Method for increasing phenol yield through catalytic pyrolysis of retired fan blade

Abstract

The invention discloses a method for increasing the phenol yield through catalytic pyrolysis of a decommissioned fan blade, and relates to the technical field of waste composite material resourceful treatment.The method comprises the steps that decommissioned fan blade fragment materials obtained after crushing and magnetic separation are subjected to multispectral imaging, multispectral data are obtained, and pretreatment is conducted; performing spectral feature extraction on the preprocessed multispectral data by using a convolutional neural network; identifying and separating the fragment materials based on spectral characteristics and automatic separation equipment; carrying out activation and carbonization treatment on the separated balsa wood fragments to prepare a carbon-based catalyst; carrying out catalytic pyrolysis on the separated fragment material by utilizing an automatic adjusting reactor and a carbon-based catalyst; collecting a product generated by catalytic pyrolysis, and carrying out qualitative and quantitative analysis on phenols to obtain a qualitative analysis result and a quantitative analysis result. According to the method, through a mode of combining multispectral imaging and the convolutional neural network, different types of composite materials are accurately identified and classified.

Description

Technical Field

[0001] The invention relates to the technical field of resource processing of waste composite materials, and in particular to a method for catalytically pyrolyzing retired fan blades to increase the yield of phenols. Background Art

[0002] With the rapid development of the wind energy industry, more and more wind turbine blades are retired due to aging or technology updates. These blades are mainly composed of composite materials such as balsa wood, PET foam, glass fiber reinforced plastic (GFRP) and PVC. They have high strength and durability and are difficult to degrade naturally, which brings great challenges to waste disposal. Traditional treatment methods, such as landfill and incineration, not only waste land resources, but also cause serious environmental pollution, release harmful gases and microparticles, and increase ecological burden.

[0003] In recent years, pyrolysis technology has been gradually used for the resource treatment of retired blades, but conventional pyrolysis methods have problems such as uneven product distribution and low yield of phenolic products, which limit their practical application. How to efficiently treat retired fan blades and increase the yield of high value-added phenolic products has become a technical problem that needs to be solved in this field. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method for improving the yield of phenols by catalytic pyrolysis of retired fan blades to solve the problem of low yield of phenol products in traditional pyrolysis processes.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, the present invention provides a method for improving the yield of phenols by catalytic pyrolysis of retired fan blades, which comprises performing multispectral imaging on the retired fan blade fragments after crushing and magnetic separation, acquiring multispectral data, and performing preprocessing; The convolutional neural network is used to extract spectral features from the preprocessed multispectral data; Identify and separate fragment materials based on spectral features and automated separation equipment; Activating and carbonizing the separated balsa wood fragments to prepare a carbon-based catalyst; The separated fragment materials are subjected to catalytic pyrolysis using an automatically regulating reactor and a carbon-based catalyst; collecting products generated by catalytic pyrolysis, and performing qualitative and quantitative analysis on phenols to obtain qualitative analysis results and quantitative analysis results; Generate a phenol yield report based on the qualitative and quantitative analysis results.

[0007] As a preferred embodiment of the method for improving the yield of phenols by catalytic pyrolysis of retired fan blades according to the present invention, the retired fan blade fragment materials include balsa wood fragments, PET foam fragments, glass fiber reinforced plastic fragments and PVC fragments; the preprocessing includes white reference correction, smoothing denoising and normalization.

[0008] As a preferred solution of the method for improving the yield of phenols by catalytic pyrolysis of retired fan blades according to the present invention, a convolutional neural network is used to extract spectral features from the pre-processed multi-spectral data, and the specific steps are as follows: The NumPy library is used to adjust the format of the preprocessed multispectral data to obtain multispectral data in a format that conforms to the convolutional neural network; The multispectral data in the format of the convolutional neural network is input into the existing multi-layer convolutional neural network, and supervised learning is performed using the Adam optimizer and the cross entropy loss function to obtain the output spectral features from the fully connected layer.

[0009] As a preferred solution of the method for increasing the yield of phenols by catalytic pyrolysis of retired fan blades according to the present invention, the fragment materials are identified and separated based on spectral characteristics and automated separation equipment. The specific steps are as follows: Using the spectral features extracted by convolutional neural networks, spectral feature databases were established for balsa wood fragments, PET foam fragments, glass fiber reinforced plastic fragments, and PVC fragments. Use multi-spectral sensors, manipulators and conveyor belts to form automated separation equipment; The spectral feature database is combined with the automated separation equipment to identify and separate the fragment materials to obtain the separated fragment materials.

[0010] As a preferred embodiment of the method for increasing the yield of phenols by catalytic pyrolysis of retired fan blades according to the present invention, the separated balsa wood fragments are activated and carbonized to prepare a carbon-based catalyst, and the specific steps are as follows: The sorted balsa wood chips are first cleaned, then dried, and finally ground to obtain balsa wood powder; The balsa wood powder is mixed with the activator solution, and the mixture is shaken and activated at room temperature, and the mixture after the shaking and activation is dried to obtain dry activated balsa wood powder; The carbonized balsa wood powder is obtained by heating the dried activated balsa wood powder in an inert atmosphere; The carbonized balsa wood powder is cooled to room temperature, residual activator and by-products are removed with dilute hydrochloric acid, the dilute hydrochloric acid is then removed with deionized water, and finally the balsa wood powder from which the dilute hydrochloric acid is removed is dried to obtain a carbon-based catalyst.

[0011] As a preferred embodiment of the method for increasing the yield of phenols by catalytic pyrolysis of retired fan blades according to the present invention, the separated fragment materials are catalytically pyrolyzed by using an automatic regulating reactor and a carbon-based catalyst, and the specific steps are as follows: uniformly mixing the separated fragment material and the carbon-based catalyst to obtain a mixture of the fragment material and the carbon-based catalyst; feeding a mixture of the fragment material and the carbon-based catalyst into an automatically regulating reactor; The automatic regulating reactor is started to perform catalytic pyrolysis to obtain gaseous products.

[0012] As a preferred embodiment of the method for increasing the yield of phenols by catalytic pyrolysis of retired fan blades according to the present invention, the products generated by catalytic pyrolysis are collected, and the phenols are qualitatively and quantitatively analyzed to obtain qualitative analysis results and quantitative analysis results. The specific steps are as follows: The gaseous product is condensed through a condenser to obtain a liquid product; Extracting phenols from liquid products using solvent extraction; The extracted phenols were qualitatively and quantitatively analyzed by gas chromatography-mass spectrometry to obtain qualitative analysis results and quantitative analysis results.

[0013] As a preferred solution of the method for increasing the yield of phenols by catalytic pyrolysis of retired fan blades according to the present invention, a phenol yield report is generated according to the qualitative analysis results and the quantitative analysis results. The specific steps are as follows: According to the results of qualitative and quantitative analysis, the types and contents of phenols were determined; The types and contents of phenols obtained by the pyrolysis method without using a carbon-based catalyst are compared with the types and contents of phenols catalyzed by pyrolysis with the addition of a carbon-based catalyst to obtain a comparative result; Generate a yield change graph based on temperature, catalyst dosage, reaction time and phenol yield; Combine the comparison results with the yield change graph to generate a phenol yield report.

[0014] In a second aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the method for increasing the yield of phenols by catalytic pyrolysis of retired fan blades as described in the first aspect of the present invention is implemented.

[0015] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the method for increasing the yield of phenols by catalytic pyrolysis of retired fan blades as described in the first aspect of the present invention is implemented.

[0016] The beneficial effects of the present invention are as follows: the present invention provides an efficient method for improving the yield of phenols by catalytic pyrolysis, which can effectively process a large number of retired wind turbine blade fragments. By combining multispectral imaging and convolutional neural networks, different types of composite materials are accurately identified and classified to ensure the purity of the materials during the catalytic pyrolysis process. At the same time, the carbon-based catalyst prepared by activation and carbonization of balsa wood fragments significantly improves the yield of phenolic compounds in the pyrolysis reaction, making up for the shortcomings of traditional pyrolysis methods. In addition, the use of an automatic regulating reactor realizes precise control of reaction conditions, further improving the reaction efficiency and the accuracy of product collection. Through qualitative and quantitative analysis, the present invention can generate a detailed phenol yield report, providing a reliable basis for subsequent process optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0018] Figure 1 This is a flow chart of the method for increasing the yield of phenols by catalytic pyrolysis of retired fan blades in Example 1.

[0019] Figure 2 This is a flow chart for preparing the carbon-based catalyst in Example 1. DETAILED DESCRIPTION

[0020] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0021] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0023] Example 1, reference Figure 1 and Figure 2, which is the first embodiment of the present invention, provides a method for improving the yield of phenols by catalytic pyrolysis of retired fan blades, comprising the following steps: S1. Multispectral imaging is performed on the fragments of retired wind turbine blades after crushing and magnetic separation to obtain multispectral data and perform preprocessing, including the following steps: The materials of the retired wind turbine blade fragments after crushing and magnetic separation include balsa wood fragments, PET foam fragments, glass fiber reinforced plastic fragments and PVC fragments.

[0024] A multispectral camera covering the visible to near-infrared spectral range (400nm-1000nm) is used to collect spectra of retired wind turbine blade fragments to obtain multispectral data.

[0025] The multispectral data are preprocessed, including white reference correction, smoothing, denoising and normalization.

[0026] Specifically, a standard white reference plate (a material with known reflectivity) is used to perform white reference calibration on the multispectral camera. It should be noted that by comparing the image data of the white reference plate with the image data of the actual material, errors caused by uneven light sources or inconsistent camera responses are eliminated.

[0027] The Savitzy-Golay smoothing algorithm is used to smooth the multi-spectral curves in the multi-spectral data to reduce the interference of noise and ensure the smoothness and continuity of the material spectral reflectance curve. Specifically, the spectral data points of each band are smoothed through a sliding window, and the window size is set to 7 data points.

[0028] The data in each multispectral image is normalized by linear normalization. The specific normalization formula is: ; in, is the normalized spectral intensity, is the raw spectral intensity, is the minimum value of the spectral intensity, is the maximum value of the spectral intensity.

[0029] It should be noted that this normalization method is very common when processing image data, spectral data, etc., which requires normalizing data of different magnitudes for subsequent processing (such as convolutional neural network input).

[0030] The preprocessed multispectral data is stored in the form of a three-dimensional matrix (image height, image width, number of spectral bands).

[0031] S2. Using a convolutional neural network to extract spectral features from the preprocessed multispectral data, including the following steps: The NumPy library is used to adjust the format of the preprocessed multispectral data to obtain multispectral data in a format that conforms to the convolutional neural network. Specifically, the NumPy library is used to read the preprocessed multispectral data, and the format of the data is automatically inferred to be (number of samples, height, width, number of bands) or (number of samples, number of bands, height, width) according to the number of dimensions and the range of the number of bands of the data through programming, and the corresponding conversion is performed. It should be noted that in order to automatically determine the dimension, the data layout is inferred based on the typical range of the number of dimensions and the number of bands of the data.

[0032] Further multispectral data format adjustment: The multispectral data processed by the NumPy library is converted into PyTorch tensor form through the torch.tensor function in the PyTorch library. It should be noted that the torch.tensor function is a common function in the PyTorch library used to convert Python data types (such as lists, tuples) or NumPy arrays into PyTorch tensors. Tensors are the basic data structure used to process and store data in PyTorch, similar to NumPy arrays, but they are more suitable for performing efficient automatic differentiation and parallel computing on GPUs.

[0033] Adam (Adaptive Moment Estimation) is a commonly used optimization algorithm that combines momentum and adaptive learning rate to update the parameters of the model, with faster convergence speed and better robustness. The cross entropy loss function is used for multi-classification problems to calculate the difference between the category distribution predicted by the model and the true category distribution.

[0034] The multispectral data in the format of the convolutional neural network is input into the existing multi-layer convolutional neural network, and supervised learning is performed using the Adam optimizer and the cross entropy loss function to obtain the output spectral features from the fully connected layer.

[0035] Specifically, when defining the Adam optimizer, specify the model parameters (i.e., the weights and biases of the convolutional neural network) and the learning rate. The learning rate controls the step size of each update of the model parameters, and the common initial value of the learning rate is 0.001. When defining the cross entropy loss function, only the output of the model is compared with the true label to calculate the loss value.

[0036] It should be noted that the Adam optimizer can adaptively adjust the learning rate according to the gradient momentum and historical changes of each parameter, so that it has better effects in most deep learning tasks.

[0037] Specifically, the training process includes forward propagation, loss calculation, back propagation, and parameter update. It should be noted that the training of convolutional neural networks usually requires multiple iterations, and each iteration runs the model once on the entire training dataset and updates the parameters. As the number of iterations increases, the performance of the model will gradually improve and the loss value will gradually decrease.

[0038] Extract features from the output of the fully connected layer of the convolutional neural network. Specifically, the input multispectral data is passed through the trained convolutional neural network and forward propagation is performed. It should be noted that no backpropagation or parameter update is performed at this time, but the network is set to evaluation mode to ensure that the extracted spectral features are stable and not affected by the training process. Spectral features can be regarded as a high-dimensional representation of the input data, which contains spectral information after multiple layers of convolution and pooling.

[0039] S3, based on spectral characteristics and automated separation equipment, identifying and separating the fragmented materials, including the following steps, Using the spectral features extracted by the convolutional neural network, spectral feature databases are established for balsa wood fragments, PET foam fragments, glass fiber reinforced plastic fragments, and PVC fragments. Specifically, these spectral feature databases should contain unique spectral information for each material to facilitate rapid matching and identification during subsequent classification. The data should be trained with spectral data from a large number of samples to ensure the accuracy and wide applicability of the database.

[0040] It should be noted that in order to ensure the reliability and accuracy of the database, when establishing the spectral feature library, it is necessary to consider the changes in the spectral characteristics of the material under different environmental conditions (such as temperature and humidity).

[0041] The automated separation equipment is composed of multispectral sensors, manipulators and conveyor belts. Specifically, the multispectral sensor is used to capture the spectral data of the debris material in real time, the manipulator is responsible for performing the separation operation according to the recognition results of the sensor, and the conveyor belt is responsible for transferring the debris material to different areas of the separation equipment to achieve continuous automated processing.

[0042] It should be noted that multispectral sensors can capture multiple bands of information about materials, thereby achieving accurate identification of different materials. The speed of the conveyor belt and the operating frequency of the manipulator should match the recognition speed of the sensor to ensure efficient operation of the system. Automated separation equipment should take into account the shape, size and weight of the material to avoid misoperation or improper separation.

[0043] Combined with the spectral feature database and automated separation equipment, the fragmented materials are identified and separated to obtain separated fragmented materials. Specifically, after the multispectral sensor obtains the spectral data of the material, it matches the spectral features in the database to identify the type of material. After the identification is completed, the robot classifies the corresponding materials into the designated area to complete the separation.

[0044] It should be noted that the accuracy of identification and separation depends on the accuracy of the spectral database and the sensitivity of the sensor. The system should have real-time feedback and adjustment functions to deal with possible identification errors or material mixing. At the same time, the separated materials should be checked regularly to ensure the accuracy of classification and the purity of the materials.

[0045] S4, activating and carbonizing the separated balsa wood fragments to prepare a carbon-based catalyst, comprising the following steps: The sorted balsa wood chips are first cleaned, then dried, and finally ground to obtain balsa wood powder. Specifically, the cleaning step should remove impurities and contaminants on the surface of the balsa wood, and the drying step should be controlled within an appropriate temperature and time range to avoid damage to the wood fiber structure, and ground to a particle size of 80 mesh to 200 mesh.

[0046] The balsa wood powder is mixed with the activator solution and activated by shaking at room temperature. The mixture after shaking activation is dried to obtain dry activated balsa wood powder. Specifically, it is placed in a ZnCl2 solution at an impregnation ratio of 1:2 and shaken in a shaker for 24 hours. After impregnation, the mixture is dried at 105°C for 48 hours. It should be noted that the type and concentration of the activator will directly affect the pore structure and surface area of ​​the balsa wood powder, so it is necessary to select appropriate activators and treatment conditions according to experimental requirements. The drying process also needs to control temperature and time to prevent structural damage to the activated balsa wood powder.

[0047] Carbonized balsa wood powder is obtained by heating dried activated balsa wood powder in an inert atmosphere. Specifically, in a pyrolysis tube furnace, the temperature is raised from room temperature under an argon atmosphere of 100 mL / min, and the temperature is raised to 600°C at a heating rate of 10°C / min and maintained for 90 min. It should be noted that the choice of carbonization temperature has an important influence on the specific surface area, pore structure and catalytic performance of the final carbon-based material. The flow rate and uniformity of the inert atmosphere also need to be strictly controlled to avoid local overheating or incomplete carbonization.

[0048] The carbonized balsa wood powder is cooled to room temperature, and the residual activator and by-products are removed with dilute hydrochloric acid, and then the dilute hydrochloric acid is removed with deionized water. Finally, the balsa wood powder with the dilute hydrochloric acid removed is dried to obtain a carbon-based catalyst. Specifically, after cooling, it is first rinsed with 0.1 M HCl solution, then rinsed with deionized water until the pH of the filtrate is close to neutral, and then dried at 105°C for 48h to obtain a carbon-based catalyst. It should be noted that the concentration and soaking time of dilute hydrochloric acid should be optimized according to the type and amount of material residues. The drying process needs to be controlled within an appropriate temperature range to avoid destroying the pore structure of the carbon-based catalyst.

[0049] S5, using an automatic regulating reactor and a carbon-based catalyst to catalytically pyrolyze the separated fragment materials, comprising the following steps: The separated fragment material and the carbon-based catalyst are uniformly mixed to obtain a mixture of the fragment material and the carbon-based catalyst. Specifically, the mixing process should ensure that the catalyst is evenly distributed on the surface of the material to increase the contact area between the catalyst and the material, thereby improving the efficiency of catalytic pyrolysis. It should be noted that the uniformity of the mixing directly affects the effect of the catalytic reaction, and the mixing device must have sufficient stirring capacity to ensure that the catalyst and the fragment material can fully contact.

[0050] The mixture of the fragment material and the carbon-based catalyst is placed in an automatically regulating reactor. Specifically, the reactor needs to have automatic temperature control, pressure regulation and atmosphere control functions to ensure the stability and safety of the catalytic pyrolysis process. It should be noted that the design of the reactor should ensure uniform heating and be able to withstand high temperature and high pressure environments. The reactor should also have a real-time monitoring function to ensure that the temperature, pressure and atmosphere parameters during the reaction process are within the ideal range.

[0051] The automatic regulating reactor is started to perform catalytic pyrolysis to obtain gaseous products. Specifically, the pyrolysis process decomposes the material into gaseous products by heating, wherein the carbon-based catalyst improves the selectivity and yield of the product by adjusting the reaction path and accelerating the reaction rate. It should be noted that the pyrolysis temperature and reaction time are key factors affecting the quality and composition of the gaseous products. In addition, the addition of the catalyst can significantly reduce the temperature required for the reaction and improve the selectivity of the target product.

[0052] S6, collecting products generated by catalytic pyrolysis, and performing qualitative and quantitative analysis on phenols to obtain qualitative analysis results and quantitative analysis results, comprising the following steps: The gaseous product is condensed by the condenser to obtain a liquid product. Specifically, the condenser lowers the temperature of the gaseous product to convert it into a liquid state, which is convenient for subsequent separation and analysis. It should be noted that the temperature control of the condenser needs to be precise to ensure that the target products such as phenols can be completely condensed without overcooling to cause the generation of other unnecessary by-products. At the same time, the condensation equipment has an anti-blocking function to ensure that the condensed product can be collected smoothly.

[0053] Phenols are extracted from liquid products using solvent extraction. Specifically, solvent extraction selectively dissolves phenols and separates them from complex liquid products. Commonly used solvents include ether, methanol, etc. It should be noted that the selection of extraction solvents should be optimized according to the solubility of phenols to ensure efficient separation. In addition, the amount of solvent and extraction time during the extraction process also need to be reasonably controlled to improve the extraction efficiency.

[0054] The extracted phenols are qualitatively and quantitatively analyzed using a gas chromatography-mass spectrometer to obtain qualitative and quantitative analysis results. Specifically, gas chromatography is used to separate phenolic compounds, while the mass spectrometer determines the type and content of phenols by detecting their molecular mass and fragmentation pattern. It should be noted that the separation efficiency of gas chromatography and the detection sensitivity of mass spectrometry are key factors affecting the accuracy of the analysis results. The instrument should be calibrated during the analysis process, and a standard sample should be used for comparison.

[0055] S7. Generating a phenol yield report according to the qualitative analysis results and the quantitative analysis results, comprising the following steps: The types and contents of phenols are determined based on the results of qualitative and quantitative analysis. Specifically, qualitative analysis is used to identify the types of phenols, while quantitative analysis is used to calculate the specific content of each phenol. It should be noted that the analysis results should be verified by repeated experiments to ensure the accuracy of the data. At the same time, the differences in products under different experimental conditions (such as temperature and catalyst dosage) need to be considered to obtain comprehensive analysis results.

[0056] The types and contents of phenols obtained by the pyrolysis method without using a carbon-based catalyst are compared with the types and contents of phenols catalyzed by adding a carbon-based catalyst to obtain a comparative result. Specifically, by analyzing the differences in the products of the two methods, the role of the carbon-based catalyst in improving the yield and selectivity of phenols is evaluated. It should be noted that other experimental conditions (such as temperature and time) should be kept consistent during the comparison process to ensure the scientificity and effectiveness of the comparison results.

[0057] A yield change graph is generated based on temperature, catalyst dosage, reaction time, and phenol yield. Specifically, the change graph helps researchers optimize reaction conditions to increase the yield of phenols by showing the changing trend of phenol yield under different experimental conditions. It should be noted that data visualization should be clear and intuitive, and the graph should be clearly marked with coordinate axes and units to ensure that researchers can quickly understand and analyze the data.

[0058] Combine the comparison results with the yield change graph to generate a phenol yield report. Specifically, the report should summarize the types, contents, yields of phenols, and the effects of carbon-based catalysts on the pyrolysis process, and provide comprehensive experimental conclusions and optimization suggestions.

[0059] It should be noted that the report should include detailed data analysis and chart presentation to ensure that the report content is scientific and readable and can provide reference for subsequent research.

[0060] This embodiment also provides a computer device, which is suitable for the method of catalytic pyrolysis of retired fan blades to increase the yield of phenols, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the method of catalytic pyrolysis of retired fan blades to increase the yield of phenols as proposed in the above embodiment.

[0061] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0062] The present embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for realizing catalytic pyrolysis of retired fan blades to increase phenol yield as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0063] In summary, the present invention accurately identifies and classifies different types of composite materials by combining multispectral imaging and convolutional neural networks, ensuring the purity of materials in the catalytic pyrolysis process. At the same time, the carbon-based catalyst prepared by activation and carbonization of balsa wood chips significantly improves the yield of phenolic compounds in the pyrolysis reaction, making up for the shortcomings of traditional pyrolysis methods. In addition, the use of an automatic regulating reactor achieves accurate control of reaction conditions, further improving the accuracy of reaction efficiency and product collection. Through qualitative and quantitative analysis, the present invention can generate a detailed phenol yield report, providing a reliable basis for subsequent process optimization.

[0064] Example 2, referring to Table 1, is the second example of the present invention. To further verify the technical solution of the present invention, experimental simulation data of a method for increasing the yield of phenols by catalytic pyrolysis of retired fan blades are provided.

[0065] In order to verify the effectiveness of the method of improving the yield of phenols by catalytic pyrolysis of retired wind turbine blades, the following experimental steps were carried out. First, balsa wood fragments, PET foam fragments, glass fiber reinforced plastic fragments, and PVC fragments were obtained from retired wind turbine blades. Multispectral imaging was performed on the fragments after crushing and magnetic separation to obtain their multispectral data. The multispectral data was preprocessed by white reference correction, smoothing denoising, and normalization to remove noise and environmental interference in the data. Subsequently, convolutional neural network (CNN) was used to extract spectral features from the preprocessed multispectral data. The data format was adjusted using the NumPy library to ensure that the dimension of the input data met the input requirements of CNN. Finally, the spectral features of different materials were extracted through supervised learning of the convolutional neural network.

[0066] After the spectral feature database is established, the material classification is carried out using an automated separation device, which consists of a multispectral sensor, a manipulator, and a conveyor belt. The multispectral sensor obtains the spectral data of the material in real time and compares it with the spectral features in the database to identify the type of material. The manipulator classifies and collects different types of debris materials based on the identification results.

[0067] Next, the separated balsa wood fragments are activated and carbonized. First, the balsa wood fragments are cleaned and dried, and then ground into powder. The balsa wood powder is mixed with the activator ZnCl2 in different mass ratios (such as 1:2, 1:1) and activated by shaking at room temperature. After the activated mixture is dried, it is placed in an inert atmosphere (nitrogen) for carbonization. The carbonization process temperature is set at 600°C and lasts for 3 hours. The carbonized balsa wood powder is cooled to room temperature, and the residual activator and by-products are removed with dilute hydrochloric acid. Finally, it is washed with deionized water and dried to obtain the final carbon-based catalyst.

[0068] The carbon-based catalyst was evenly mixed with the separated fragment materials (PET foam fragments, glass fiber reinforced plastic fragments, PVC fragments) and put into an automatic regulating reactor. The catalytic pyrolysis process in the reactor was carried out at a high temperature of 600°C, and the pyrolysis time was controlled at 2 hours. The gaseous products produced by pyrolysis were condensed by a condenser and the liquid products were collected. Subsequently, phenolic compounds were extracted from the liquid products by solvent extraction, and qualitative and quantitative analysis was performed using gas chromatography-mass spectrometry (GC-MS).

[0069] The details are shown in Table 1 below: Table 1 Process parameters and recovery effects

[0070] From the data in the table, it can be seen that the addition of catalyst significantly increased the total amount of phenolic compounds and the phenol content. Taking the data of catalytic pyrolysis group 1 to catalytic pyrolysis group 6 as an example, all the ZnCl 2 In the experiments using the catalyst as an activator, the total amount of phenolic compounds was much higher than that of control groups 1 to 5. Especially in catalytic pyrolysis group 3, when the mass ratio of balsa wood to activator was 1:1 and the mass ratio of epoxy resin to PVC core material was 0.75:0.25, the total amount of phenolic compounds reached the highest value of 96.27%, and the phenol content also reached 76.27%. In contrast, the total amount of phenolic compounds in control group 5 without the use of catalyst was only 40.18%, showing the significant effect of the catalyst on improving the yield of phenols.

[0071] Further analysis revealed that the type and mass ratio of the activator also had a significant effect on the product. 2 When used as an activator, the total amount of phenolic compounds is generally higher than that when using H 3 PO 4 The catalytic pyrolysis groups (such as catalytic pyrolysis groups 1 to catalytic pyrolysis groups 6 and catalytic pyrolysis groups 7 to catalytic pyrolysis groups 11). In addition, the change in the mass ratio of epoxy resin to PVC core material also has a great influence on the product. When the proportion of epoxy resin increases, the total amount of phenolic compounds and phenol content are increased. For example, the total amount of phenolic compounds in catalytic pyrolysis group 3 (0.75:0.25) is significantly higher than that in catalytic pyrolysis group 6 (0:1).

[0072] Compared with the prior art, the present invention significantly improves the yield of phenolic compounds, especially in terms of phenol content, by introducing a carbon-based catalyst. Compared with the control group without a catalyst, the catalytic pyrolysis group using a catalyst showed a higher phenol content, milder reaction conditions, and higher product selectivity. This innovative improvement not only effectively solves the problems of low phenol yield and harsh conditions in the pyrolysis process in the prior art, but also provides a new way for the resource utilization of retired fan blades.

[0073] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for increasing the yield of phenols by catalytic pyrolysis of retired fan blades, characterized in that: include, Multispectral imaging is performed on the fragments of retired wind turbine blades after crushing and magnetic separation to obtain multispectral data and perform preprocessing; The convolutional neural network is used to extract spectral features from the preprocessed multispectral data; Identify and separate fragment materials based on spectral features and automated separation equipment; Activating and carbonizing the separated balsa wood fragments to prepare a carbon-based catalyst; The separated fragment materials are subjected to catalytic pyrolysis using an automatically regulating reactor and a carbon-based catalyst; collecting products generated by catalytic pyrolysis, and performing qualitative and quantitative analysis on phenols to obtain qualitative analysis results and quantitative analysis results; Generate a phenol yield report based on the qualitative and quantitative analysis results.

2. The method for increasing the yield of phenols by catalytic pyrolysis of retired fan blades according to claim 1, characterized in that: The retired wind turbine blade fragments include balsa wood fragments, PET foam fragments, glass fiber reinforced plastic fragments and PVC fragments; The preprocessing includes white reference correction, smoothing denoising and normalization.

3. The method for increasing the yield of phenols by catalytic pyrolysis of retired fan blades according to claim 1, characterized in that: The convolutional neural network is used to extract spectral features from the preprocessed multispectral data. The specific steps are as follows: The NumPy library is used to adjust the format of the preprocessed multispectral data to obtain multispectral data in a format that conforms to the convolutional neural network; The multispectral data in the format of the convolutional neural network is input into the existing multi-layer convolutional neural network, and supervised learning is performed using the Adam optimizer and the cross entropy loss function to obtain the output spectral features from the fully connected layer.

4. The method for increasing the yield of phenols by catalytic pyrolysis of retired fan blades according to claim 1, characterized in that: Based on spectral characteristics and automated separation equipment, the fragment materials are identified and separated. The specific steps are as follows: Using the spectral features extracted by convolutional neural networks, spectral feature databases were established for balsa wood fragments, PET foam fragments, glass fiber reinforced plastic fragments, and PVC fragments. Use multi-spectral sensors, manipulators and conveyor belts to form automated separation equipment; The spectral feature database is combined with the automated separation equipment to identify and separate the fragment materials to obtain the separated fragment materials.

5. The method for increasing the yield of phenols by catalytic pyrolysis of retired fan blades according to claim 1, characterized in that: The separated balsa wood fragments are activated and carbonized to prepare a carbon-based catalyst. The specific steps are as follows: The sorted balsa wood chips are first cleaned, then dried, and finally ground to obtain balsa wood powder; The balsa wood powder is mixed with the activator solution, and the mixture is shaken and activated at room temperature, and the mixture after the shaking and activation is dried to obtain dry activated balsa wood powder; The carbonized balsa wood powder is obtained by heating the dried activated balsa wood powder in an inert atmosphere; The carbonized balsa wood powder is cooled to room temperature, residual activator and by-products are removed with dilute hydrochloric acid, the dilute hydrochloric acid is then removed with deionized water, and finally the balsa wood powder from which the dilute hydrochloric acid is removed is dried to obtain a carbon-based catalyst.

6. The method for increasing the yield of phenols by catalytic pyrolysis of retired fan blades according to claim 1, characterized in that: The separated fragment materials are subjected to catalytic pyrolysis using an automatic regulating reactor and a carbon-based catalyst. The specific steps are as follows: uniformly mixing the separated fragment material and the carbon-based catalyst to obtain a mixture of the fragment material and the carbon-based catalyst; feeding a mixture of the fragment material and the carbon-based catalyst into an automatically regulating reactor; The automatic regulating reactor is started to perform catalytic pyrolysis to obtain gaseous products.

7. The method for increasing the yield of phenols by catalytic pyrolysis of retired fan blades according to claim 1, characterized in that: The products generated by catalytic pyrolysis are collected, and the phenols are qualitatively and quantitatively analyzed to obtain qualitative analysis results and quantitative analysis results. The specific steps are as follows: The gaseous product is condensed through a condenser to obtain a liquid product; Extracting phenols from liquid products using solvent extraction; The extracted phenols were qualitatively and quantitatively analyzed by gas chromatography-mass spectrometry to obtain qualitative analysis results and quantitative analysis results.

8. The method for increasing the yield of phenols by catalytic pyrolysis of retired fan blades according to claim 7, characterized in that: According to the qualitative and quantitative analysis results, a phenol yield report is generated. The specific steps are as follows: According to the results of qualitative and quantitative analysis, the types and contents of phenols were determined; The types and contents of phenols obtained by the pyrolysis method without using a carbon-based catalyst are compared with the types and contents of phenols catalyzed by pyrolysis with the addition of a carbon-based catalyst to obtain a comparative result; Generate a yield change graph based on temperature, catalyst dosage, reaction time and phenol yield; Combine the comparison results with the yield change graph to generate a phenol yield report.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for catalytic pyrolysis of retired wind turbine blades to increase phenol yield as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for increasing the yield of phenols by catalytic pyrolysis of retired fan blades as described in any one of claims 1 to 7 are implemented.

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