Preparation method of corrosion-resistant protective film on the surface of copper smelting boiler tube

Abstract

The present application relates to the technical field of copper smelting boiler tube protective film preparation, and specifically discloses a method for preparing a corrosion-resistant protective film on the surface of a copper smelting boiler tube, which includes: obtaining the operating data of the flue gas entering the boiler from the reactor; determining the material of the surface protective film and the method for making the surface protective film based on the operating data of the flue gas; performing surface pretreatment on the boiler inlet to obtain the boiler inlet after surface pretreatment; applying a transition layer to the boiler inlet after surface pretreatment; and forming the surface protective film on the transition layer by the method for making the surface protective film. In this way, a suitable protective film material can be recommended based on the actual operating conditions of the flue gas, and a corresponding method for making the film can be given based on the material, so as to realize a more intelligent process for preparing the corrosion-resistant protective film on the surface of the copper smelting boiler tube, which helps to cope with complex corrosive environments and extend the service life of the boiler tube.

Description

Technical Field

[0001] The present application relates to the technical field of preparation of protective films for copper smelting boiler tubes, and more specifically, to a method for preparing a corrosion-resistant protective film on the surface of a copper smelting boiler tube. Background Art

[0002] In the copper smelting industry, boiler tubes, as one of the core equipment, are in high temperature, high pressure and corrosive environment for a long time. The corrosion of boiler tubes will not only shorten their service life and increase maintenance costs, but also affect production efficiency and safety. Therefore, improving the corrosion resistance of boiler tubes is the key to improving the stability and economic benefits of copper smelting process. Traditional metal parts anti-corrosion technology mainly includes the use of corrosion-resistant materials, increasing the thickness of parts and other methods. Although it improves the corrosion resistance of metal parts to a certain extent, it is still difficult to meet the needs of long-term stable operation under complex working conditions such as high temperature, oxygen-rich, and harmful impurity corrosion. In addition, the use of metal or non-metallic anti-corrosion protective films alone also has certain limitations under these extreme conditions, and it is impossible to extend the deep corrosion resistance and service life of metal parts in metallurgical production.

[0003] In recent years, with the advancement of science and technology and the development of interdisciplinary research, modifying the metal surface, such as coating a composite protective film on its surface, has become a new solution. This protective film is a fusion of metal or alloy materials and non-metallic materials, which can effectively isolate the direct contact between corrosive media and metal parts, and significantly improve the corrosion resistance and service life of metal parts. However, the existing protective film technology still faces some challenges. For example, the plated protective film is usually thin and attached to the surface of the metal part. The structural strength of the protective film itself is relatively weak, and it is easily damaged by severe or violent external forces, which is not suitable for actual application needs. In addition, the protective film will gradually wear out during use, and the effective working time of the surface protective film will be affected by many factors. For example, different high temperature conditions and boiler tube flue gas components will have different degrees of impact on protective films of different materials. The traditional corrosion-resistant protective film preparation scheme does not pay attention to this effect, which is easy to cause excessive wear of the protective film during use and reduce the service life of copper smelting boiler tubes.

[0004] Therefore, an optimized preparation scheme for the corrosion-resistant protective film on the surface of copper smelting boiler tubes is desired. Summary of the invention

[0005] The present application provides a method for preparing a corrosion-resistant protective film on the surface of a copper smelting boiler tube, which can recommend a suitable protective film material based on the actual working conditions of the flue gas, and provide a corresponding manufacturing method based on the material, so as to realize a more intelligent preparation process of the corrosion-resistant protective film on the surface of the copper smelting boiler tube, which helps to cope with complex corrosive environments and extend the service life of the boiler tube.

[0006] In a first aspect, a method for preparing a corrosion-resistant protective film on the surface of a copper smelting boiler tube is provided, comprising:

[0007] Acquire the operating data of the flue gas entering the boiler from the reaction furnace, wherein the operating data of the flue gas includes the time sequence of the flue gas temperature and the components of the flue gas;

[0008] Determining the material of the surface protection film and the method for manufacturing the surface protection film based on the working condition data of the flue gas;

[0009] Performing surface pretreatment on the boiler inlet to obtain a surface pretreated boiler inlet;

[0010] Applying a transition layer to the boiler inlet after the surface pretreatment;

[0011] The surface protection film is formed on the transition layer by the surface protection film manufacturing method.

[0012] The present application provides a method for preparing a corrosion-resistant protective film on the surface of a copper smelting boiler tube, which includes: obtaining the operating data of the flue gas entering the boiler from the reactor; determining the material of the surface protective film and the method for making the surface protective film based on the operating data of the flue gas; performing surface pretreatment on the boiler inlet to obtain a boiler inlet after surface pretreatment; applying a transition layer to the boiler inlet after surface pretreatment; and forming the surface protective film on the transition layer by the method for making the surface protective film. In this way, a suitable protective film material can be recommended based on the actual operating conditions of the flue gas, and a corresponding method for making the film can be given based on the material, so as to realize a more intelligent preparation process of the corrosion-resistant protective film on the surface of the copper smelting boiler tube, which helps to cope with complex corrosive environments and extend the service life of the boiler tube. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings of the embodiments of the present application are briefly introduced below. Obviously, the drawings described below only relate to some embodiments of the present application, and are not intended to limit the present application.

[0014] Figure 1 This is a schematic flow chart of a method for preparing a corrosion-resistant protective film on the surface of a copper smelting boiler tube according to an embodiment of the present application.

[0015] Figure 2 In the method for preparing the corrosion-resistant protective film on the surface of the copper smelting boiler tube in an embodiment of the present application, a schematic flow chart of determining the material of the surface protective film and the method for manufacturing the surface protective film based on the operating data of the flue gas.

[0016] Figure 3In the method for preparing the corrosion-resistant protective film on the surface of the copper smelting boiler tube in the embodiment of the present application, a data flow diagram for determining the material of the surface protective film and the method for manufacturing the surface protective film based on the operating data of the flue gas.

[0017] Figure 4 In the method for preparing the corrosion-resistant protective film on the surface of the copper smelting boiler tube in the embodiment of the present application, a feature compensation interactive analysis is performed on the flue gas temperature time series correlation feature vector and the flue gas component fully connected correlation coding vector to obtain a schematic flow chart of the deep interaction characteristics of the flue gas temperature-flue gas component. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work also fall within the scope of protection of the present application.

[0019] In view of the above technical problems, in the technical solution of the present application, a method for preparing a corrosion-resistant protective film on the surface of a copper smelting boiler tube is proposed. Figure 1 The schematic flow chart of the method for preparing the corrosion-resistant protective film on the surface of the copper smelting boiler tube according to the embodiment of the present application is shown in FIG. Figure 1 As shown, the method for preparing the corrosion-resistant protective film on the surface of the copper smelting boiler tube includes: S1, obtaining the operating data of the flue gas entering the boiler from the reactor, wherein the operating data of the flue gas includes the time queue of the flue gas temperature and the flue gas composition; S2, based on the operating data of the flue gas, determining the material of the surface protective film and the method for making the surface protective film; S3, performing surface pretreatment on the boiler inlet to obtain the boiler inlet after surface pretreatment; S4, applying a transition layer to the boiler inlet after the surface pretreatment; S5, forming the surface protective film on the transition layer by the method for making the surface protective film.

[0020] In the above-mentioned method for preparing the corrosion-resistant protective film on the surface of the copper smelting boiler tube, the S1 obtains the working condition data of the flue gas entering the boiler from the reactor, wherein the working condition data of the flue gas includes the time queue of the flue gas temperature and the composition of the flue gas. It should be understood that the boiler tube is in a high temperature, high pressure and corrosive environment for a long time, which is prone to corrosion, thereby shortening its service life, increasing maintenance costs, and affecting production efficiency and safety. Obtaining the working condition data of the flue gas (including temperature and composition) can more accurately analyze the corrosion mechanism, select the appropriate surface protective film material and preparation method, thereby improving the corrosion resistance of the boiler tube. Different flue gas components (such as SO2, HCl, etc.) and temperature conditions will lead to different corrosion mechanisms. By obtaining the working condition data of the flue gas, the protective film material suitable for specific working conditions can be more accurately recommended to cope with complex corrosive environments. By analyzing the temperature and composition of the flue gas, combined with advanced data analysis and machine learning technology, the most suitable protective film material and its preparation method can be recommended, thereby extending the service life of the boiler tube and reducing maintenance costs.

[0021] Optionally, in one embodiment of the present application, the flue gas composition includes a proportion of harmful impurities, and the harmful impurities include: 1. Nitrogen (N2): Its source is the gas that does not participate in the oxidation cracking reaction in the front-end reactor. Nitrogen itself is not corrosive, but its presence will affect the concentration and reaction kinetics of other corrosive gases. 2. Sulfur dioxide (SO2): Its source is the product formed by insufficient reaction. SO2 is a highly corrosive gas that can react with water to form sulfurous acid (H2SO3), which is then oxidized to sulfuric acid (H2SO4), which has a strong corrosive effect on metal surfaces. 3. Sulfur trioxide (SO3): Its source is the product of further oxidation of SO2. SO3 reacts with water to form sulfuric acid (H2SO4), which has a stronger corrosive effect on metal surfaces. 4. Oxygen (O2): Its source is oxygen in the air. Oxygen participates in the oxidation reaction and accelerates the oxidation corrosion of the metal surface. 5. Extremely corrosive anions such as sulfur (S), chlorine (Cl), and fluorine (F): Its source is impurities in the raw materials or reaction by-products. These anions react chemically with the metal surface to form corrosion products, which accelerate the corrosion of the metal. 6. Low-valent, non-thermally conductive, highly viscous silicate suspensions: They come from entrained mineral particles. These suspensions adhere to the boiler tubes, and because they are non-thermally conductive, they will cause the boiler tubes to lose their heat conduction function, leading to local overheating and even tube bursting. In addition, these suspensions may also corrode the metal tube material at high temperatures.

[0022] In the above-mentioned method for preparing the corrosion-resistant protective film on the surface of the copper smelting boiler tube, the S2 determines the material of the surface protective film and the method for making the surface protective film based on the working condition data of the flue gas. It should be understood that different flue gas components (such as SO2, HCl, etc.) and temperature conditions will lead to different corrosion mechanisms. For example, SO2 and SO3 will generate strong acids, which will accelerate the corrosion of metals; low-valent non-conductive and highly viscous silicate suspensions will adhere to the metal surface, affecting heat conduction and causing local overheating. By analyzing the temperature and composition of the flue gas, it is possible to more accurately recommend protective film materials suitable for specific corrosive environments and effectively respond to complex corrosion challenges. By recommending suitable protective film materials and preparation methods based on the actual working condition data of the flue gas, the service life of the boiler tube can be significantly extended, the number of maintenance and replacement times can be reduced, the production cost can be reduced, and the production efficiency and safety can be improved. Different protective film materials and preparation methods are suitable for different working conditions. For example, plasma spraying is suitable for high temperature environments, while flame spraying is suitable for medium temperature environments. By determining the best protective film material and preparation method based on flue gas operating data, the preparation process can be optimized to ensure the quality and performance of the protective film.

[0023] More specifically, the temperature of flue gas directly affects the high temperature resistance of the protective film. In a high temperature environment, the protective film needs to have good thermal stability and high temperature oxidation resistance. By analyzing the temperature data of flue gas, the material and preparation method of the protective film that can work stably at high temperatures can be selected. The corrosive components in flue gas (such as SO2, SO3, HCl, etc.) will cause chemical corrosion to the metal surface. Different components have different corrosion mechanisms, and it is necessary to select a protective film material that can effectively resist these corrosive components. By analyzing the composition of flue gas, the appropriate protective film material can be selected more accurately. The effective working time of the protective film depends not only on its chemical properties, but also on its physical and mechanical properties. For example, the protective film needs to have sufficient hardness and toughness to resist physical wear and mechanical stress at high temperatures. By determining the material and preparation method of the protective film based on the working condition data of flue gas, the physical and mechanical properties of the protective film under actual working conditions can be ensured.

[0024] Optionally, in one embodiment of the present application, the material of the boiler tube is selected, including stainless steel, boiler steel, etc. Based on the specific flue gas composition, the metal material that is most suitable for working under its working conditions and has good strength is selected, so that it can play a due supporting and auxiliary role on the surface anti-corrosion protective film; the manufacturing process and method of the protective film are demonstrated, and the selection is based on the respective characteristics of plasma spray coating, flame spray coating, arc spray coating, hot melt coating, surface surfacing, etc.; according to the thickness of the anti-corrosion protective film, the thickness is in the range of 0.2mm-0.35mm, and the material powder of the protective film required for use is prepared.

[0025] In the above-mentioned method for preparing the corrosion-resistant protective film on the surface of the copper smelting boiler tube, the S3 is to perform surface pretreatment on the boiler inlet to obtain the boiler inlet after surface pretreatment. It should be understood that the surface pretreatment can remove dirt, oil stains and other impurities on the surface of the boiler inlet, making the surface cleaner and smoother. This helps to improve the adhesion between the protective film and the substrate, ensuring that the protective film can be firmly attached to the surface of the boiler tube in a high temperature and corrosive environment to prevent it from falling off. Appropriate surface roughness can increase the contact area between the protective film and the substrate, improve the mechanical bite force of the protective film, thereby enhancing the wear resistance and corrosion resistance of the protective film. Through surface pretreatment, the surface roughness can be controlled within a suitable range. Surface pretreatment can remove tiny defects and irregular shapes on the surface, reduce stress concentration points, and thus reduce the risk of cracks and peeling caused by stress concentration during use of the protective film. A flat and clean surface is conducive to the uniform deposition and densification of the protective film, ensuring that the protective film forms a continuous and uniform covering layer on the entire surface, thereby improving its protective effect.

[0026] Optionally, in one embodiment of the present application, the surface pretreatment of the boiler tube includes surface processing, surface roughening by sandblasting, the roughness is between Ra0.2-0.4, and cleaning is dust-free and oil-free for transition layer processing. More specifically, the surface of the boiler inlet is thoroughly cleaned with solvents (such as acetone, alcohol, etc.) and detergents to remove dirt, oil stains and other impurities on the surface. After cleaning, the surface is thoroughly dried with compressed air or drying equipment to ensure that there is no moisture residue on the surface. The surface of the boiler inlet is polished with sandpaper, grinding wheel or grinder to remove the unevenness and burrs on the surface to make the surface flatter and smoother. For parts that require higher surface finish, polishing can be performed with polishing agents and polishing wheels. The surface of the boiler inlet is sandblasted with a sandblaster to increase the surface roughness by high-speed jetting of abrasives (such as quartz sand, steel sand, etc.) to improve the adhesion of the protective film. After sandblasting, the surface roughness is checked with a roughness measuring instrument to ensure that the roughness is between Ra0.2-0.4, which meets the requirements for protective film preparation. After blasting, clean the surface again with solvents and detergents to remove dust and residues generated during the blasting process. After cleaning, dry the surface thoroughly again with compressed air or drying equipment to ensure that the surface is clean and dust-free.

[0027] In the above-mentioned method for preparing the corrosion-resistant protective film on the surface of the copper smelting boiler tube, the S4 is to apply a transition layer to the boiler inlet after the surface pretreatment. It should be understood that the transition layer can form an intermediate layer between the substrate and the protective film to enhance the bonding force between the protective film and the substrate. The transition layer usually has good adhesion and mechanical properties, which can effectively fill the tiny defects on the surface of the substrate and improve the overall adhesion of the protective film. The transition layer can further improve the roughness of the surface and make the surface more uniform and smooth. This helps the protective film to form a more uniform and dense covering layer during the deposition process, thereby improving the protective effect of the protective film. The transition layer can reduce the stress concentration points on the surface of the substrate and reduce the risk of cracks and peeling caused by stress concentration during the use of the protective film. This helps to improve the durability and stability of the protective film. The transition layer usually has good corrosion resistance and can form an additional barrier between the substrate and the protective film to further improve the corrosion resistance of the entire protection system.

[0028] Optionally, in one embodiment of the present application, a suitable transition layer material is selected according to the material of the substrate and the material of the protective film. Commonly used transition layer materials include metals (such as nickel, aluminum, chromium, etc.), alloys (such as nickel-chromium alloys, nickel-cobalt alloys, etc.) and ceramics (such as aluminum oxide, zirconium oxide, etc.). Ensure that the surface of the boiler inlet after surface pretreatment is clean, flat, and has moderate roughness. This step has been completed in the previous step. Prepare using a transition layer preparation method, specifically, the transition layer preparation method includes: physical vapor deposition (PVD): using physical vapor deposition technology (such as magnetron sputtering, electron beam evaporation, etc.) to deposit a layer of transition layer material on the surface of the boiler inlet after surface pretreatment. Chemical vapor deposition (CVD): using chemical vapor deposition technology (such as hot wire CVD, plasma enhanced CVD, etc.) to deposit a layer of transition layer material on the surface of the boiler inlet after surface pretreatment. Thermal spraying: using thermal spraying technology (such as plasma spraying, flame spraying, arc spraying, etc.) to spray a layer of transition layer material on the surface of the boiler inlet after surface pretreatment. Electroplating: Use electroplating technology to electroplate a layer of transition layer material on the boiler inlet surface after surface pretreatment.

[0029] More specifically, in one embodiment of the present application, plasma spraying is used to prepare the transition layer. Specifically, first, the boiler inlet after surface pretreatment is fixed on the spraying equipment. Select suitable nickel-chromium alloy powder as the transition layer material. Use plasma spraying equipment to heat the nickel-chromium alloy powder to a molten state and spray it onto the surface of the boiler inlet after surface pretreatment. Control the spraying parameters (such as current, voltage, gas flow, etc.) to ensure that the thickness of the transition layer is between 5 microns and 10 microns. After spraying, use compressed air or drying equipment to thoroughly dry the surface to ensure that the transition layer is uniform and dense.

[0030] In the above method for preparing the corrosion-resistant protective film on the surface of the copper smelting boiler tube, the S5 is to form the surface protective film on the transition layer by the surface protective film manufacturing method. Specifically, the continuity of the anti-corrosion protective film manufacturing process is ensured, and the protective film is continuously manufactured at one time according to the demonstrated protective film manufacturing process method and the anti-corrosion protective film thickness requirement, and the film manufacturing process is prevented from being suspended or unstable in feeding.

[0031] In particular, in the above-mentioned method for preparing the corrosion-resistant protective film on the surface of the copper smelting boiler tube, it is considered that the effective working time of the surface protective film depends not only on the physical and mechanical strength of the metal parts under high temperature conditions, but also on the composition of the flue gas entering the boiler tube. In other words, the temperature and composition of the flue gas directly affect the performance of the protective film. Different flue gas components (such as SO2, HCl, etc.) and temperature conditions will lead to different corrosion mechanisms. In actual application scenarios, the flue gas comes out of the reactor and enters the boiler. At this time, the flue gas temperature is very high, 1100 degrees Celsius-1250 degrees Celsius, and the flue gas composition contains gases that do not participate in the oxidation and cracking reaction in the front-end reactor, such as nitrogen, SO2 and SO3 acid radicals that are not fully reacted, O, S, Cl, F, highly corrosive anions, and entrained low-valent, non-thermally conductive, highly viscous silicate suspensions. Therefore, it is crucial to determine the material of the surface protective film and the method for making the surface protective film based on the operating data of the flue gas. It can analyze the temperature and composition of the flue gas, combined with advanced data analysis and machine learning technology, to recommend the most suitable protective film material and its preparation method, thereby helping to improve the corrosion resistance of boiler tubes, extend their service life, and reduce maintenance costs.

[0032] Based on this, when using the operating data of the flue gas to determine the material and manufacturing method of the surface protective film, the technical concept of the present application is to analyze the flue gas temperature time series data and flue gas components by using data processing and analysis algorithms based on artificial intelligence and deep learning, so as to capture the compensatory interaction characteristics between the flue gas temperature time series semantics and the component semantics, so as to recommend the surface protective film material. In addition, the manufacturing method of the surface protective film is determined based on the recommended surface protective film material. In this way, it is possible to recommend suitable protective film materials based on the actual operating conditions of the flue gas, and give corresponding manufacturing methods based on the materials, so as to realize a more intelligent preparation process of corrosion-resistant protective films on the surface of copper smelting boiler tubes, which helps to cope with complex corrosive environments and extend the service life of boiler tubes.

[0033] Optionally, in one embodiment of the present application, Figure 2 In the method for preparing the corrosion-resistant protective film on the surface of the copper smelting boiler tube in an embodiment of the present application, a schematic flow chart of determining the material of the surface protective film and the method for manufacturing the surface protective film based on the operating data of the flue gas. Figure 3 This is a data flow diagram of the method for preparing the corrosion-resistant protective film on the surface of the copper smelting boiler tube according to the embodiment of the present application, in which the material of the surface protective film and the method for preparing the surface protective film are determined based on the working condition data of the flue gas. Figure 2 and Figure 3 As shown, based on the working condition data of the flue gas, the material of the surface protective film and the method for manufacturing the surface protective film are determined, including: S21, time series encoding the time queue of the flue gas temperature to obtain a flue gas temperature time series associated feature vector; S22, fully connected encoding the flue gas components to obtain a flue gas component fully connected associated coding vector; S23, feature compensation interactive analysis of the flue gas temperature time series associated feature vector and the flue gas component fully connected associated coding vector to obtain a deep interaction feature of flue gas temperature and flue gas components; S24, inputting the flue gas temperature and flue gas component deep interaction feature vector into a surface protective film material recommender based on a first decoder to obtain a decoding result of the material of the surface protective film.

[0034] In the above-mentioned method for preparing the corrosion-resistant protective film on the surface of the copper smelting boiler tube, the S21 performs time series encoding on the time queue of the flue gas temperature to obtain a flue gas temperature time series associated feature vector. Specifically, in the technical solution of the present application, first, the time queue of the flue gas temperature is input into a flue gas temperature time series encoder based on a forward LSTM model for encoding to extract the time series implicit associated feature information of the flue gas temperature, thereby obtaining a flue gas temperature time series associated feature vector.

[0035] Optionally, in one embodiment of the present application, the time queue of the flue gas temperature is time-series encoded to obtain a flue gas temperature time-series associated feature vector, including: inputting the time queue of the flue gas temperature into a flue gas temperature time-series encoder based on a forward LSTM model to obtain the flue gas temperature time-series associated feature vector.

[0036] In the above-mentioned method for preparing the corrosion-resistant protective film on the surface of the copper smelting boiler tube, the S22 is to fully connect the components of the flue gas to obtain a fully connected associated coding vector of the flue gas components. It should be understood that, considering that the components of the flue gas usually contain a variety of gases and particulate matter, there are complex interactions between these components. Therefore, in order to better characterize the complex correlations between these flue gas components, in the technical solution of the present application, a flue gas component embedding encoder based on a multi-layer perceptron model is further used to fully connect the components of the flue gas to obtain a fully connected associated coding vector of the flue gas components. It should be understood that the multi-layer perceptron model has a powerful nonlinear modeling capability, which can capture the complex interactions and nonlinear relationships between the flue gas components, thereby better representing the chemical properties of the flue gas, which is crucial to understanding how different components jointly affect corrosion behavior.

[0037] Optionally, in one embodiment of the present application, the smoke components are fully connected encoded to obtain a smoke component fully connected associated encoding vector, including: using a smoke component embedding encoder based on a multi-layer perceptron model to fully connect the smoke components to obtain the smoke component fully connected associated encoding vector.

[0038] In the above-mentioned method for preparing the corrosion-resistant protective film on the surface of the copper smelting boiler tube, the S23 performs a feature compensation interactive analysis on the flue gas temperature time series correlation feature vector and the flue gas component fully connected correlation coding vector to obtain the deep interaction characteristics of the flue gas temperature and the flue gas component. It should be understood that the flue gas temperature time series correlation feature vector and the flue gas component fully connected correlation coding vector respectively contain the time series implicit correlation characteristics of the flue gas temperature and the complex nonlinear correlation feature information between the flue gas components, and both of these features have an important influence on the corrosion behavior of the boiler tube. In order to more accurately select the appropriate surface protection film material and preparation method, it is necessary to comprehensively consider the interaction effect of the flue gas temperature and the component. However, the flue gas temperature time series correlation feature vector and the flue gas component fully connected correlation coding vector are derived from different data sources and feature spaces respectively. Directly fusing these two feature vectors may result in information loss and inaccurate interaction results. Based on this, in the technical solution of the present application, the flue gas temperature time series correlation feature vector and the flue gas component fully connected correlation coding vector are subjected to a feature compensation interactive analysis to obtain the deep interaction characteristics of the flue gas temperature and the flue gas component. In particular, the processing of feature-compensated interactive analysis enables data from different sources (flue gas temperature time series features and fully connected correlation features of flue gas components) to be effectively compared and integrated in a unified space. At the same time, by introducing interactive enhancement technology, cross-feature patterns that are critical to the prediction of specific boiler tube corrosion behavior and the recommendation of surface protective film materials can be more effectively identified and utilized. In addition, feature-compensated interactive analysis can achieve efficient interaction across feature spaces, compensate for information loss in the feature mapping process, extract deep interactive features, and improve the overall performance and generalization ability of the model. This process provides a more accurate and comprehensive basis for selecting appropriate surface protective film materials and preparation methods, which helps to improve the corrosion resistance and service life of copper smelting boiler tubes.

[0039] Optionally, in one embodiment of the present application, Figure 4 In the method for preparing the corrosion-resistant protective film on the surface of the copper smelting boiler tube of the embodiment of the present application, the characteristic compensation interactive analysis is performed on the flue gas temperature time series correlation feature vector and the flue gas component fully connected correlation coding vector to obtain the schematic flow chart of the deep interaction feature of the flue gas temperature and the flue gas component. Figure 4As shown, the smoke temperature time series correlation feature vector and the smoke component fully connected correlation coding vector are subjected to feature compensation interactive analysis to obtain the deep interaction feature of smoke temperature and smoke component, including: S231, respectively performing co-space mapping and interpolation processing on the smoke temperature time series correlation feature vector and the smoke component fully connected correlation coding vector to obtain the modulated smoke temperature time series correlation feature vector, the modulated smoke component fully connected correlation coding vector, the interpolated smoke temperature time series correlation feature vector and the interpolated smoke component fully connected correlation coding vector; S232, the modulated smoke temperature time series correlation feature vector and the The interpolated smoke temperature time series correlation feature vector, the modulated smoke component fully connected correlation coding vector and the interpolated smoke component fully connected correlation coding vector are input into the feature mapping deviation semantic measurement network to obtain the smoke temperature time series mapping deviation semantic compensation factor and the smoke component mapping deviation semantic compensation factor; S233, based on the smoke temperature time series mapping deviation semantic compensation factor and the smoke component mapping deviation semantic compensation factor, the modulated smoke temperature time series correlation feature vector and the modulated smoke component fully connected correlation coding vector are compensated interactively encoded to obtain the smoke temperature-smoke component deep interaction feature.

[0040] Optionally, in one embodiment of the present application, the smoke temperature time series correlation feature vector and the smoke component fully connected correlation coding vector are respectively subjected to co-space mapping and interpolation processing to obtain the modulated smoke temperature time series correlation feature vector, the modulated smoke component fully connected correlation coding vector, the interpolated smoke temperature time series correlation feature vector and the interpolated smoke component fully connected correlation coding vector, including: inputting the smoke temperature time series correlation feature vector and the smoke component fully connected correlation coding vector into a co-space mapper based on a fully connected layer to obtain the modulated smoke temperature time series correlation feature vector and the modulated smoke component fully connected correlation coding vector, wherein, The time series associated feature vector of the modulated smoke temperature and the fully connected associated coding vector of the modulated smoke components have the same characteristic scale; the time series associated feature vector of the smoke temperature and the fully connected associated coding vector of the smoke components are interpolated to obtain the time series associated feature vector of the interpolated smoke temperature and the fully connected associated coding vector of the interpolated smoke components, wherein the time series associated feature vector of the interpolated smoke temperature and the time series associated feature vector of the modulated smoke temperature have the same characteristic scale, and the fully connected associated coding vector of the modulated smoke components and the fully connected associated coding vector of the interpolated smoke components have the same characteristic scale.

[0041] Optionally, in one embodiment of the present application, the modulated smoke temperature time series associated feature vector and the interpolated smoke temperature time series associated feature vector as well as the modulated smoke component fully connected associated coding vector and the interpolated smoke component fully connected associated coding vector are input into a feature mapping deviation semantic metric network to obtain a smoke temperature time series mapping deviation semantic compensation factor and a smoke component mapping deviation semantic compensation factor, including: calculating the positional division between the modulated smoke temperature time series associated feature vector and the interpolated smoke temperature time series associated feature vector to obtain a smoke temperature time series semantic association strength contribution vector; Calculate the logarithmic function value with base two of the absolute value of each eigenvalue in the smoke temperature time series semantic association strength contribution vector to obtain the logarithmic representation vector of the smoke temperature time series semantic association strength contribution; calculate the positional dot product between the modulated smoke temperature time series association eigenvector and the logarithmic representation vector of the smoke temperature time series semantic association strength contribution, and then sum the obtained dot product vector position by position to obtain the smoke temperature time series semantic association strength contribution value; calculate the natural exponential function value with the natural constant e as the base and the smoke temperature time series semantic association strength contribution value as the exponential power to obtain the smoke temperature time series mapping deviation semantic compensation factor.

[0042] Optionally, in one embodiment of the present application, the modulated smoke temperature temporal association feature vector and the modulated smoke component fully connected association coding vector are compensated and interactively encoded based on the smoke temperature temporal mapping deviation semantic compensation factor and the smoke component mapping deviation semantic compensation factor to obtain the smoke temperature-smoke component deep interaction feature, including: based on the smoke temperature temporal mapping deviation semantic compensation factor and the smoke component mapping deviation semantic compensation factor, the modulated smoke temperature temporal association feature vector and the modulated smoke component fully connected association coding vector are compensated to obtain the compensated smoke temperature temporal association feature vector and the compensated smoke component fully connected association coding vector; the compensated smoke temperature temporal association feature vector and the compensated smoke component fully connected association coding vector are input into the multi-dimensional significant interaction network between features to obtain the smoke temperature-smoke component deep interaction feature vector as the smoke temperature-smoke component deep interaction feature.

[0043] Optionally, in one embodiment of the present application, the modulated smoke temperature timing association feature vector and the modulated smoke component fully connected association coding vector are compensated based on the smoke temperature timing mapping deviation semantic compensation factor and the smoke component mapping deviation semantic compensation factor to obtain the compensated smoke temperature timing association feature vector and the compensated smoke component fully connected association coding vector, including: calculating the sum of the square of the smoke component mapping deviation semantic compensation factor and the smoke component mapping deviation semantic compensation factor to obtain the smoke component mapping deviation semantic compensation modulation factor; calculating the positional division between the modulated smoke temperature timing association feature vector and the smoke component mapping deviation semantic compensation modulation factor to obtain the compensated smoke temperature timing association feature vector.

[0044] Optionally, in one embodiment of the present application, the compensated smoke temperature time series correlation feature vector and the compensated smoke component fully connected correlation encoding vector are input into a multidimensional significant interaction network between features to obtain a smoke temperature-smoke component deep interaction feature vector as the smoke temperature-smoke component deep interaction feature, including: calculating the positional subtraction, positional dot multiplication, positional addition and positional division between the compensated smoke temperature time series correlation feature vector and the compensated smoke component fully connected correlation encoding vector to obtain a compensated smoke temperature-smoke component difference correlation feature vector, a compensated smoke temperature-smoke component deep interaction feature vector, and a calculated positional subtraction, positional dot multiplication, positional addition and positional division between the compensated smoke temperature time series correlation feature vector and the compensated smoke component fully connected correlation encoding vector. The compensated smoke temperature-smoke component difference associated feature vector, the compensated smoke temperature-smoke component dot product associated feature vector, and the compensated smoke temperature-smoke component dot division associated feature vector are added by position, and then processed through a one-dimensional convolution layer, and the sequence of obtained feature vectors is subjected to maximum pooling processing to obtain the smoke temperature-smoke component deep interaction feature vector.

[0045] In summary, in the embodiment of the present application, the smoke temperature time series correlation feature vector and the smoke component fully connected correlation coding vector are subjected to feature compensation interactive analysis using the following feature compensation interactive formula to obtain the smoke temperature-smoke component deep interaction feature;

[0046] Wherein, the feature compensation interaction formula includes:

[0047]

[0048]

[0049]

[0050]

[0051]

[0052]

[0053]

[0054]

[0055]

[0056] in, and are the smoke temperature time series correlation feature vector and the smoke component fully connected correlation coding vector, respectively. and are the flue gas temperature time series weight matrix and the flue gas temperature time series offset vector, respectively. and are the smoke component weight matrix and the smoke component bias vector respectively, for function, and are the time series correlation feature vector of smoke temperature after modulation and the fully connected correlation coding vector of smoke components after modulation, For interpolation processing, and are the time series correlation feature vector of smoke temperature after interpolation and the fully connected correlation coding vector of smoke components after interpolation, and are the characteristic values ​​of each position in the time series associated characteristic vector of the smoke temperature after modulation and the characteristic values ​​of each position in the fully connected associated coding vector of the smoke components after modulation, and are the characteristic values ​​of each position in the time series associated characteristic vector of the flue gas temperature after the interpolation processing and the characteristic values ​​of each position in the fully connected associated coding vector of the flue gas components after the interpolation processing, is the absolute value symbol, is the scale of the time series correlation feature vector of the flue gas temperature after modulation, is the value of the natural exponential function with the natural constant e as the base, and are the semantic compensation factor for smoke temperature time series mapping deviation and the semantic compensation factor for smoke component mapping deviation, respectively. and are the time series correlation feature vector of smoke temperature after compensation and the fully connected correlation coding vector of smoke components after compensation, , , and They are positional subtraction, positional dot multiplication, positional addition, and positional division. For feature fusion processing, is a one-dimensional convolution operation, For maximum pooling processing, is the deep interaction feature vector of the flue gas temperature and flue gas components.

[0057] Specifically, the processing process of feature compensation interaction analysis includes: modulating the flue gas temperature time series correlation feature vector and the flue gas component fully connected correlation encoding vector through the common space mapper of the fully connected layer, and being able to learn the nonlinear mapping relationship between the two feature vectors to map the two feature vectors into the same feature space, ensuring that they have the same feature scale before subsequent processing, thereby achieving semantic interaction across feature spaces. In addition, this step not only unifies the feature scale, but also reduces the noise in the original data and enhances the useful signal, providing a solid foundation for subsequent processing.

[0058] Next, considering that some of the original feature information may be lost when performing feature space mapping, this may lead to a decrease in the effectiveness of feature interaction analysis, because some subtle differences that are important for subsequent boiler tube corrosion behavior prediction and surface protective film material recommendation tasks may not be captured. At the same time, if there is a certain degree of correlation or covariance between the flue gas temperature time series correlation features and the flue gas component fully connected correlation coding features, after each independent spatial transformation, such an intrinsic connection may be destroyed, which will make subsequent attempts to mine the interaction pattern between them more difficult. Therefore, in the technical solution of the present application, after spatial mapping, it is necessary to compensate for the feature mapping deviation of the flue gas temperature time series correlation feature vector and the flue gas component fully connected correlation coding vector.

[0059] In order to compensate for the mapping deviation, the feature vectors before and after mapping (the smoke temperature time series associated feature vector and the smoke component fully connected associated coding vector) are first scaled by difference processing. It is worth mentioning that the interpolation process not only helps to adjust the resolution or density of the feature vector, but also may reveal the relationship between the original features that is not directly observed, which helps to improve the model's perception of subtle changes. Then, the feature vectors after modulation and interpolation are compared and analyzed through the feature mapping deviation semantic measurement network to identify which parts have been excessively or insufficiently modified, and the corresponding compensation factors are calculated accordingly to reflect how to adjust the two sets of feature vectors to better match, and quantify the degree of semantic deviation introduced in different transformation processes. Through interpolation processing and feature mapping deviation semantic measurement network, these information losses can be identified and compensated, ensuring that the smoke temperature time series associated features and the smoke component fully connected associated coding features retain more useful information during the interaction process.

[0060] Next, based on the semantic compensation factor of the smoke temperature time series mapping deviation and the semantic compensation factor of the smoke component mapping deviation, the modulated smoke temperature time series correlation feature vector and the modulated smoke component fully connected correlation encoding vector adjusted by the common space mapper are compensated and corrected. The operation at this stage is essentially a response to the previously detected deviation, and ultimately produces a more accurate and consistent compensated feature modulation vector. This mechanism helps to eliminate information distortion caused by data preprocessing and improve the sensitivity and stability of the model to subtle changes.

[0061] The last step is to send the compensated feature modulation vector into the multi-dimensional significant interaction network between features, which can explore and extract the deep connection between the two optimized flue gas temperature time series correlation features and the flue gas component fully connected correlation coding features. This helps to capture those patterns or structures that are crucial to the boiler tube corrosion behavior prediction and surface protection film material recommendation task objectives but are difficult to observe directly from a single feature vector, thereby improving the overall performance and generalization ability of the model.

[0062] In the above-mentioned method for preparing the corrosion-resistant protective film on the surface of the copper smelting boiler tube, the S24, inputs the deep interaction feature vector of the flue gas temperature-flue gas components into the surface protection film material recommender based on the first decoder to obtain the decoding result of the material of the surface protection film. That is to say, the deep interaction features between the flue gas temperature and the flue gas components are used to perform decoding regression, so as to recommend the material of the surface protection film. Optionally, in one embodiment of the present application, the surface protection film material recommender based on the first decoder is a decoder based on a neural network. The neural network-based decoder includes an input layer, several hidden layers and an output layer. The input layer receives the deep interaction feature vector of the flue gas temperature-flue gas components, the hidden layer uses the trained model weights for decoding regression, and the output layer outputs the material of the surface protection film. Optionally, in one embodiment of the present application, based on the working condition data of the flue gas, the material of the surface protection film and the method for making the surface protection film are determined, and it also includes: based on the decoding result of the material of the surface protection film, determining the method for making the surface protection film. In particular, here, the manufacturing method of the surface protective film includes plasma spray coating, flame spray coating, arc spray coating, hot melt coating, surface surfacing, etc. In this way, it is possible to recommend a suitable protective film material based on the actual working conditions of the flue gas, and give a corresponding manufacturing method based on the material, so as to realize a more intelligent preparation process of the corrosion-resistant protective film on the surface of the copper smelting boiler tube, which helps to cope with complex corrosive environments and extend the service life of the boiler tube.

[0063] Preferably, when the smoke temperature time series correlation feature vector and the smoke component fully connected correlation coding vector respectively represent the short-range-long-range bidirectional time series correlation feature of smoke temperature and the fully connected embedded coding of smoke components, when performing feature interaction based on feature mapping deviation compensation, the feature mapping deviation compensation interaction representation of the smoke temperature-smoke component deep interaction feature vector will also have an interactive regression understanding representation imbalance relative to the distribution field selective state space under the difference in feature extraction dimensions of heteromodal data, thereby deviating from the long-range feature representation regression understanding of the source data and affecting the accuracy of the decoding results.

[0064] Therefore, the present application considers that when the smoke temperature-smoke component deep interaction feature vector is input into the surface protection film material recommender based on the first decoder to obtain the decoding result of the material of the surface protection film, the smoke temperature-smoke component deep interaction feature vector is optimized, and the optimization process includes:

[0065] Determine the number of super-distributed eigenvalues ​​whose difference from the eigenmean in the deep interaction eigenvector of the smoke temperature-smoke component is greater than the threshold, and calculate the reciprocal of the logarithm of the number of super-distributed eigenvalues ​​with base two and the exponential value of the reciprocal of the number of super-distributed eigenvalues ​​with base natural constant to obtain the first smoke temperature-smoke component deep interaction nonlinear trajectory representation value and the second smoke temperature-smoke component deep interactive nonlinear trajectory representation value :

[0066]

[0067]

[0068]

[0069] in, represents the deep interaction feature vector of smoke temperature and smoke components, The first one represents the deep interaction feature vector of the smoke temperature and smoke components. eigenvalues, represents the characteristic mean of the deep interaction characteristic vector of the smoke temperature and smoke components, Representation calculation The number of is the number of super-distributed eigenvalues ​​in the smoke temperature-smoke composition deep interaction eigenvector, and is the threshold hyperparameter;

[0070] Calculate the hyperbolic sine function value of the sum of squares of all eigenvalues ​​of the smoke temperature-smoke component deep interaction eigenvector:

[0071]

[0072] in, represents the length of the deep interaction feature vector of smoke temperature and smoke components, represents the hyperbolic sine function, Represents the value of the hyperbolic sine function.

[0073] The exponential value of the hyperbolic sine function value with the natural constant as the base is calculated and then divided by the square of the length of the smoke temperature-smoke component deep interaction characteristic vector to obtain the smoke temperature-smoke component deep interaction potential manifold value ,in, represents a natural constant;

[0074] Calculate the potential manifold value of the deep interaction between the smoke temperature and the smoke components Relative to the first smoke temperature-smoke component deep interactive nonlinear trajectory representation value and the second smoke temperature-smoke component deep interactive nonlinear trajectory representation value The integrated value of the deep interaction covariance of flue gas temperature and flue gas components is:

[0075]

[0076] in, It represents the integrated value of the deep interaction covariance between smoke temperature and smoke components;

[0077] The power function eigenvector of the deep interaction eigenvector of the smoke temperature-smoke component is calculated with the integrated value of the deep interaction covariance of the smoke temperature-smoke component as the inverse, and the autocorrelation matrix of the power function eigenvector is calculated to obtain the deep interaction adaptive characterization matrix of the smoke temperature-smoke component, that is:

[0078]

[0079] in, represents a power function feature vector with the inverse of the integrated value of the deep interaction covariance of the smoke temperature and smoke components for calculating the deep interaction feature vector of the smoke temperature and smoke components, represents matrix multiplication, represents the transpose of a vector, Represents the smoke temperature-smoke component deep interactive adaptive representation matrix;

[0080] The smoke temperature-smoke component deep interaction feature vector is matrix-multiplied with the smoke temperature-smoke component deep interaction adaptive characterization matrix to obtain an optimized smoke temperature-smoke component deep interaction feature vector, that is:

[0081]

[0082] Here, the smoke temperature-smoke component deep interaction feature vector is a row vector, represents the optimized smoke temperature-smoke component deep interaction feature vector. Finally, the optimized smoke temperature-smoke component deep interaction feature vector is input into the surface protection film material recommender based on the first decoder to obtain the decoding result of the material of the surface protection film.

[0083] That is, for the problem of the lack of global adaptive mapping mechanism caused by cross-distribution field association caused by the attribute cluster of the deep interaction feature vector of the flue gas temperature-flue gas component under the preset parameter discrete arrangement, a multi-dimensional tensor field cell characterization framework is constructed by the mutual covariance coupling driving scheme of the deep interaction feature vector of the flue gas temperature-flue gas component, and the deep interaction law of its parameter space is revealed by analyzing the fully connected topological association composite mode. Therefore, by adopting the gradient-based multi-dimensional field implicit factor simulation method, a parameter space nonlinear association modeling system is established, so as to realize the topological reconstruction of the probabilistic trajectory evolution mode of the deep interaction feature vector of the flue gas temperature-flue gas component, and finally improve the accuracy of the decoding result of the material of the surface protective film obtained by the surface protective film material recommender based on the first decoder of the deep interaction feature vector of the flue gas temperature-flue gas component. In this way, it is possible to recommend a suitable protective film material based on the actual working conditions of the flue gas, and give a corresponding manufacturing method based on the material, so as to realize a more intelligent preparation process of the corrosion-resistant protective film on the surface of the copper smelting boiler tube.

[0084] Furthermore, the following more specific embodiments are provided for the above preparation method.

[0085] Embodiment 1: At the entrance of the boiler where the flue gas enters, the boiler tube is made of boiler steel. The surface coating thickness is selected to be 0.20mm-0.35mm. Its composition is a specific ceramic powder with a particle size less than 325 meshes, and its composition ratio range is: 80mol%-92mol% zirconium oxide, 8mol% or more yttrium oxide, 0.5mol%-2mol% nickel-chromium alloy powder, and the film making method is plasma spraying. Use position: flue gas enters the entrance of the boiler. The corrosion faced at this time is: silicates bonded to the surface of the boiler tube, long-term high temperature causes the metal lattice structure of the tube body to be thermally fatigued, non-conductive, and oxidized at high temperature. The characteristics of the selected surface film component are: first, there is no chemical reaction between it and the boiler tube metal; second, under high temperature conditions, the film structure is very stable; third, it has thermal conductivity; and fourth, it is resistant to oxidation.

[0086] Example 2: When the flue gas enters the middle of the boiler, the flue gas temperature is in the range of 500-900 degrees Celsius. The flue gas composition at this stage changes slightly. Compared with the composition just entering the boiler room, the proportion of low-valent non-thermal conductive and viscous silicate suspension is reduced, but those other harmful impurities have not changed. Because the proportion of silicate suspension is reduced, the concentration of these other harmful components is relatively increased. Therefore, for the boiler tube material at the manhole door, in this temperature range, the corrosion impact of these harmful impurities on the boiler tube will be relatively strong. The boiler tube material is boiler steel. The surface coating thickness is selected to be 0.20mm-0.35mm. Its composition is a specific alloy ceramic powder with a particle size less than 325 mesh, and its composition ratio range is: Cr3C2 + 22wt%-27wt%NiCr alloy powder. Film making method: supersonic flame spraying. Use location: the middle area of ​​the boiler, especially for the manhole door. The main corrosion resistance of flue gas with a temperature of 500-900 degrees Celsius is: chemical corrosion impact between harmful impurities and manhole door furnace tubes, and possible oxidation corrosion. The characteristics of the selected surface film components: first, cut off the direct contact between the boiler tube metal and harmful impurity elements, second, no chemical reaction occurs between the film material and the impurity elements, third, anti-oxidation impact, fourth, good thermal conductivity, no local overheating, and fifth, the film has adaptability to temperature fluctuations.

[0087] The equipment components used for coating in this application include a spray gun, a power supply, a powder delivery device, a cooler, an air supply system and a control cabinet, wherein the spray gun is a plasma generator, and the electricity, gas, powder, water, etc. of the entire operating system are concentrated in the spray gun; the power supply provides direct current to the spray gun, and the powder delivery device is a device that delivers alloy ceramic powder to the spray gun as required; the cooler is used for equipment cooling, because the arc temperature is very high, the spray gun can work stably and extend the service life of the nozzle only if it is well cooled; the air supply system ensures stable and continuous working gas and powder delivery gas; the control cabinet regulates and controls water, electricity, gas, and powder. The process parameters that affect the quality of the surface film layer in this scheme are mainly: 1. The composition distribution of ceramic powder or alloy ceramic powder must be uniform. For the powder material of the surface film layer, because it is a mixture of a specific proportion, it is different from a single phase ceramic powder or a single metal powder. Only when the particle size is matched and evenly distributed can it be adjusted and controlled through relevant parameters, and the film layer can avoid segregation and be dense and firm. 2. Carrier gas: The core temperature is very high. The carrier gas flow has a certain enthalpy and flow rate. The change of the enthalpy and flow rate determines the spraying efficiency and coating density. 3. Powder feeding: The powder feeding speed must be guaranteed and it must be delivered to the core of the carrier gas flame to obtain the best heating and spraying speed. 4. The distance between the nozzle and the substrate component is 11.5cm+ / -0.10cm. Different materials have different spraying distances between the nozzle and the workpiece substrate. Under the premise of ensuring the coating quality and the substrate temperature, the distance from the nozzle to the substrate should be slightly smaller. 5. Spraying angle: The spraying angle is 89 degrees + / -0.5 degrees to avoid the "shadow effect" that may cause the protective film layer to loosen. 6. The movement speed of the spray gun relative to the substrate. Controlling the movement speed of the spray gun can achieve good superposition and uniform thickness between the spray widths of a single stroke, and can also prevent oxidation caused by local hot spots. 7. Substrate temperature control: The substrate temperature is controlled at 180ºC+ / -2.5ºC. Keeping this temperature stable can ensure a stable curing speed and avoid different density differences due to changes in the thickness of the protective film.

[0088] In summary, according to the embodiment of the present application, the method for preparing the corrosion-resistant protective film on the surface of the copper smelting boiler tube is explained, which includes: obtaining the working condition data of the flue gas entering the boiler from the reactor; determining the material of the surface protective film and the method for making the surface protective film based on the working condition data of the flue gas; performing surface pretreatment on the boiler inlet to obtain the boiler inlet after surface pretreatment; applying a transition layer to the boiler inlet after the surface pretreatment; forming the surface protective film on the transition layer by the method for making the surface protective film. In this way, it is possible to recommend a suitable protective film material based on the actual working condition of the flue gas, and give a corresponding method for making the film based on the material, so as to realize a more intelligent preparation process of the corrosion-resistant protective film on the surface of the copper smelting boiler tube, which helps to cope with complex corrosive environments and extend the service life of boiler tubes.

[0089] The basic principle of the present invention is described above in conjunction with specific embodiments. However, it should be pointed out that the advantages, strengths, effects, etc. mentioned in the present invention are only examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. must be possessed by each embodiment of the present invention. In addition, the specific details of the above embodiments are only for the purpose of illustration and facilitation of understanding, rather than limitation, and the above details do not limit the present invention to being implemented by adopting the above specific details.

[0090] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, please refer to the relevant description of other embodiments. In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the module division is only a logical function division, and there may be other division methods in actual implementation. The modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0091] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0092] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference to a figure in a claim should not be considered as limiting the claim to which it relates.

[0093] In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units stated in the device claim can also be implemented by one unit through software or hardware.

[0094] Finally, it should be noted that the above description has been given for the purpose of illustration and description. In addition, the above embodiments are only used to illustrate the technical solution of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for preparing a corrosion-resistant protective film on the surface of a copper smelting boiler tube, characterized in that: include: Acquire the operating data of the flue gas entering the boiler from the reaction furnace, wherein the operating data of the flue gas includes the time sequence of the flue gas temperature and the components of the flue gas; Determining the material of the surface protection film and the method for manufacturing the surface protection film based on the working condition data of the flue gas; Performing surface pretreatment on the boiler inlet to obtain a surface pretreated boiler inlet; Applying a transition layer to the boiler inlet after the surface pretreatment; The surface protection film is formed on the transition layer by the surface protection film manufacturing method.

2. The method for preparing the corrosion-resistant protective film on the surface of the copper smelting boiler tube according to claim 1, characterized in that: Based on the working condition data of the flue gas, the material of the surface protection film and the method for manufacturing the surface protection film are determined, including: Performing time series encoding on the time queue of the flue gas temperature to obtain a flue gas temperature time series correlation feature vector; Performing full connection encoding on the smoke components to obtain a smoke component full connection association encoding vector; The smoke temperature time series correlation feature vector and the smoke component fully connected correlation coding vector are subjected to feature compensation interactive analysis to obtain the deep interaction feature of smoke temperature and smoke component, including: respectively performing co-space mapping and interpolation processing on the smoke temperature time series correlation feature vector and the smoke component fully connected correlation coding vector to obtain the modulated smoke temperature time series correlation feature vector, the modulated smoke component fully connected correlation coding vector, the interpolated smoke temperature time series correlation feature vector and the interpolated smoke component fully connected correlation coding vector; The processed smoke temperature time series associated feature vector, the modulated smoke component fully connected associated coding vector, and the interpolated smoke component fully connected associated coding vector are input into a feature mapping deviation semantic measurement network to obtain a smoke temperature time series mapping deviation semantic compensation factor and a smoke component mapping deviation semantic compensation factor; based on the smoke temperature time series mapping deviation semantic compensation factor and the smoke component mapping deviation semantic compensation factor, the modulated smoke temperature time series associated feature vector and the modulated smoke component fully connected associated coding vector are compensated interactively encoded to obtain the smoke temperature-smoke component deep interaction feature; The smoke temperature-smoke component deep interaction feature is input into a surface protection film material recommender based on the first decoder to obtain a decoding result of the material of the surface protection film.

3. The method for preparing the corrosion-resistant protective film on the surface of the copper smelting boiler tube according to claim 2, characterized in that: The time sequence of the flue gas temperature is time-series encoded to obtain a flue gas temperature time-series associated feature vector, including: inputting the time sequence of the flue gas temperature into a flue gas temperature time-series encoder based on a forward LSTM model to obtain the flue gas temperature time-series associated feature vector.

4. The method for preparing the corrosion-resistant protective film on the surface of the copper smelting boiler tube according to claim 3, characterized in that: The smoke components are fully connected encoded to obtain a smoke component fully connected associated encoding vector, including: using a smoke component embedding encoder based on a multi-layer perceptron model to fully connect the smoke components to obtain the smoke component fully connected associated encoding vector.

5. The method for preparing the corrosion-resistant protective film on the surface of copper smelting boiler tube according to claim 4, characterized in that: The smoke temperature time series correlation feature vector and the smoke component fully connected correlation coding vector are respectively subjected to co-space mapping and interpolation processing to obtain a modulated smoke temperature time series correlation feature vector, a modulated smoke component fully connected correlation coding vector, an interpolated smoke temperature time series correlation feature vector, and an interpolated smoke component fully connected correlation coding vector, including: Inputting the smoke temperature time series correlation feature vector and the smoke component fully connected correlation coding vector into a co-space mapper based on a fully connected layer to obtain the modulated smoke temperature time series correlation feature vector and the modulated smoke component fully connected correlation coding vector, wherein the modulated smoke temperature time series correlation feature vector and the modulated smoke component fully connected correlation coding vector have the same characteristic scale; The smoke temperature time series associated feature vector and the smoke component fully connected associated coding vector are interpolated to obtain the smoke temperature time series associated feature vector after interpolation and the smoke component fully connected associated coding vector after interpolation, wherein the smoke temperature time series associated feature vector after interpolation has the same characteristic scale as the smoke temperature time series associated feature vector after modulation, and the smoke component fully connected associated coding vector after modulation has the same characteristic scale as the smoke component fully connected associated coding vector after interpolation.

6. The method for preparing the corrosion-resistant protective film on the surface of copper smelting boiler tube according to claim 5, characterized in that: Inputting the modulated smoke temperature time series associated feature vector and the interpolated smoke temperature time series associated feature vector as well as the modulated smoke component fully connected associated coding vector and the interpolated smoke component fully connected associated coding vector into a feature mapping deviation semantic metric network to obtain a smoke temperature time series mapping deviation semantic compensation factor and a smoke component mapping deviation semantic compensation factor, including: Calculating the positional division between the modulated smoke temperature time series associated feature vector and the interpolated smoke temperature time series associated feature vector to obtain a smoke temperature time series semantic association strength contribution vector; Calculating the logarithmic function value with base 2 of the absolute value of each eigenvalue in the flue gas temperature time series semantic association strength contribution vector to obtain a logarithmic representation vector of the flue gas temperature time series semantic association strength contribution; After calculating the position-by-position dot product between the modulated flue gas temperature time series association feature vector and the logarithmic representation vector of the flue gas temperature time series semantic association strength contribution, the obtained dot product vectors are summed position by position to obtain the flue gas temperature time series semantic association strength contribution value; A natural exponential function value with a natural constant e as the base and the contribution value of the flue gas temperature time series semantic association strength as the exponential power is calculated to obtain the flue gas temperature time series mapping deviation semantic compensation factor.

7. The method for preparing the corrosion-resistant protective film on the surface of copper smelting boiler tube according to claim 6, characterized in that: Based on the smoke temperature time series mapping deviation semantic compensation factor and the smoke component mapping deviation semantic compensation factor, the modulated smoke temperature time series correlation feature vector and the modulated smoke component fully connected correlation coding vector are compensated interactively encoded to obtain the smoke temperature-smoke component deep interaction feature, including: Based on the smoke temperature time series mapping deviation semantic compensation factor and the smoke component mapping deviation semantic compensation factor, the modulated smoke temperature time series correlation feature vector and the modulated smoke component fully connected correlation coding vector are compensated to obtain a compensated smoke temperature time series correlation feature vector and a compensated smoke component fully connected correlation coding vector; The compensated smoke temperature time series correlation feature vector and the compensated smoke component fully connected correlation encoding vector are input into the feature-to-feature multidimensional significant interaction network to obtain the smoke temperature-smoke component deep interaction feature vector as the smoke temperature-smoke component deep interaction feature.

8. The method for preparing the corrosion-resistant protective film on the surface of copper smelting boiler tube according to claim 7, characterized in that: The method of compensating the modulated smoke temperature time series associated feature vector and the modulated smoke component fully connected associated coding vector based on the smoke temperature time series mapping deviation semantic compensation factor and the smoke component mapping deviation semantic compensation factor to obtain a compensated smoke temperature time series associated feature vector and a compensated smoke component fully connected associated coding vector, including: Calculating the sum of the square of the smoke component mapping deviation semantic compensation factor and the smoke component mapping deviation semantic compensation factor to obtain a smoke component mapping deviation semantic compensation modulation factor; The positional division between the modulated smoke temperature time series associated feature vector and the smoke component mapping deviation semantic compensation modulation factor is calculated to obtain the compensated smoke temperature time series associated feature vector.

9. The method for preparing the corrosion-resistant protective film on the surface of the copper smelting boiler tube according to claim 8, characterized in that: Inputting the compensated smoke temperature time series correlation feature vector and the compensated smoke component fully connected correlation encoding vector into a multi-dimensional significant interaction network between features to obtain a smoke temperature-smoke component deep interaction feature vector as the smoke temperature-smoke component deep interaction feature, including: Calculate the positional subtraction, positional dot multiplication, positional addition and positional division between the compensated smoke temperature time series associated feature vector and the compensated smoke component fully connected associated coding vector to obtain a compensated smoke temperature-smoke component difference associated feature vector, a compensated smoke temperature-smoke component dot multiplication associated feature vector, a compensated smoke temperature-smoke component sum associated feature vector and a compensated smoke temperature-smoke component dot division associated feature vector; The compensated smoke temperature-smoke component difference associated feature vector, the compensated smoke temperature-smoke component dot product associated feature vector, the compensated smoke temperature-smoke component sum associated feature vector and the compensated smoke temperature-smoke component dot division associated feature vector are added by position and then processed through a one-dimensional convolution layer, and the sequence of obtained feature vectors is subjected to maximum pooling processing to obtain the smoke temperature-smoke component deep interaction feature vector.

10. The method for preparing the corrosion-resistant protective film on the surface of copper smelting boiler tube according to claim 9, characterized in that: Based on the working condition data of the flue gas, the material of the surface protection film and the manufacturing method of the surface protection film are determined, and the manufacturing method of the surface protection film is determined based on the decoding result of the material of the surface protection film.

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