon is substituted by sulfur, that is, the high temperature of the furnace can be maintained without adding extra fuel, meeting the smelting conditions. This method can effectively solve problems such as high fuel consumption, high smelting cost, and high carbon emission pressure in traditional bath smelting, helping enterprises achieve energy conservation, emission reduction, cost reduction, and efficiency improvement.

[0221] Furthermore, as Figure 7 shown, the device for self-heating with sulfur substitution and chaotic stirring enhanced copper smelting provided in this embodiment can be replaced with another structure. Replace the attached Figure 6 side blowing with bottom blowing, and the specific description is as follows: It includes a flue gas inlet 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 tapping port 18, and a first spray gun 19;

[0222] The flue gas inlet 12 is connected to the upcomer 6. The auxiliary burner port 13 is installed on the right side of the flue gas inlet 12. The slag discharge port 14 is installed at the lower end of the auxiliary burner port 13. The detection port 15 and the first feeding port 16 are installed successively on the left side of the flue gas inlet 12. The main burner port 17 is installed on the opposite side of the auxiliary burner port 13. The metal tapping port 18 and multiple first spray guns 19 are installed at the bottom of the flue gas inlet 12.

[0223] Preferably, in this embodiment, top blowing is achieved. Fuel is input through the main burner port 17 and the auxiliary burner port 13 for combustion to heat the reaction zone, promoting the reaction between carbon disulfide and copper ore; the heat generated by combustion enables the reactants to reach the required high temperature in the furnace, promoting chemical reactions and reducing the copper in the ore to the metallic state. At the same time, the upcomer 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 lance 19 in the device is used to inject gas or liquid into the molten metal, which can achieve chaotic stirring, continuously stir the molten metal, uniformly mix the smelting reactants, and improve the reaction efficiency; the chaotic stirring technology can enhance the metal extraction rate, reduce the smelting time, and improve 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 tapping port 18 are used to discharge the slag generated during smelting and obtain the metal liquid; the detection port 15 is used to monitor parameters such as the temperature and gas composition in the furnace, facilitating the adjustment of reaction conditions to ensure the safety and efficiency of the smelting process.

[0224] Through the comprehensive utilization of self-heating and chaotic stirring in this embodiment, the extraction rate of copper is improved, the copper in the raw materials is more fully reduced, and resource waste is reduced; the combustion of carbon disulfide triggers a self-heating reaction, reducing external energy consumption, and at the same time, the efficient heat exchange and gas flow design maximize the thermal energy utilization rate; the flue and flue gas ports mentioned in the device design can effectively discharge harmful gases, protecting the working environment and the surrounding ecology; by monitoring the detection port 15, the situation in the furnace can be understood in real time, and operations can be adjusted in a timely manner to avoid safety hazards. This technology realizes the efficient utilization of ore and fuel in the copper smelting process, reducing the dependence on natural resources; the combination of self-heating and chaotic stirring technologies provides new ideas for improving the copper smelting process, promoting scientific research and technological progress in the metallurgical field; by improving smelting efficiency, reducing waste emissions, and optimizing resource utilization, it meets the requirements of modern industrial sustainable development, creating conditions for realizing environmentally friendly metallurgy; through technological innovation, production efficiency is improved, operating costs are reduced, and thus the overall economic benefits of the metallurgical industry are enhanced.

[0225] Through the technical combination of carbon disulfide self-heating and chaotic stirring in this embodiment, not only the smelting efficiency of copper and the metal recovery rate are improved, but also important environmental protection and resource utilization benefits are achieved; it has important practical significance for improving the overall level and economic benefits of the metal smelting industry.

[0226] Furthermore, as Figure 8 shown, the device for strengthening copper smelting with carbon disulfide self-heating and chaotic stirring provided in this embodiment can be replaced with another structure, replacing the attached Figure 6 side blowing with top blowing, which is specifically described as follows: including a flue gas outlet 20, a second feeding port 21, a furnace slag protective layer 22, a second lance 23, a melt outlet 24, and a refractory furnace lining 25;

[0227] On the outside of the slag protective layer 22 is the refractory lining 25. Inside the slag protective layer 22, a second lance 23 is provided. The refractory lining 25 is provided with a second feeding port 21, and on the left side of the refractory lining 25 is a melt outlet 24.

[0228] Preferably, the second lance 23 of this embodiment is located inside the slag protective layer 22 and can inject gas or other substances into the melt to promote chaotic stirring. This stirring enhances the uniformity of the melt, enables the reactants to be fully mixed, and improves the reaction efficiency and the extraction rate of the metal. The function of the slag protective 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 furnace lining, and reduce heat loss; the design of the refractory lining 25 ensures the stability and safety of the furnace body at high temperatures, and at the same time provides heat conduction performance to ensure efficient heat conduction; the second feeding port 21 is used to feed auxiliary materials or new reactants into the furnace so as to supplement raw materials in a timely manner during the smelting process; the melt outlet 24 is used to discharge the molten metal after smelting is completed, facilitating subsequent casting or further processing; the flue gas outlet 20 guides the waste gas generated in the furnace out of the furnace, which helps environmental protection, prevents harmful gases from accumulating in the furnace, and reduces the impact on the operators.

[0229] This embodiment combines the chaotic stirring of the lance and the self-heating effect to achieve a more complete reaction, significantly improving the copper recovery rate and the smelting speed; the design of the refractory lining and the slag protective layer ensures the stability of the furnace body during long-term high-temperature operation, reducing the risk brought by the corrosion of high-temperature materials; by reducing the external heating demand through the self-heating reaction, optimizing energy utilization, and at the same time being equipped with a flue gas outlet to enhance environmental safety and reduce pollution to the external environment; the additional feeding port, lance and other designs provide technical flexibility, enabling the operator to adjust the raw materials and operating conditions during the smelting process according to actual needs; by improving the smelting efficiency and the metal recovery rate, waste is reduced, promoting the recycling of copper resources and making contributions to sustainable development; the design and application of this device represent the progress of modern metallurgical processes, integrating self-heating, chaotic stirring and advanced materials science, providing reference for other metal smelting processes; the efficient high-temperature process and material utilization rate reduce the production cost, thus enhancing the economic benefits of the metallurgical industry and strengthening the market competitiveness.

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

[0231] Such as Figure 9As shown in the figure, 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 for strengthening copper smelting with thiocarbon autothermal and chaotic stirring in any of the above embodiments.

[0233] The processor is configured to execute the program instructions stored in the memory to perform copper smelting with thiocarbon autothermal and chaotic stirring.

[0234] Among them, the processor can also be referred to as a CPU (Central Processing Unit, central processing unit). The processor may be an integrated circuit chip with signal processing capabilities. The processor can 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. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0235] Furthermore, Figure 10 It is a schematic structural diagram of a storage medium according to an embodiment of the present application. The storage medium of the embodiment of the present application stores program instructions capable of implementing all the above methods. Among them, the program instructions can be stored in the above storage medium in the form of a software product, including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods described in various embodiments of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes, or terminal devices such as computers, servers, mobile phones, and tablets.

[0236] In 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 merely illustrative. For example, the division of units is only a logical function division. In actual implementation, there may be other division methods. For example, 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 displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of devices or units can be in electrical, mechanical, or other forms.

[0237] In addition, in each embodiment of the present invention, each functional unit may be integrated into one processing unit, may exist separately as individual physical units, 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 a software functional unit. The above is only the implementation manner of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

[0238] The specific implementation manners of the invention have been described in detail above, but they are only examples. The present invention is not limited to the specific implementation manners described above. For those skilled in the art, any equivalent modification or substitution of the invention is also within the scope of the present invention. Therefore, all equivalent transformations, modifications, improvements, etc. made without departing from the spirit and principles of the present invention should be covered by the scope of the present invention.

Claims

1. A method for strengthening copper smelting by carbon disulfide self-heating and chaotic stirring, characterized in that, The method for strengthening copper smelting with carbon disulfide autothermal and chaotic stirring includes: Carry out the initial ore material ratio, and use non-contact measurement to monitor the furnace temperature in real time; Adjust the addition of ore materials according to the furnace temperature monitored in real time. When the furnace temperature is lower than the expected range, add sulfur-rich ore; when the furnace temperature is higher than the expected range, add sulfur-poor ore; input the added ore material ratio into the sulfur-oxygen matching program to calculate the minimum threshold of the oxygen input amount; Input the furnace temperature, slag thickness, copper matte thickness and the minimum threshold of oxygen input amount monitored in real time into the non-linear chaotic flow calculation program to calculate the injection speed, oxygen supply concentration and injection angle; after the smelting process starts, according to the adjustment of the injection speed, oxygen concentration and injection angle, feed back to the furnace temperature in real time to achieve dynamic control; Calculating the minimum threshold of the oxygen input amount includes: The input substances are copper concentrate, slag former and oxygen-enriched air: ； : Copper concentrate, slag-forming agent, and oxygen-enriched air, t / h; The total feeding amount of the furnace charge and the proportion of each component are expressed as: ； Wherein: represents the proportion of the content of component j in the furnace charge; represents the feeding amount of chalcocite, covellite, chalcopyrite, bornite, cuprite, and tenorite, t / h; Represents the proportion of the content of component j in chalcocite, covellite, chalcopyrite, bornite, cuprite, and tenorite; According to the chemical composition and component proportion of the substances in the furnace charge, calculate the proportion of the content of elements copper, iron, sulfur and oxygen participating in the smelting reaction: ； where Mr j represents the relative atomic mass of element or component j; The output substances in the copper smelting process strengthened by chaotic stirring are divided into copper slag, copper matte, dust and flue gas: ； Let the amounts of copper slag, copper matte, soot and flue gas produced be represented by t / h; let be the product l be the proportion of the content of element and component j in For elemental sulfur, in the smelting products of copper slag, copper matte, and fumes, the S content is t / h. From the sulfur element balance, the sulfur content in the flue gas can be known as: t / h. The oxygen consumption of sulfur element before and after the reaction is: ； Mass of oxygen element in the input material kg. The theoretical oxygen demand for smelting per hour based on the conservation of oxygen element is as follows: ； The non-linear chaotic flow calculation program includes: Calculate the database of the core indexes of the injection effect changing with different smelting state parameters and injection parameters through computational fluid dynamics technology; Construct a mathematical model of non-linear chaotic flow enhanced stirring according to the database, which can effectively reflect the relationship between smelting state parameters, injection parameters and core indexes; Through the established mathematical model of non-linear chaotic flow enhanced stirring, calculate the injection parameters according to the smelting state parameters monitored in real time during the smelting process; The core indexes are material non-uniformity, temperature non-uniformity, velocity non-uniformity and stirring dead zone; the smelting state parameters are slag layer thickness, copper matte thickness and furnace temperature; the injection parameters are injection speed, oxygen enrichment concentration and injection angle.

2. The method for strengthening copper smelting by carbon disulfide self-heating and chaotic stirring according to claim 1, characterized in that The process of the initial ore material ratio and real-time monitoring includes: Carry out multi-dimensional data analysis on the physical, chemical and mineralogical characteristics of the ore materials, establish an association model between the ore materials and the expected smelting effect, and optimize the initial ore material ratio; at the same time, receive the feedback of the furnace temperature, and dynamically optimize the ratio strategy according to the feedback of different furnace problems; Arrange a number of thermal imaging cameras and infrared sensors around the furnace according to the volume of the furnace. 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 acquisition network with a number of thermal imaging cameras, and summarize the captured temperature data to the nodes of the distributed temperature acquisition network; Fuse the temperature data summarized to the nodes, generate a temperature distribution map according to the fused temperature data, compare the furnace temperature with the expected range, and obtain the comparison result between the furnace temperature and the expected range.

3. The method for strengthening copper smelting by carbon disulfide self-heating and chaotic stirring according to claim 2, characterized in that, The process of optimizing the initial ore material ratio includes: Collect the physical properties of various types of mineral materials; use the chemical analysis method XRF to determine the elemental content in the mineral materials, as well as the valence and existence form of each element, and conduct chemical composition analysis on the mineral materials; through a scanning electron microscope, 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 data of the collected various physical properties, chemical compositions and mineralogical characteristics; identify the key characteristics that have a significant impact on the smelting effect, and select and construct characteristic variables; Create and optimize an association model according to the relationship between the characteristic variables and the smelting effect to reflect the correlation between the mineral material characteristics and the smelting effect; use the data to train the selected association model, verify it under multiple different mineral materials and smelting conditions, and use cross-validation to evaluate the performance of the association model; Determine the optimization objective function, including maximizing the smelting output, increasing the metal recovery rate or reducing the energy consumption; optimize the initial mineral material ratio through repeated iteration; according to the real-time feedback data, conduct online learning and adjustment on the association model, and dynamically optimize the initial mineral material ratio strategy.

4. The method for strengthening copper smelting by carbon disulfide self-heating and chaotic stirring according to claim 3, characterized in that, The process of selecting and constructing the feature vector includes: After the data standardization process is completed, use a histogram to visualize the characteristics of various types of mineral materials to obtain the preliminary relationship between the characteristics of different mineral materials such as particle size, chemical composition and mineral type and the smelting effect of metal recovery rate, and identify the preliminary key characteristics; use color coding to display the correlation strength between each mineral material characteristic and the smelting effect; Combine multiple mineral material characteristics, and find the characteristic combinations that jointly have a significant impact on the smelting effect through feature importance scoring to further confirm the preliminary key characteristics; after identifying the preliminary key characteristics, introduce the construction of feature interaction terms to capture possible non-linear relationships, deeply explore the internal connections between the characteristics, and construct the interaction term characteristics of particle size and chemical composition; Set a dynamic threshold, and the key characteristics exceeding this dynamic threshold are retained; combine the selected key characteristics with the extracted interaction characteristics to form a new set of characteristic variables containing the feature vector framework, representing the physical, chemical and mineralogical characteristics of the mineral materials, and clarify the source, measurement method, influencing factors and their theoretical basis of each selected key characteristic.

5. The method for strengthening copper smelting by carbon disulfide autothermal and chaotic stirring according to claim 3, characterized in that The process of using data to train the selected association model 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; independently train each basic model, 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; Use a logistic function to convert the performance of the basic model into weights, perform weighted averaging on the prediction results of all basic models, calculate the predicted value of each basic model multiplied by its corresponding weight according to the performance evaluation results of all basic models, and sum all the weighted predicted values to obtain the association model; Regarding features and smelting effects as nodes, by constructing a feature network, analyzing the connection strength between features, and identifying the most critical feature combinations from them; using the collected feature variables to construct an association model, conducting preliminary training of the association model with the combined training data, and adopting the Bayesian optimization algorithm for hyperparameter tuning of the association model.

6. The method for strengthening copper smelting by carbon disulfide self-heating and chaotic stirring according to claim 2, characterized in that, The process of confirming the positions of the thermal imaging camera device and the infrared sensor includes: Adopting hierarchical modeling, dividing the furnace into multiple functional areas such as the feed inlet, combustion zone, reaction zone, and gas flow outlet area, using computer-aided design tools to construct a high-precision geometric model, capturing complex terrain and heat exchange characteristics; inputting the thermal conductivity, specific heat capacity, and density material properties of each area inside the furnace. Designing multiple scenarios for simulation according to different furnace operation conditions such as fuel type, combustion intensity, and cooling method; performing coupled analysis of fluid and thermal fields using computational fluid dynamics software, and obtaining more accurate heat flux distribution and temperature field data by using high grid accuracy and detailed time step sampling. Visualize the heat flux and temperature distribution of the simulation results in the form of heat maps and streamlines to intuitively display the temperature and heat flux change trends in each area and identify key heat sources and temperature gradients. According to the simulation results, use cluster analysis to identify areas with significant temperature changes, focusing on positions such as the inner wall of the furnace with high temperature and sharp temperature changes, high heat load areas, and gas flow intersections. Construct a priority thermal measurement monitoring map based on the distribution characteristics of heat flux data to determine the monitoring positions.

7. The method for strengthening copper smelting by carbon disulfide self-heating and chaotic stirring according to claim 1, characterized in that The process of comparing the furnace temperature with the expected range includes: When the monitored temperature is lower than the set expected range, automatically activate the low-temperature feedback mechanism; automatically select high-sulfur ore varieties from the ore warehouse according to the current temperature state of the furnace, automatically weigh and convey the selected high-sulfur ore, and dynamically allocate different varieties of high-sulfur ore according to their calorific values and physical properties. Combined with the low-temperature information, start the sulfur-oxygen matching program, calculate the minimum threshold of the oxygen supply while adjusting the oxygen supply; after adding the high-sulfur ore, continuously monitor the change of the furnace temperature, adopt a feedback control mechanism, set the evaluation criteria for temperature increase, and once the temperature reaches the expected range, stop adding the high-sulfur ore and switch to the reaction adjustment of using low-sulfur ore. During the smelting process, form a dynamically adjusted closed-loop system, and continuously iterate and adjust the ratio of high-sulfur ore and low-sulfur ore through subsequent low-temperature feedback data to optimize each reaction process.

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