Preparation method of biomass-based activated carbon catalytic material

Abstract

The invention relates to the technical field of carbon-based flue gas purification catalysts, and particularly discloses a preparation method of a biomass-based activated carbon catalyst carrier material, which comprises the following steps: respectively acquiring product carbonization images at two different moments by using a camera, and processing the product carbonization images by using an image processing technology based on deep learning to obtain the biomass-based activated carbon catalyst carrier material. The method comprises the following steps: respectively extracting product carbonization characteristics in two images, intelligently estimating a product carbonization degree coefficient based on a product carbonization characteristic difference between the two images, and then further calculating the time still required for complete carbonization of the product by combining a time interval between two time points and the product carbonization degree coefficient. Therefore, by monitoring and accurately controlling the carbonization process in real time, the problem of non-uniform carbonization or excessive carbonization in the traditional process can be effectively avoided, so that the performance and the stability of the catalyst are improved.

Description

Technical Field

[0001] The present application relates to the technical field of carbon-based flue gas purification catalysts, and more specifically, to a method for preparing a biomass-based activated carbon catalyst carrier material. Background Art

[0002] Nitrogen oxides (NOx) are one of the main components of air pollution. Their emissions not only cause environmental problems such as acid rain and photochemical smog, but also have serious impacts on human health. With the increasing awareness of environmental protection and increasingly stringent emission standards, denitrification catalysts, as key materials for reducing nitrogen oxide emissions, have become a hot topic in current research, with their performance optimization and preparation technology innovation. Among the many denitrification catalysts, biomass-based activated carbon has become an important direction for the development of efficient denitrification catalysts due to its wide range of sources, renewable nature, and environmental friendliness.

[0003] The invention patent with publication number CN110433788A discloses a denitration catalyst with biomass-based activated carbon as a carrier and a preparation method thereof. The catalyst uses biomass-based activated carbon as a substrate, crushes the biomass raw materials, washes them with water, and dries them before high-temperature carbonization and chemical activation treatment, so that the catalyst has good denitration performance, achieving the purpose of saving resources and protecting the environment. Among them, the carbonization process is a key step in the preparation of biomass-based activated carbon, which directly affects the performance of the final catalyst. However, the traditional carbonization process usually relies on time parameters set by experience, lacks real-time monitoring and precise control of the degree of carbonization, and easily leads to uneven carbonization or excessive carbonization, thereby affecting the performance of the catalyst.

[0004] Therefore, an optimized method for preparing biomass-based activated carbon catalytic materials is desired. Summary of the invention

[0005] In order to solve the above technical problems, the present application is proposed. The embodiment of the present application provides an optimized method for preparing biomass-based activated carbon catalytic materials, which uses a camera to collect product carbonization images at two different times, and uses deep learning-based image processing technology to process the product carbonization images, respectively extract the product carbonization characteristics in the two images, and then based on the difference in product carbonization characteristics between the two images, intelligently estimate the product carbonization degree coefficient, and then further combine the time interval between the two time points and the product carbonization degree coefficient to calculate the time required for the product to be completely carbonized. In this way, by real-time monitoring and precise control of the carbonization process, the problems of uneven carbonization or excessive carbonization in traditional processes can be effectively avoided, thereby improving the performance and stability of the catalyst.

[0006] Accordingly, according to one aspect of the present application, a method for preparing a biomass-based activated carbon catalytic material is provided, comprising:

[0007] S1: Take the biomass raw material, crush it, wash it with water, and then dry it at 105℃ for 2h;

[0008] S2: The dried product is taken out, N2 is used as a protective gas, and the temperature is increased to 500-800°C at a heating rate of 10°C / min for carbonization treatment, and a biomass-based activated carbon precursor is obtained after 1-2 hours;

[0009] S3: immersing the biomass-based activated carbon precursor in a saturated KOH solution for 2 hours;

[0010] S4: taking out the impregnated product and heating it to 400-800°C for activation for 0.5-2h to obtain biomass-based activated carbon;

[0011] S5: washing the activated biomass-based activated carbon with a 1-2M hydrochloric acid solution, then rinsing with deionized water until neutral, and then drying to a constant weight to obtain biomass-based activated carbon powder particles;

[0012] S6: The biomass-based activated carbon powder particles and the binder are mixed evenly in a weight ratio of 100:(0.5-1.5), and then kneaded into a shape to obtain a biomass-based activated carbon catalytic material carrier.

[0013] In the above-mentioned method for preparing biomass-based activated carbon catalytic materials, the step S2 includes: collecting product carbonization images through a camera at a first time point and a second time point respectively to obtain a first product carbonization image and a second product carbonization image; determining the product carbonization time based on the first time point and the second time point; extracting the product carbonization characteristics of the first product carbonization image and the second product carbonization image respectively to obtain a first product carbonization coding feature map and a second product carbonization coding feature map; performing spatial constraint enhancement processing based on pixel granularity on the first product carbonization coding feature map and the second product carbonization coding feature map respectively to obtain a first product carbonization spatial enhancement coding feature map and a second product carbonization spatial enhancement coding feature map; generating a product carbonization degree coefficient based on the feature difference between the first product carbonization spatial enhancement coding feature map and the second product carbonization spatial enhancement coding feature map; and predicting the time required for complete carbonization of the product based on the product carbonization degree coefficient and the product carbonization time.

[0014] In the above-mentioned method for preparing biomass-based activated carbon catalytic material, the product carbonization characteristics of the first product carbonization image and the second product carbonization image are extracted respectively to obtain a first product carbonization coding feature map and a second product carbonization coding feature map, including: passing the first product carbonization image and the second product carbonization image through a product carbonization feature extractor based on a deep separable convolutional neural network model to obtain the first product carbonization coding feature map and the second product carbonization coding feature map.

[0015] In the above-mentioned method for preparing biomass-based activated carbon catalytic material, the first product carbonization coding feature map and the second product carbonization coding feature map are respectively subjected to pixel granularity-based spatial constraint enhancement processing to obtain a first product carbonization spatial enhancement coding feature map and a second product carbonization spatial enhancement coding feature map, including: feature dispersion of the first product carbonization coding feature map along the channel dimension to obtain a set of first product carbonization pixel granularity feature vectors; calculating the semantic association score between any two first product carbonization pixel granularity feature vectors in the set of the first product carbonization pixel granularity feature vectors to obtain a first product carbonization pixel granularity semantic association score topological matrix; based on the pixel spatial position information of the first product carbonization coding feature map, spatial attention attenuation modulation is performed on the first product carbonization pixel granularity semantic association score topological matrix to obtain a first product carbonization semantic association space soft constraint score topological feature matrix; based on the first product carbonization semantic association space soft constraint score topological feature matrix, the first product carbonization coding feature map is subjected to pixel granularity spatial enhancement processing to obtain the first product carbonization space enhancement coding feature map.

[0016] In the above-mentioned method for preparing biomass-based activated carbon catalytic material, the semantic association score between any two first product carbonization pixel particle size feature vectors in the set of the first product carbonization pixel particle size feature vectors is calculated to obtain a first product carbonization pixel particle size semantic association score topological matrix, including: performing nonlinear mapping on each first product carbonization pixel particle size feature vector in the set of the first product carbonization pixel particle size feature vectors to obtain a set of mapped first product carbonization pixel particle size feature vectors; calculating the Poincare distance between any two mapped first product carbonization pixel particle size feature vectors in the set of mapped first product carbonization pixel particle size feature vectors as the semantic association score to obtain the first product carbonization pixel particle size semantic association score topological matrix.

[0017] In the above-mentioned method for preparing biomass-based activated carbon catalytic materials, based on the pixel spatial position information of the first product carbonization coding feature map, the first product carbonization pixel granularity semantic association score topological matrix is ​​spatially attended to and attenuated modulated to obtain the first product carbonization semantic association space soft constraint score topological feature matrix, including: based on the spatial distance between any two first product carbonization pixel granularity feature vectors in the set of the first product carbonization pixel granularity feature vectors, the first product carbonization pixel granularity semantic association score topological matrix is ​​spatially attended to and attenuated modulated to obtain the first product carbonization semantic association space soft constraint score topological matrix; the first product carbonization semantic association space soft constraint score topological matrix is ​​void convolutionally encoded to obtain the first product carbonization semantic association space soft constraint score topological feature matrix.

[0018] In the preparation method of the above-mentioned biomass-based activated carbon catalytic material, the first product carbonization pixel granularity semantic association score topological matrix is ​​spatially focused on attenuation modulation based on the spatial distance between any two first product carbonization pixel granularity feature vectors in the set of the first product carbonization pixel granularity feature vectors to obtain the first product carbonization semantic association spatial soft constraint score topological matrix, including: calculating the natural exponential function value of the Euclidean distance between any two first product carbonization pixel granularity feature vectors in the set of the first product carbonization pixel granularity feature vectors to obtain the first product carbonization pixel granularity spatial distance topological matrix; multiplying the first product carbonization pixel granularity spatial distance topological matrix by a preset attenuation modulation constant to obtain a spatial attention attenuation modulation matrix, and calculating the first product carbonization pixel granularity semantic association score topological matrix and the spatial attention attenuation modulation matrix divided by the position point to obtain the first product carbonization semantic association spatial soft constraint score topological matrix.

[0019] In the above-mentioned method for preparing biomass-based activated carbon catalytic materials, based on the soft constraint score topological feature matrix of the first product carbonization semantic association space, the first product carbonization coding feature map is subjected to pixel granularity space enhancement processing to obtain the first product carbonization space enhanced coding feature map, including: inputting the set of the first product carbonization pixel granularity feature vectors and the first product carbonization semantic association space soft constraint score topological feature matrix into the graph convolutional coding module to obtain a set of context-related enhanced first product carbonization pixel granularity feature vectors; and performing feature shape reshaping on the set of context-related enhanced first product carbonization pixel granularity feature vectors to obtain the first product carbonization space enhanced coding feature map.

[0020] In the above-mentioned method for preparing biomass-based activated carbon catalytic materials, a product carbonization degree coefficient is generated based on the characteristic difference between the first product carbonization space enhancement coding characteristic map and the second product carbonization space enhancement coding characteristic map, including: inputting the first product carbonization space enhancement coding characteristic map and the second product carbonization space enhancement coding characteristic map into a product carbonization degree measurement network to obtain a product carbonization degree change coding characteristic map; inputting the product carbonization degree change coding characteristic map into a decoder-based product carbonization degree estimation module to obtain the product carbonization degree coefficient.

[0021] In the above-mentioned method for preparing biomass-based activated carbon catalytic material, the time required for complete carbonization of the product is predicted based on the product carbonization degree coefficient and the product carbonization time, including: dividing the product carbonization degree coefficient by the product carbonization time to obtain the time required for complete carbonization of the product.

[0022] According to another aspect of the present application, a biomass-based activated carbon catalytic material is provided, which is prepared by the aforementioned method for preparing the biomass-based activated carbon catalytic material.

[0023] Compared with the prior art, the biomass-based activated carbon catalytic material and its preparation method provided by the present application collects product carbonization images at two different times through a camera, and uses image processing technology based on deep learning to process the product carbonization images, respectively extracts the product carbonization characteristics in the two images, and then based on the difference in product carbonization characteristics between the two images, intelligently estimates the product carbonization degree coefficient, and then further combines the time interval between the two time points and the product carbonization degree coefficient to deduce the time required for the product to be completely carbonized. In this way, by real-time monitoring and precise control of the carbonization process, the problems of uneven carbonization or excessive carbonization in traditional processes can be effectively avoided, thereby improving the performance and stability of the catalyst. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] By describing the embodiments of the present application in more detail in conjunction with the accompanying drawings, the above and other purposes, features and advantages of the present application will become more apparent. The accompanying drawings are used to provide a further understanding of the embodiments of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings, the same reference numerals generally represent the same components or steps.

[0025] Figure 1 The present invention is a flow chart of the carbonization process in the method for preparing biomass-based activated carbon catalytic material according to an embodiment of the present application.

[0026] Figure 2 It is a data flow diagram of the carbonization process in the method for preparing biomass-based activated carbon catalytic material according to an embodiment of the present application.

[0027] Figure 3 It is a flow chart of sub-step S24 of the carbonization process in the method for preparing biomass-based activated carbon catalytic material according to an embodiment of the present application.

[0028] Figure 4 This is a flow chart of sub-step S243 of the carbonization process in the method for preparing a biomass-based activated carbon catalytic material according to an embodiment of the present application.

[0029] Figure 5 It is a flow chart of sub-step S25 of the carbonization process in the method for preparing biomass-based activated carbon catalytic material according to an embodiment of the present application. DETAILED DESCRIPTION

[0030] As shown in this application and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0031] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules can be used and run on the user terminal and / or server. The modules are only illustrative, and different aspects of the system and method can use different modules.

[0032] Flowcharts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps may be processed in reverse order or simultaneously as required. Meanwhile, other operations may also be added to these processes, or a certain step or several steps of operations may be removed from these processes.

[0033] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0034] The present application proposes a method for preparing a biomass-based activated carbon catalytic material, comprising: S1: drying a biomass raw material at 105°C for 2 hours; S2: taking out the dried product, using N2 as a protective gas, and heating it to 500-800°C at a heating rate of 10°C / min for carbonization treatment, and obtaining a biomass-based activated carbon precursor after 1-4 hours; S3: immersing the biomass-based activated carbon precursor in a saturated KOH solution for 2 hours; S4: immersing the immersion product in a saturated KOH solution for 2 hours. The product is taken out and heated to 400-800°C for activation for 0.5-2h to obtain biomass-based activated carbon; S5: The activated biomass-based activated carbon is washed with 1-2M hydrochloric acid solution, then rinsed with deionized water until neutral, and then dried to constant weight to obtain biomass-based activated carbon powder particles; S6: The biomass-based activated carbon powder particles and the binder are mixed evenly at a weight ratio of 100:(0.5-1.5) and kneaded to obtain a biomass-based activated carbon catalytic material carrier. .

[0035] In this preparation process, the carbonization process is a key step in the preparation of biomass-based activated carbon, which directly affects the performance of the final catalyst. However, the traditional carbonization process usually relies on the time parameters set by experience, lacks real-time monitoring and precise control of the degree of carbonization, and easily leads to uneven carbonization or excessive carbonization, thereby affecting the performance of the catalyst. In view of the above technical problems, the present application proposes an optimized method for preparing biomass-based activated carbon catalytic materials, which collects product carbonization images at two different times through a camera, and uses image processing technology based on deep learning to process the product carbonization images, respectively extracts the product carbonization features in the two images, and then based on the difference in product carbonization features between the two images, intelligently estimates the product carbonization degree coefficient, and then further combines the time interval between the two time points and the product carbonization degree coefficient to deduce the time required for the product to be completely carbonized. In this way, by real-time monitoring and precise control of the carbonization process, the problems of uneven carbonization or excessive carbonization in the traditional process can be effectively avoided, thereby improving the performance and stability of the catalyst.

[0036] Figure 1 The present invention is a flow chart of the carbonization process in the method for preparing biomass-based activated carbon catalytic material according to an embodiment of the present application. Figure 2 Schematic diagram of data flow of the carbonization process in the method for preparing biomass-based activated carbon catalytic material according to an embodiment of the present application. Figure 1 and Figure 2As shown, according to the preparation method of the biomass-based activated carbon catalytic material of the embodiment of the present application, the steps include: S21, collecting product carbonization images through a camera at a first time point and a second time point to obtain a first product carbonization image and a second product carbonization image; S22, determining the product carbonization time based on the first time point and the second time point; S23, extracting the product carbonization characteristics of the first product carbonization image and the second product carbonization image respectively to obtain a first product carbonization coding feature map and a second product carbonization coding feature map; S24, performing spatial constraint enhancement processing based on pixel granularity on the first product carbonization coding feature map and the second product carbonization coding feature map respectively to obtain a first product carbonization spatial enhancement coding feature map and a second product carbonization spatial enhancement coding feature map; S25, generating a product carbonization degree coefficient based on the feature difference between the first product carbonization spatial enhancement coding feature map and the second product carbonization spatial enhancement coding feature map; S26, predicting the time required for complete carbonization of the product based on the product carbonization degree coefficient and the product carbonization time.

[0037] In the above-mentioned method for preparing biomass-based activated carbon catalytic materials, the step S21, respectively, collects product carbonization images through a camera at a first time point and a second time point to obtain a first product carbonization image and a second product carbonization image. It should be understood that during the carbonization process, the product has a carbonization rate at different carbonization stages. For example, in the initial stage of carbonization, the carbonization rate is faster, and the carbonization rate will drop significantly when it is close to complete carbonization. Therefore, based on the consideration of the change in carbonization rate, in the technical solution of the present application, by comparing and analyzing the product carbonization images at two different time points, the carbonization degree of the product can be effectively estimated.

[0038] In the above-mentioned method for preparing biomass-based activated carbon catalytic materials, the step S22 determines the carbonization time of the product based on the first time point and the second time point. It should be understood that since the carbonization rate refers to the change in the degree of carbonization of the product per unit time, in order to more accurately understand the carbonization speed of the product, the present application further determines the carbonization time of the product based on the time interval between the first time point and the second time point, thereby providing an important basis for predicting the time required for complete carbonization of the product. In a specific example of the present application, the second time point is the current time point, and the first time point is any time point before the current time point.

[0039] In the above-mentioned method for preparing biomass-based activated carbon catalytic materials, the step S23 extracts the product carbonization features of the first product carbonization image and the second product carbonization image respectively to obtain the first product carbonization coding feature map and the second product carbonization coding feature map. In a specific example of the present application, the step S23 includes: passing the first product carbonization image and the second product carbonization image through a product carbonization feature extractor based on a deep separable convolutional neural network model to obtain the first product carbonization coding feature map and the second product carbonization coding feature map. That is, in order to extract the carbonization features in the first product carbonization image and the second product carbonization image respectively, so as to facilitate the subsequent carbonization degree estimation, the present application adopts a deep separable convolutional neural network model as a product carbonization feature extractor, and by performing deep convolution and point-by-point convolution operations on the first product carbonization image and the second product carbonization image respectively, it is possible to efficiently extract the carbonization information such as the color, texture and shape of the product in the image, thereby obtaining the first product carbonization coding feature map and the second product carbonization coding feature map. It should be understood that the deep separable convolutional neural network effectively reduces the computational complexity of the model by decomposing the standard convolution operation into two steps: deep convolution and point-by-point convolution, while retaining the depth and breadth of feature extraction. Specifically, in the deep convolution stage, the network focuses on learning the independent spatial features of each feature channel of the image, while in the point-by-point convolution stage, the network integrates features across channels by applying 1x1 convolution kernels to each feature channel, thereby achieving a balance between the depth and breadth of the features. In this way, the deep separable convolutional neural network model can effectively capture subtle changes in the carbonization process and provide accurate feature support for the subsequent generation of the carbonization degree coefficient.

[0040] In the above-mentioned method for preparing biomass-based activated carbon catalytic materials, in step S24, the first product carbonization coding characteristic map and the second product carbonization coding characteristic map are respectively subjected to spatial constraint enhancement processing based on pixel granularity to obtain the first product carbonization spatial enhancement coding characteristic map and the second product carbonization spatial enhancement coding characteristic map. It should be understood that, considering that there may be some redundant information or irrelevant features in the first product carbonization coding characteristic map and the second product carbonization coding characteristic map, in order to further enhance the distinguishability of features to improve the accuracy of carbonization degree estimation, the present application proposes a feature enhancement method, which performs spatial constraint enhancement processing based on pixel granularity on the first product carbonization coding characteristic map and the second product carbonization coding characteristic map, respectively, to highlight the features closely related to the characterization of the degree of carbonization, while suppressing the influence of irrelevant or noise features, thereby avoiding misjudgment caused by mutations of local features and improving the sensitivity of the model to key information.

[0041] Figure 3FIG. 2 is a flow chart of sub-step S24 of the carbonization process in the method for preparing biomass-based activated carbon catalytic material according to an embodiment of the present application. Figure 3 As shown, the step S24 includes the steps of: S241, performing feature dispersion on the first product carbonization coding feature map along the channel dimension to obtain a set of first product carbonization pixel granularity feature vectors; S242, calculating the semantic association score between any two first product carbonization pixel granularity feature vectors in the set of the first product carbonization pixel granularity feature vectors to obtain a first product carbonization pixel granularity semantic association score topological matrix; S243, based on the pixel spatial position information of the first product carbonization coding feature map, performing spatial attention attenuation modulation on the first product carbonization pixel granularity semantic association score topological matrix to obtain a first product carbonization semantic association space soft constraint score topological feature matrix; S244, based on the first product carbonization semantic association space soft constraint score topological feature matrix, performing pixel granularity spatial enhancement processing on the first product carbonization coding feature map to obtain the first product carbonization space enhanced coding feature map.

[0042] In a specific example of the present application, the step S242 includes: performing nonlinear mapping on each first product carbonization pixel granularity feature vector in the set of the first product carbonization pixel granularity feature vector to obtain a set of mapped first product carbonization pixel granularity feature vectors; calculating the Poincare distance between any two mapped first product carbonization pixel granularity feature vectors in the set of mapped first product carbonization pixel granularity feature vectors as the semantic association score to obtain the first product carbonization pixel granularity semantic association score topological matrix. That is, by calculating the semantic association score between any two product carbonization pixel granularity feature vectors in the set, a product carbonization pixel granularity semantic association score topological matrix is ​​constructed, thereby revealing the intrinsic contextual semantic association of the image. In particular, in this process, considering that compared with Euclidean space, hyperbolic space can more effectively capture the nonlinear relationship between data, the present application first maps each product carbonization pixel granularity feature vector from Euclidean space to Poincare space, so that it adapts to the geometric characteristics of hyperbolic space while maintaining the original semantic information, and calculates the Poincare distance between any two mapped product carbonization pixel granularity feature vectors as the semantic association score between the two, so as to utilize the geometric characteristics of Poincare space to enhance the nonlinear association between features, improve the model's ability to distinguish features of different categories, and thus more accurately simulate the intrinsic semantic association structure of the image.

[0043] Figure 4 FIG. 2 is a flow chart of sub-step S243 of the carbonization process in the method for preparing biomass-based activated carbon catalytic material according to an embodiment of the present application. Figure 4As shown, the step S243 includes the steps of: S2431, performing spatial attention attenuation modulation on the first product carbonization pixel granularity semantic association score topological matrix based on the spatial distance between any two first product carbonization pixel granularity feature vectors in the set of the first product carbonization pixel granularity feature vectors to obtain the first product carbonization semantic association space soft constraint score topological matrix; S2432, performing hole convolution coding on the first product carbonization semantic association space soft constraint score topological matrix to obtain the first product carbonization semantic association space soft constraint score topological feature matrix. In a specific example of the present application, the step S2431 includes: calculating the natural exponential function value of the Euclidean distance between any two first product carbonization pixel granularity feature vectors in the set of the first product carbonization pixel granularity feature vectors to obtain the first product carbonization pixel granularity spatial distance topological matrix; multiplying the first product carbonization pixel granularity spatial distance topological matrix by a preset attenuation modulation constant to obtain a spatial attention attenuation modulation matrix, and calculating the first product carbonization pixel granularity semantic association score topological matrix and dividing the spatial attention attenuation modulation matrix by the position point to obtain the first product carbonization semantic association space soft constraint score topological matrix.

[0044] It should be understood that, considering that in practical applications, adjacent image regions often have stronger correlations than distant regions, this application further performs spatial attention attenuation modulation on the above pixel granularity semantic association score topological matrix based on the spatial distance between any two product carbonization pixel granularity feature vectors to simulate the association attenuation characteristics between local image regions, so that the model pays more attention to the feature association of adjacent regions. Furthermore, the score matrix adjusted by spatial soft constraints is subjected to atrous convolution encoding using the atrous convolution technique to capture the semantic association topological features between the pixel granularity features of each product carbonization, and generate a product carbonization semantic association spatial soft constraint score topological feature matrix.

[0045] In a specific example of the present application, the step S244 includes: inputting the set of the first product carbonization pixel granularity feature vectors and the first product carbonization semantic association space soft constraint score topological feature matrix into the graph convolutional coding module to obtain a set of context-related enhanced first product carbonization pixel granularity feature vectors; reshaping the set of context-related enhanced first product carbonization pixel granularity feature vectors to obtain the first product carbonization space enhanced coding feature map. That is, the set of product carbonization pixel granularity feature vectors and the product carbonization semantic association space soft constraint score topological feature matrix are processed by graph convolution coding technology to utilize the graph convolution characteristics to update the pixel granularity feature representation of each image based on the semantic association information between pixels, and to enhance the global semantic context association feature expression of the pixel granularity feature of the product carbonization image by learning the intrinsic semantic association structure of the product carbonization image. Furthermore, by reshaping the characteristic shapes of the carbonized pixel granularity features of each product after contextual association enhancement, the original image structure is restored to obtain the first product carbonization spatial enhancement coding feature map and the second product carbonization spatial enhancement coding feature map, thereby achieving refined enhancement of the local detail features of the image. It is worth mentioning that graph convolutional coding is a deep learning model for processing graph structured data. It extends the concept of convolutional neural networks to graph structures, so that it can operate directly on graph data. The core idea of ​​graph convolutional coding is to use the structural information of the graph to update the representation of the nodes, thereby capturing the complex relationship between the nodes. In graph convolutional coding, the feature representation of each node not only includes its own features, but also the features of its neighboring nodes. This information aggregation is achieved through graph convolution operations, which allows the model to extract and propagate features on the graph.

[0046] Accordingly, the first product carbonization coding characteristic map is processed by the following characteristic enhancement formula to obtain the first product carbonization spatial enhancement coding characteristic map, wherein the characteristic enhancement formula is:

[0047]

[0048]

[0049] T = AtrousConv(S′)

[0050] V′=GCN(V,T)

[0051] X′=reshape[V′,(H,W,C)]

[0052] Where X is the first product carbonization coding feature map, H, W and C represent the height, width and number of channels of the first product carbonization coding feature map, respectively, reshape[·] represents the feature shape reshaping, V represents the set of the first product carbonization pixel granularity feature vectors, v i and v j represent the i-th and j-th first product carbonization pixel size feature vectors in the set of the first product carbonization pixel size feature vectors respectively, the value of HW is the number of feature vectors in the set of the first product carbonization pixel size feature vectors, and v i ∈R C , the value of C is the length of the carbonized pixel particle size feature vector of the first product, W1 is the weight transformation matrix, W2 is the weight transformation vector, h i and h j denote the i-th and j-th carbonized pixel particle size feature vectors of the first product after mapping in the set of the carbonized pixel particle size feature vectors of the first product after mapping, arccosh(·) denotes the inverse hyperbolic cosine function in hyperbolic space, ‖·‖ denotes the norm of the feature vector, d P (h i ,h j ) represents the Poincare distance between the carbonized pixel size feature vector of the first product after the i-th mapping and the carbonized pixel size feature vector of the first product after the j-th mapping, S ij represents the semantic association score value of the position (i, f) in the topological matrix of the semantic association score of the carbonized pixel granularity of the first product, θ is the preset attenuation modulation constant, d(v i ,v j ) represents the Euclidean distance between the i-th first product carbonization pixel granularity feature vector and the j-th first product carbonization pixel granularity feature vector, S′ is the soft constraint score topological matrix of the first product carbonization semantic association space, S ij ′ is the eigenvalue of the position (i, j) in the soft constraint score topology matrix of the carbonized semantic association space of the first product, AtrousConv(·) represents the hole convolution coding, T is the soft constraint score topology feature matrix of the carbonized semantic association space of the first product, GCN(·) represents the graph convolution processing, V′ represents the set of context-related enhanced first product carbonization pixel granularity feature vectors, and X′ represents the first product carbonization space enhanced coding feature map.

[0053] In the above-mentioned method for preparing biomass-based activated carbon catalytic materials, the step S25 generates a product carbonization degree coefficient based on the characteristic difference between the first product carbonization space enhancement coding characteristic diagram and the second product carbonization space enhancement coding characteristic diagram. Figure 5 FIG. 2 is a flow chart of sub-step S25 of the carbonization process in the method for preparing biomass-based activated carbon catalytic material according to an embodiment of the present application. Figure 5 As shown, the step S25 includes the steps of: S251, inputting the first product carbonization space enhancement coding feature map and the second product carbonization space enhancement coding feature map into the product carbonization degree measurement network to obtain the product carbonization degree change coding feature map; S252, inputting the product carbonization degree change coding feature map into the decoder-based product carbonization degree estimation module to obtain the product carbonization degree coefficient.

[0054] Specifically, in step S251, the first product carbonization space enhancement coding feature map and the second product carbonization space enhancement coding feature map are input into the product carbonization degree measurement network to obtain the product carbonization degree change coding feature map. It should be understood that in order to achieve an accurate estimation of the product carbonization degree, the present application further uses the product carbonization degree measurement network to measure the difference between the first product carbonization space enhancement coding feature map and the second product carbonization space enhancement coding feature map. Specifically, the product carbonization degree measurement network performs a position difference calculation on the first product carbonization space enhancement coding feature map and the second product carbonization space enhancement coding feature map to quantitatively represent the carbonization feature difference between the two, thereby obtaining a product carbonization degree change coding feature map.

[0055] Specifically, in step S252, the product carbonization degree change coding feature map is input into the product carbonization degree estimation module based on the decoder to obtain the product carbonization degree coefficient. That is, the product carbonization degree estimation module is constructed using the decoder model, and the product carbonization degree change coding feature map is extracted and learned layer by layer by using the decoder to map it to the numerical representation space of the product carbonization degree, and a quantitative index for reflecting the relative change of the product carbonization degree from the first time point to the second time point is generated, that is, the product carbonization degree coefficient, so as to realize the real-time quantitative evaluation of the product carbonization degree.

[0056] Preferably, in an example of the present application, inputting the product carbonization degree change coding characteristic diagram into a decoder-based product carbonization degree estimation module to obtain a product carbonization degree coefficient includes: calculating the sum of the absolute values ​​of each characteristic value of the product carbonization degree change coding characteristic diagram to obtain a first product carbonization degree change coding space structure value, and calculating the square root of the sum of the squares of each characteristic value of the product carbonization degree change coding characteristic diagram to obtain a second product carbonization degree change coding space structure value; multiplying each characteristic value of the product carbonization degree change coding characteristic diagram with the first product carbonization degree change coding space structure value and the second product carbonization degree change coding space structure value to obtain a first product carbonization degree change coding structure reference value and a second product carbonization degree change coding structure reference value corresponding to each characteristic value; multiplying each characteristic value of the product carbonization degree change coding characteristic diagram with the scale of the product carbonization degree change coding characteristic diagram and the square root of the scale to obtain The first product carbonization degree change coding scale transformation value and the second product carbonization degree change coding scale transformation value corresponding to each characteristic value; dividing the first product carbonization degree change coding structure reference value by the difference between the first product carbonization degree change coding space structure value and the first product carbonization degree change coding scale transformation value to obtain the first product carbonization degree change coding transformation adjustment value; dividing the second product carbonization degree change coding structure reference value by the difference between the second product carbonization degree change coding space structure value and the second product carbonization degree change coding scale transformation value to obtain the second product carbonization degree change coding transformation adjustment value; calculating the weighted sum of the first product carbonization degree change coding transformation adjustment value and the second product carbonization degree change coding transformation adjustment value to obtain each characteristic value of the optimized product carbonization degree change coding feature map; inputting the optimized product carbonization degree change coding feature map into the decoder-based product carbonization degree estimation module to obtain the product carbonization degree coefficient.

[0057] Accordingly, the optimization process of the product carbonization degree change coding characteristic diagram is represented by the following optimization formula:

[0058]

[0059] f 1i =(α×f i ) / (α-n×f i )

[0060]

[0061] f i ∈F∈R W′×H′×C′

[0062] f 1i ∈F1∈RW′×H′×C′

[0063] f 2i ∈F2∈R W′×H′×C′

[0064] n=W′×H′×C′

[0065] Wherein, F represents the coding characteristic diagram of the carbonization degree change of the product, f i represents the ith eigenvalue of the coding characteristic diagram of the carbonization degree change of the product, W′, H′ and C′ represent the width, height and number of channels of the coding characteristic diagram of the carbonization degree change of the product, n is the number of eigenvalues ​​of the coding characteristic diagram of the carbonization degree change of the product, R represents a real number set, α represents the first coding space structure value of the carbonization degree change of the product, β represents the second coding space structure value of the carbonization degree change of the product, f 1i represents the first product carbonization degree change coding transformation adjustment value corresponding to the ith characteristic value in the product carbonization degree change coding characteristic map, F1 is the first product carbonization degree change coding transformation adjustment map composed of the first product carbonization degree change coding transformation adjustment values, and f 2i represents the second product carbonization degree change coding transformation adjustment value corresponding to the ith characteristic value in the product carbonization degree change coding characteristic map, F2 is the second product carbonization degree change coding transformation adjustment map composed of each of the second product carbonization degree change coding transformation adjustment values, ω is a weight hyperparameter, represents addition according to the position point, ⊙ represents multiplication according to the position point, and F′ represents the optimized coding characteristic diagram of the change in carbonization degree of the product.

[0066] That is, the present application takes into account that the first product carbonization coding feature map and the second product carbonization coding feature map represent the product carbonization image semantics of the first product carbonization image and the second product carbonization image at the first time point and the second time point, respectively. After subjecting them to feature enhancement based on spatial soft constraints, both are input into the product carbonization degree measurement network to obtain the product carbonization degree change coding feature map. Although feature enhancement based on spatial soft constraints can enhance the spatial distribution significance of the first product carbonization coding feature map and the second product carbonization coding feature map, it will also weaken and shift the spatial distribution alignment between the first product carbonization coding feature map and the second product carbonization coding feature map, thereby making the product carbonization degree change coding feature map have a complex spatial structure, and therefore it is expected to improve its regression convergence and generalization effects based on complex spatial structures.

[0067] Therefore, in a preferred example, with respect to the spatial structure information of the feature set of the encoding feature map of the product carbonization degree change in the high-dimensional space, a scale-based frame transformation of each eigenvalue of the encoding feature map of the product carbonization degree change is performed by taking the class-norm spatial structured representation of the encoding feature map of the product carbonization degree change as a reference window, and a spatial structure-based frame attention weight adjustment of each eigenvalue of the encoding feature map of the product carbonization degree change is implemented to ensure the invariance of the spatial transformation (translation, scaling and rotation) of the encoding feature map of the product carbonization degree change under the interaction of feature space, thereby improving the convergence and generalization effect of the decoding regression of the feature set of the encoding feature map of the product carbonization degree change under the complex spatial structure representation, and improving the accuracy of the product carbonization degree coefficient obtained by inputting it into the decoder-based product carbonization degree estimation module.

[0068] In the above-mentioned method for preparing biomass-based activated carbon catalytic materials, the step S26 predicts the time required for complete carbonization of the product based on the product carbonization degree coefficient and the product carbonization time. In a specific example of the present application, the step S26 includes: dividing the product carbonization degree coefficient by the product carbonization time to obtain the time required for complete carbonization of the product. It should be understood that the product carbonization degree coefficient is a quantitative indicator to measure the change in the degree of carbonization of the product within the product carbonization time range. By calculating the ratio between the product carbonization degree coefficient and the product carbonization time, the carbonization rate and carbonization stage of the product can be effectively revealed, which can be used as a measure of the time required for complete carbonization of the product, and can provide effective guidance and reference for carbonization control in industrial production processes.

[0069] In summary, the preparation method of biomass-based activated carbon catalytic materials is explained, which uses a camera to collect product carbonization images at two different times, and uses deep learning-based image processing technology to process the product carbonization images, respectively extract the product carbonization characteristics in the two images, and then based on the difference in product carbonization characteristics between the two images, intelligently estimate the product carbonization degree coefficient, and then further combine the time interval between the two time points and the product carbonization degree coefficient to calculate the time required for the product to be completely carbonized. In this way, by real-time monitoring and precise control of the carbonization process, the problems of uneven carbonization or excessive carbonization in traditional processes can be effectively avoided, thereby improving the performance and stability of the catalyst.

[0070] Furthermore, a biomass-based activated carbon catalytic material is also provided. Specifically, the biomass-based activated carbon catalytic material is prepared by the above-mentioned method for preparing the biomass-based activated carbon catalytic material.

[0071] 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.

[0072] In the above embodiments, the description of each embodiment has its own emphasis. For the 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 system can be implemented in other ways. For example, the system embodiment described above is only schematic. For example, the unit division is only a logical function division, and there may be other division methods in actual implementation. The units described as separate components may or may not be physically separated, and the components displayed as units 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 units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0073] 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.

[0074] In addition, it is obvious that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. The multiple units stated in the system claims can also be implemented by one unit through software or hardware.

[0075] 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 biomass-based activated carbon catalytic material, comprising: S1: Take the biomass raw material and dry it at 105℃ for 2h; S2: taking out the dried product, using N2 as a protective gas, and heating it to 500-800°C at a heating rate of 10°C / min for carbonization treatment, and obtaining a biomass-based activated carbon precursor after 1-4 hours; S3: immersing the biomass-based activated carbon precursor in a saturated KOH solution for 2 hours; S4: taking out the impregnated product and heating it to 400-800°C for activation for 0.5-2h to obtain biomass-based activated carbon; S5: washing the activated biomass-based activated carbon with a 1-2M hydrochloric acid solution, then rinsing it with deionized water until it is neutral, and then drying it to a constant weight to obtain biomass-based activated carbon powder particles; S6: mixing the biomass-based activated carbon powder particles and a binder at a weight ratio of 100:(0.5-1.5) and kneading them into a shape to obtain a biomass-based activated carbon catalytic material carrier, characterized in that the step S2 also includes: Collecting product carbonization images through a camera at a first time point and a second time point respectively to obtain a first product carbonization image and a second product carbonization image; Determining a product carbonization time based on the first time point and the second time point; Extracting product carbonization features of the first product carbonization image and the second product carbonization image respectively to obtain a first product carbonization coding feature map and a second product carbonization coding feature map; Performing pixel-granularity-based spatial constraint enhancement processing on the first product carbonization coding characteristic map and the second product carbonization coding characteristic map to obtain a first product carbonization spatial enhancement coding characteristic map and a second product carbonization spatial enhancement coding characteristic map; generating a product carbonization degree coefficient based on a characteristic difference between the first product carbonization spatial enhancement coding characteristic diagram and the second product carbonization spatial enhancement coding characteristic diagram; The time required for complete carbonization of the product is predicted based on the product carbonization degree coefficient and the product carbonization time.

2. The method for preparing a biomass-based activated carbon catalytic material according to claim 1, characterized in that: Extracting product carbonization features of the first product carbonization image and the second product carbonization image respectively to obtain a first product carbonization coding feature map and a second product carbonization coding feature map, including: The first product carbonization image and the second product carbonization image are passed through a product carbonization feature extractor based on a deep separable convolutional neural network model to obtain the first product carbonization coding feature map and the second product carbonization coding feature map.

3. The method for preparing a biomass-based activated carbon catalytic material according to claim 2, characterized in that: The first product carbonization coding characteristic map and the second product carbonization coding characteristic map are respectively subjected to a spatial constraint enhancement process based on pixel granularity to obtain a first product carbonization spatial enhancement coding characteristic map and a second product carbonization spatial enhancement coding characteristic map, including: Performing feature dispersion on the first product carbonization coding feature map along the channel dimension to obtain a set of first product carbonization pixel particle size feature vectors; Calculating the semantic association score between any two first product carbonization pixel granularity feature vectors in the set of the first product carbonization pixel granularity feature vectors to obtain a first product carbonization pixel granularity semantic association score topological matrix; Based on the pixel spatial position information of the first product carbonization coding feature map, the first product carbonization pixel granularity semantic association score topological matrix is ​​subjected to spatial attention attenuation modulation to obtain the first product carbonization semantic association spatial soft constraint score topological feature matrix; Based on the soft constraint score topological feature matrix of the first product carbonization semantic association space, the first product carbonization coding feature map is subjected to pixel granularity spatial enhancement processing to obtain the first product carbonization spatial enhanced coding feature map.

4. The method for preparing a biomass-based activated carbon catalytic material according to claim 3, characterized in that: Calculating the semantic association score between any two first product carbonization pixel granularity feature vectors in the set of the first product carbonization pixel granularity feature vectors to obtain a first product carbonization pixel granularity semantic association score topological matrix, including: Performing nonlinear mapping on each first product carbonization pixel particle size feature vector in the set of the first product carbonization pixel particle size feature vectors to obtain a set of mapped first product carbonization pixel particle size feature vectors; The Poincare distance between any two first product carbonized pixel granularity feature vectors after mapping in the set of the first product carbonized pixel granularity feature vectors after mapping is calculated as the semantic association score to obtain the first product carbonized pixel granularity semantic association score topological matrix.

5. The method for preparing a biomass-based activated carbon catalytic material according to claim 4, characterized in that: Based on the pixel spatial position information of the first product carbonization coding feature map, the first product carbonization pixel granularity semantic association score topology matrix is ​​subjected to spatial attention attenuation modulation to obtain the first product carbonization semantic association space soft constraint score topology feature matrix, including: Based on the spatial distance between any two first product carbonization pixel granularity feature vectors in the set of the first product carbonization pixel granularity feature vectors, the first product carbonization pixel granularity semantic association score topological matrix is ​​subjected to spatial attention attenuation modulation to obtain the first product carbonization semantic association spatial soft constraint score topological matrix; The soft constraint score topology matrix of the carbonized semantic association space of the first product is subjected to dilated convolution coding to obtain the soft constraint score topology feature matrix of the carbonized semantic association space of the first product.

6. The method for preparing a biomass-based activated carbon catalytic material according to claim 5, characterized in that: Based on the spatial distance between any two first product carbonization pixel granularity feature vectors in the set of the first product carbonization pixel granularity feature vectors, the first product carbonization pixel granularity semantic association score topological matrix is ​​subjected to spatial attention attenuation modulation to obtain the first product carbonization semantic association spatial soft constraint score topological matrix, including: Calculating the natural exponential function value of the Euclidean distance between any two first product carbonization pixel particle size feature vectors in the set of the first product carbonization pixel particle size feature vectors to obtain a first product carbonization pixel particle size space distance topological matrix; The first product carbonization pixel granularity spatial distance topological matrix is ​​multiplied by a preset attenuation modulation constant to obtain a spatial attention attenuation modulation matrix, and the first product carbonization pixel granularity semantic association score topological matrix is ​​calculated and divided by the spatial attention attenuation modulation matrix by a position point to obtain the first product carbonization semantic association spatial soft constraint score topological matrix.

7. The method for preparing a biomass-based activated carbon catalytic material according to claim 6, characterized in that: Based on the soft constraint score topological feature matrix of the first product carbonization semantic association space, the first product carbonization coding feature map is subjected to pixel granularity spatial enhancement processing to obtain the first product carbonization spatial enhanced coding feature map, including: Inputting the set of the first product carbonization pixel granularity feature vectors and the first product carbonization semantic association space soft constraint score topological feature matrix into a graph convolutional coding module to obtain a set of context-related enhanced first product carbonization pixel granularity feature vectors; The set of the context-related enhanced first product carbonization pixel granularity feature vectors is reshaped to obtain the first product carbonization spatial enhancement coding feature map.

8. The method for preparing a biomass-based activated carbon catalytic material according to claim 7, characterized in that: Generating a product carbonization degree coefficient based on a characteristic difference between the first product carbonization spatial enhancement coding characteristic diagram and the second product carbonization spatial enhancement coding characteristic diagram comprises: Inputting the first product carbonization spatial enhancement coding characteristic map and the second product carbonization spatial enhancement coding characteristic map into a product carbonization degree measurement network to obtain a product carbonization degree change coding characteristic map; The product carbonization degree variation coding characteristic diagram is input into a product carbonization degree estimation module based on a decoder to obtain the product carbonization degree coefficient.

9. The method for preparing a biomass-based activated carbon catalytic material according to claim 8, characterized in that: Based on the product carbonization degree coefficient and the product carbonization time, predicting the time required for complete carbonization of the product includes: The time required for complete carbonization of the product is obtained by dividing the product carbonization degree coefficient by the product carbonization time.

10. A biomass-based activated carbon catalytic material, characterized in that: The biomass-based activated carbon catalytic material is prepared by the method for preparing the biomass-based activated carbon catalytic material according to any one of claims 1-9.

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