Circulating fluidized bed-based line board resin continuous gasification control system

Abstract

The present application relates to the technical field of circulating fluidized bed, and discloses a circuit board resin continuous gasification control system based on a circulating fluidized bed, which comprises: a component identification module, which calculates a component concentration vector through an identification model; a layered temperature control module, which calculates a target temperature vector and collects an actual temperature, and calculates a bed average temperature through the actual temperature; an intelligent feeding module, which determines a feeding rate according to the component concentration vector and a target product composition; a product optimization module, which generates an operation parameter adjustment instruction according to a gas concentration deviation and the bed average temperature; and a coordinated control module, which executes the target temperature vector, the feeding rate and the operation parameter adjustment instruction; the present application solves the problem of incomplete gasification caused by temperature mismatch of different components by establishing different temperature intervals, realizes high-value utilization of waste circuit board resin, converts waste materials into synthetic gas with high added value, and realizes green recycling utilization of solid waste resources.

Description

Technical Field

[0001] This invention relates to the field of circulating fluidized bed technology, and more specifically, to a continuous vaporization control system for circuit board resin based on a circulating fluidized bed. Background Technology

[0002] With the rapid development of the electronics industry and the accelerated pace of electronic product upgrades, the amount of waste printed circuit boards (PCBs) is increasing year by year. PCB waste contains a large amount of organic resin materials, including epoxy resin, phenolic resin, polyimide resin, and other high-molecular polymers. These resin materials have high calorific value and chemical utilization value, and can be converted into high-value-added products such as syngas and hydrogen through gasification technology. Circulating fluidized bed gasification technology is widely used in the field of solid waste treatment due to its excellent heat and mass transfer characteristics, low operating temperature, and good adaptability.

[0003] Chinese Patent CN113625556A discloses an adaptive control method for a complex circulating fluidized bed industrial system. It combines an implicit generalized predictive control algorithm with online identification adaptive control to optimize the system, aiming to achieve adaptive control of complex industrial system models. Based on the CARIMA model, the algorithm employs long-term optimization performance indices and combines identification and self-correction mechanisms, exhibiting strong robustness and low model requirements, and has a wide range of applications. This algorithm overcomes the shortcomings of other adaptive algorithms such as generalized minimum variance and pole placement. The implicit algorithm does not identify the parameters of the object model; instead, it directly identifies and obtains the parameters in the optimal control law based on the input / output data. Therefore, it avoids the large amount of intermediate calculations involved in online solving of the Diophantine equations, reducing computational workload and saving time.

[0004] However, different resin components in circuit boards have different pyrolysis temperature ranges. These differences in pyrolysis characteristics necessitate the establishment of multiple temperature zones during the gasification process to achieve stratified and staged processing, thereby improving gasification efficiency and product quality. Existing circulating fluidized bed control technology cannot precisely regulate the different pyrolysis temperature requirements of multi-component resins and is ill-suited to the dynamic adjustment needs of process parameters caused by changes in the composition ratio of multi-component resins. Summary of the Invention

[0005] To solve the above problems, the present invention provides a continuous vaporization control system for circuit board resin based on a circulating fluidized bed.

[0006] This invention provides a continuous vaporization control system for circuit board resin based on a circulating fluidized bed, comprising:

[0007] The component identification module calculates the component concentration vector through an identification model.

[0008] The stratified temperature control module determines the temperature and boundary height of each temperature range based on the component concentration vector, calculates the target temperature vector, and collects the actual temperature to calculate the average bed temperature.

[0009] The intelligent feeding module calculates the composition of the target product based on the component concentration vector, and determines the feeding rate based on the component concentration vector and the composition of the target product.

[0010] The product optimization module calculates the gas concentration deviation based on the actual product composition and the target product composition, and generates operating parameter adjustment instructions based on the gas concentration deviation and the average bed temperature.

[0011] The coordination and control module executes commands to adjust the target temperature vector, feed rate, and operating parameters.

[0012] Furthermore, the steps to obtain the target temperature vector include:

[0013] The vaporization temperatures of each component, including epoxy resin, phenolic resin, and polyimide resin, were determined.

[0014] Establish temperature ranges, including low temperature, medium temperature and high temperature ranges. The default value of the center temperature of the low temperature range is the vaporization temperature of epoxy resin, the default value of the center temperature of the medium temperature range is the vaporization temperature of phenolic resin, and the default value of the center temperature of the high temperature range is the vaporization temperature of polyimide resin.

[0015] Determine the temperature distribution scheme based on the component concentration vector;

[0016] Multiple temperature measuring points are set along the height of the bed, and the temperature measuring points are evenly distributed. The temperature range to which the temperature measuring points belong is determined according to the temperature distribution scheme and the height of the temperature measuring points.

[0017] Calculate the target temperature of the temperature measuring point based on the temperature range to which the temperature measuring point belongs and the height of the temperature measuring point;

[0018] The target temperatures from multiple temperature measurement points form a target temperature vector.

[0019] Furthermore, the temperature distribution scheme is defined by the boundary heights of the temperature zones; a low-temperature zone is established at the bottom of the bed, corresponding to epoxy resin, and the range of the low-temperature zone is between the bottom of the bed and the boundary height of the bottom area; a medium-temperature zone is established in the middle of the bed, corresponding to phenolic resin, and the range of the medium-temperature zone is between the boundary height of the bottom area and the boundary height of the top area; a high-temperature zone is established at the top of the bed, corresponding to polyimide resin, and the range of the high-temperature zone is between the boundary height of the top area and the top of the bed.

[0020] When the mass fraction of epoxy resin or polyimide resin in the component concentration vector is lower than the preset mass fraction threshold, the height of the corresponding temperature range is directly set to the preset height, which is a preset multiple of the total height of the bed. At this time, the height of the bottom region boundary is the preset height, and the height of the top region boundary is the total height of the bed minus the preset height.

[0021] When the mass fraction of epoxy resin or polyimide resin in the component concentration vector is greater than or equal to the mass fraction threshold, the bottom region boundary height is calculated by multiplying the total bed height by the product of the epoxy resin mass fraction and the preset region adjustment coefficient; the top region boundary height is calculated by multiplying the total bed height, the polyimide resin mass fraction, and the preset region adjustment coefficient, and then subtracting the product from the total bed height to obtain the top region boundary height.

[0022] Furthermore, the steps for calculating the target temperature at the temperature measuring point include:

[0023] The target temperature for the temperature measuring point in the low-temperature zone is the epoxy resin vaporization temperature plus a height adjustment; the target temperature for the temperature measuring point in the medium-temperature zone is the phenolic resin vaporization temperature plus a height adjustment; and the target temperature for the temperature measuring point in the high-temperature zone is the polyimide resin vaporization temperature plus a height adjustment. The height adjustment is calculated as follows: the difference between the actual height of the temperature measuring point and the height of the zone center is multiplied by a preset height linear correction coefficient. The height of the zone center is the arithmetic mean of the boundary heights of the temperature zone to which the temperature measuring point belongs.

[0024] Furthermore, the feeding rate is the product of the preset baseline feeding rate, the component correction factor, and the product target correction factor. The component correction factor is the product of the preset epoxy resin correction coefficient and the epoxy resin mass fraction plus 1, then subtracted from the product of the preset polyimide correction coefficient and the polyimide mass fraction. The product target correction factor is the sum of 1 and the carbon monoxide deviation and the hydrogen deviation. The carbon monoxide deviation is the preset carbon monoxide correction coefficient multiplied by the carbon monoxide difference, where the carbon monoxide difference is the difference between the target molar fraction of carbon monoxide in the target product composition and the preset baseline molar fraction of carbon monoxide. The hydrogen deviation is the preset hydrogen correction coefficient multiplied by the hydrogen difference, where the hydrogen difference is the difference between the target molar fraction of hydrogen in the target product composition and the preset baseline molar fraction of hydrogen.

[0025] Furthermore, the target product composition includes a target mole fraction of carbon monoxide, a target mole fraction of hydrogen, and a target mole fraction of methane. The target mole fraction of carbon monoxide is calculated as follows: the product of the preset epoxy resin carbon monoxide contribution coefficient and the epoxy resin mass fraction, plus the product of the preset phenolic resin carbon monoxide contribution coefficient and the phenolic resin mass fraction, plus the product of the preset polyimide resin carbon monoxide contribution coefficient and the polyimide resin mass fraction. The target mole fraction of hydrogen is calculated as follows: the product of the preset epoxy resin hydrogen contribution coefficient and the epoxy resin mass fraction, plus the product of the preset phenolic resin hydrogen contribution coefficient and the phenolic resin mass fraction, plus the product of the preset polyimide resin hydrogen contribution coefficient and the polyimide resin mass fraction. The target mole fraction of methane is calculated as follows: the product of the preset epoxy resin methane contribution coefficient and the epoxy resin mass fraction, plus the product of the preset phenolic resin methane contribution coefficient and the phenolic resin mass fraction, plus the product of the preset polyimide resin methane contribution coefficient and the polyimide resin mass fraction.

[0026] Furthermore, the steps for generating operating parameter adjustment instructions include:

[0027] Calculate the gas concentration deviation, which includes the carbon monoxide mole fraction deviation and the hydrogen mole fraction deviation.

[0028] Based on the gas concentration deviation, the operating parameters are calculated, including air volume flow rate adjustment and steam volume flow rate adjustment.

[0029] The operating parameters are subjected to safety constraint checks. The operating parameters after the safety constraint checks are used to form operating parameter adjustment instructions, which include adjustments to air volume flow rate and steam volume flow rate.

[0030] Furthermore, the steps for calculating the operating parameters include:

[0031] The calculation method for air volume flow rate adjustment is: preset air reference volume flow rate plus the product of preset air flow rate adjustment coefficient and carbon monoxide mole fraction deviation;

[0032] The calculation method for adjusting the steam volumetric flow rate is: the preset steam reference volumetric flow rate plus the product of the preset steam flow rate adjustment coefficient and the hydrogen mole fraction deviation.

[0033] Furthermore, the steps for checking safety constraints include:

[0034] Read the preset safety constraint range, which includes the safety constraint range for air volume flow rate and the safety constraint range for steam volume flow rate; determine whether the operating parameters are within the safety constraint range. Compliant parameters are operating parameters that do not exceed the safety constraint range, while out-of-limit parameters are operating parameters that exceed the safety constraint range; perform amplitude limiting processing on the operating parameters, where compliant parameters do not require processing, and out-of-limit parameters are adjusted to the safety constraint range; output the amplitude-limited operating parameters as the operating parameter adjustment command.

[0035] Furthermore, the component concentration vector includes the mass fraction of each component, which includes the mass fraction of epoxy resin, phenolic resin, and polyimide resin. The mass fraction of each component represents the proportion of the component's mass to the total mass of the circuit board resin.

[0036] The beneficial effects of this invention are as follows: This invention establishes three dedicated temperature zones—low temperature zone, medium temperature zone, and high temperature zone—through a zoned temperature guarantee algorithm, which correspond to the vaporization temperatures of epoxy resin, phenolic resin, and polyimide resin, respectively. This improves the vaporization conversion rate of each component and solves the problem of incomplete vaporization caused by temperature mismatch between different components. Regardless of the ratio of epoxy resin to polyimide resin content, it can provide a dedicated temperature zone for each component to ensure complete vaporization.

[0037] This invention employs near-infrared spectroscopy combined with support vector machine algorithm to achieve quantitative identification of multiple components, improving identification accuracy and reducing single detection time; it establishes an intelligent mapping relationship between component concentration vectors and control parameters, enabling real-time response to changes in feed components; and through a multi-factor correction algorithm, it dynamically adjusts the feed rate according to the component ratio, improving the feed rate control accuracy.

[0038] This invention utilizes a dynamic region boundary adjustment mechanism to adjust the coverage of each functional region in real time based on the component concentration vector, establishes a minimum content component protection mechanism, and can still automatically allocate a dedicated temperature region when the mass fraction of any component is below 5%. It employs a distributed model predictive control algorithm to minimize temperature tracking error while meeting safety constraints. Through multi-parameter closed-loop feedback control, it achieves the target deviation in product composition control accuracy.

[0039] This invention achieves high-value utilization of waste circuit board resin through precise control, converting waste into high-value-added syngas and hydrogen, realizing economical operation, avoiding environmental pollution from traditional treatment methods, and achieving green recycling of solid waste resources. Attached Figure Description

[0040] Figure 1 This is a module example diagram of the circuit board resin continuous gasification control system based on a circulating fluidized bed according to the present invention.

[0041] Figure 2 This is an example diagram of the layered temperature control module of the circuit board resin continuous gasification control system based on a circulating fluidized bed according to the present invention.

[0042] Figure 3 This is an example diagram of the temperature distribution scheme of the circuit board resin continuous gasification control system based on a circulating fluidized bed according to the present invention.

[0043] Figure 4 This is an example diagram of the intelligent feeding module of the circuit board resin continuous gasification control system based on a circulating fluidized bed according to the present invention.

[0044] Figure 5 This is an example diagram of the product optimization module of the circuit board resin continuous gasification control system based on a circulating fluidized bed according to the present invention. Detailed Implementation

[0045] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, features described in some examples may be combined in other examples.

[0046] Example 1:

[0047] A continuous vaporization control system for circuit board resin based on a circulating fluidized bed, such as Figure 1 As shown, it includes:

[0048] The component identification module 101 is used for multi-component identification of circuit board resin to obtain component concentration vectors.

[0049] The component identification module, as the information source of the entire control system, uses a near-infrared spectroscopy detection system to scan the resin on the feeding circuit board and obtain the resin feature vector. This vector contains absorption intensity information at different wavelengths, and the components include epoxy resin, phenolic resin and polyimide resin.

[0050] Near-infrared spectroscopy is a non-destructive testing device based on the principle of molecular vibrational spectroscopy. It illuminates a sample with a near-infrared beam in the wavelength range of 1000-2500 nm and records the absorption intensity of light at different wavelengths, forming a characteristic spectrum. Different resin molecules have characteristic chemical bond vibrational frequencies. For example, assuming that the COC bond in epoxy resin has a characteristic absorption peak near 1200 nm, the C=C bond in phenolic resin has a characteristic absorption peak near 1600 nm, and the C=O bond in polyimide resin has a characteristic absorption peak near 1700 nm, the content of each component can be identified and quantified by detecting the intensity of these characteristic absorption peaks.

[0051] The resin feature vector is a data representation method that converts a continuous spectral signal into a discrete digital signal. Specifically, it is a vector composed of 301 spectral intensity values ​​obtained by sampling the wavelength range of 1000-2500nm at 5nm intervals. Each value represents the absorption intensity at the corresponding wavelength, and the value range is 0-1, where 0 represents complete transmission and 1 represents complete absorption.

[0052] The component concentration vector is calculated using an identification model. The vector includes the mass fraction of each component, comprising the mass fractions of epoxy resin, phenolic resin, and polyimide resin, with the sum of these three mass fractions equal to 1. The mass fraction of each component represents the proportion of that component's mass to the total resin mass of the circuit board, and its value ranges from 0 to 1.

[0053] The identification model is built based on the radial basis function kernel function and uses the support vector regression algorithm to predict continuous values. Model training includes the following steps:

[0054] Support Vector Regression (SVR) is a machine learning method based on statistical learning theory. It achieves a nonlinear mapping between input and output by finding the optimal hyperplane in a high-dimensional feature space. The radial basis function (RBF) is the core mathematical tool in SVR. The RBF can map linearly inseparable spectral feature data to a high-dimensional space, transforming the originally complex nonlinear relationship into a linearly separable problem, thereby achieving accurate component concentration prediction.

[0055] The actual components of standard samples were accurately determined using chemical analysis methods, including high-performance liquid chromatography-mass spectrometry (HPLC-MS). Simultaneously, near-infrared spectroscopy was employed to scan the same standard samples, acquiring resin feature vectors in the 1000-2500 nm wavelength range as training input data. A training sample library containing 200 known components was established. The mass fractions of epoxy resin varied from 0.1 to 0.8%, phenolic resin from 0.1 to 0.7%, and polyimide resin from 0.1 to 0.6%, ensuring the representativeness and coverage of the samples. Each training sample corresponds to a set of resin feature vectors and a set of actual component mass fractions, forming a complete input-output training data pair.

[0056] High-performance liquid chromatography-mass spectrometry (HPLC-MS) is a high-precision analytical chemistry method. HPLC separates components in a mixed resin sample based on differences in molecular weight and polarity, while mass spectrometry identifies and quantifies each component according to its mass-to-charge ratio, achieving a detection accuracy of over 99.5%. This method can accurately determine the actual mass fraction of epoxy resin, phenolic resin, and polyimide resin, providing standard reference values ​​for establishing training samples.

[0057] A grid search combined with cross-validation method is used to optimize the key parameters of the support vector regression algorithm. The optimized parameters include: a penalty parameter (search range 0.1-100, logarithmic step size); a radial basis function kernel parameter (search range 0.001-10, logarithmic step size); and an insensitive loss parameter. The search range is 0.001-0.1.

[0058] Grid search is an exhaustive parameter optimization method that tries different parameter combinations one by one at each grid point within a predefined parameter space, evaluates the model's performance under each parameter set, and finally selects the optimal parameter combination. The penalty parameter C controls the degree of error penalty; a larger C value indicates a lower tolerance for training error and higher model complexity. The radial basis function kernel parameter γ controls the influence range of a single training sample; a larger γ value indicates a smaller influence range of a single sample and a greater susceptibility to overfitting. The insensitive loss parameter... The width of the error tolerance pipe is defined, in Prediction errors within the specified range are not included in the loss function. A larger value indicates a higher tolerance for small errors.

[0059] Cross-validation is a model performance evaluation technique that involves splitting a dataset into multiple subsets and using different subsets alternately as the validation and training sets. The average of the validation results is then used as the model's performance metric. Specifically, 10-fold cross-validation involves randomly dividing 200 training samples into 10 subsets, each containing 20 samples. Ten rounds of training and validation are performed, with nine subsets used to train the model in each round, and the remaining subset used for validation. The arithmetic mean of the validation accuracies across the ten rounds is then used as the overall model performance metric.

[0060] The model performance was evaluated using 10-fold cross-validation. The training data was randomly divided into 10 parts, with 9 parts used as the training set and 1 part as the validation set. The average prediction accuracy was calculated. When the recognition accuracy reached 95% or higher and the root mean square error was less than 0.02, the optimal model parameters were saved.

[0061] The recognition model takes a preprocessed resin feature vector as input. After dimensionality reduction via principal component analysis, the resin feature vector contains 50 principal component eigenvalues, each corresponding to a combination of spectral information within a specific wavelength range. The recognition model outputs a component concentration vector, including the mass fractions of epoxy resin, phenolic resin, and polyimide resin. Data precision is retained to three decimal places.

[0062] Principal component analysis (PCA) is a linear dimensionality reduction technique that compresses data dimensionality by finding the direction with the largest variance, while preserving as much information as possible from the original data. For a 301-dimensional resin eigenvector, PCA calculates the eigenvalues ​​and eigenvectors of the covariance matrix, selects the eigenvectors corresponding to the top 50 largest eigenvalues ​​as new basis vectors, and projects the original 301-dimensional spectral data into a 50-dimensional principal component space. Each principal component eigenvalue represents the variance of the data along that principal component direction; the larger the variance, the richer the spectral information contained in that direction. For example, the first principal component may mainly reflect the absorption characteristics around 1200 nm, and the second principal component may mainly reflect the absorption characteristics around 1600 nm. By linearly combining the 50 principal components, the main information of the original spectrum can be reconstructed, while significantly reducing data dimensionality and computational complexity.

[0063] The component concentration vector is transmitted in real time to the stratified temperature control module and the intelligent feeding module via industrial Ethernet. The transmission delay is less than the preset transmission delay upper limit, the default value of which is 50ms. The data update frequency is the preset data update interval, the default value of which is once every 10 seconds, which meets the real-time control requirements.

[0064] Through the above implementation, the component identification module can accurately and quickly identify the multi-component composition of the circuit board resin, providing reliable data support for subsequent control modules. The identification accuracy of the component identification module is greater than the preset identification accuracy requirement, the default value of which is 95%. The single detection time is less than the preset detection time requirement, the default value of which is 30 seconds, meeting the real-time control requirements of the continuous gasification process.

[0065] The stratified temperature control module 102 determines the temperature and boundary height of each temperature range based on the component concentration vector, calculates the target temperature vector, and collects the actual temperature to calculate the average temperature of the bed.

[0066] The layered temperature control module serves as the core unit for temperature setting, such as... Figure 2 As shown, the component concentration vector output by the receiving component identification module is used to calculate the precise target temperature at each bed height through a partitioned temperature guarantee algorithm combined with a dynamic boundary adjustment method.

[0067] The zoned temperature guarantee algorithm is a temperature stratification control method based on the differences in material properties. It divides the fluidized bed into multiple temperature zones along its height, setting different target temperatures for each zone to accommodate the vaporization temperature requirements of different resin components. The core idea of ​​the algorithm is to process the mixed raw materials vertically according to a temperature gradient. Lower-temperature zones primarily handle easily vaporized components, while higher-temperature zones primarily handle difficult-to-vaporize components, ensuring that all components achieve complete vaporization under optimal temperature conditions.

[0068] The dynamic boundary adjustment method is a control strategy that adjusts the boundary position of each temperature range in real time according to the changes in the concentration of the feed components. When the content of a certain component is high, the height range of the temperature range corresponding to that component is increased accordingly; when the content of a certain component is low, the height range of the temperature range corresponding to that component is decreased accordingly. At the same time, it ensures that each temperature range has a minimum height requirement to avoid any component lacking sufficient reaction space.

[0069] Based on the thermal decomposition kinetics and gasification reaction mechanisms of different resin components, the gasification temperatures of each component were determined. The default gasification temperature for epoxy resin is 450℃. At this temperature, efficient gasification is achieved primarily through ether bond cleavage, epoxy group ring-opening, and CO bond cleavage, combined with steam reforming and partial oxidation. The default gasification temperature for phenolic resin is 550℃. Complete gasification occurs through reactions such as phenolic hydroxyl dehydration, methylene bridge cleavage, and benzene ring side chain cleavage, combined with high-temperature gasification. The default gasification temperature for polyimide resin is 700℃. At the specified gasification temperature, imide ring opening and main chain CN bond cleavage are achieved, followed by subsequent gasification conversion reactions, resulting in complete gasification. These temperature values ​​are based on gasification reaction kinetics and industrial practice verification, ensuring that each component exhibits the highest gasification conversion rate and optimal product distribution at its corresponding temperature.

[0070] Thermal decomposition kinetics refers to the rate and temperature dependence of molecular bond breakage in organic compounds during heating, typically described by the Arrhenius equation. Different resins have different activation energies: epoxy resin has an activation energy of approximately 180 kJ / mol, phenolic resin approximately 220 kJ / mol, and polyimide approximately 280 kJ / mol. Higher activation energies indicate more stable molecular bonds, requiring higher temperatures for effective breakage.

[0071] The gasification reaction mechanism refers to the chemical reaction pathway and mechanism by which organic matter is converted into syngas under high temperature and the action of a gasifying agent. It mainly includes pyrolysis, gasification, combustion, and water-gas shift reactions. Pyrolysis is the process of organic matter decomposing into coke and volatiles at high temperature; gasification includes the reaction of coke with carbon dioxide to produce carbon monoxide, and the reaction of coke with water vapor to produce carbon monoxide and hydrogen; combustion is the reaction of coke with oxygen to produce carbon dioxide; and water-gas shift reaction is the reaction of carbon monoxide with water vapor to produce carbon dioxide and hydrogen. Different resins have different reaction pathways. Epoxy resins, due to their high oxygen content, are mainly converted through partial oxidation and steam reforming reactions; phenolic resins, due to their stable benzene ring structure, require higher-temperature gasification reactions for effective decomposition; and polyimides, due to their complex nitrogen-containing heterocyclic structure, involve more steps and intermediate products in their gasification process.

[0072] To ensure complete vaporization of all components at suitable temperatures, temperature zones are established, including a low-temperature zone, a medium-temperature zone, and a high-temperature zone. The low-temperature zone is specifically for epoxy resin, with its default center temperature being the epoxy resin vaporization temperature. The medium-temperature zone is specifically for phenolic resin, with its default center temperature being the phenolic resin vaporization temperature. The high-temperature zone is specifically for polyimide, with its default center temperature being the polyimide resin vaporization temperature. Regardless of variations in the mass fraction of the feed components, the system must maintain these temperature zones within the bed to ensure each component has its optimal vaporization environment.

[0073] The temperature distribution scheme is determined based on the component concentration vector. This scheme includes methods for determining the boundary heights of the bottom and top regions, such as... Figure 3 As shown, a low-temperature zone is established at the bottom of the bed, corresponding to epoxy resin, and its range is between the height of the bottom zone and the boundary of the bottom area. A medium-temperature zone is established in the middle of the bed, corresponding to phenolic resin, and its range is between the height of the bottom zone and the boundary of the top zone. A high-temperature zone is established at the top of the bed, corresponding to polyimide resin, and its range is between the height of the top zone and the top of the bed. Since the various resin components are mixed during feeding, differentiated treatment of different components is achieved by establishing a stratified temperature gradient. A low-temperature zone of 450°C is established at the bottom of the bed to mainly vaporize epoxy resin, a medium-temperature zone of 550°C is established in the middle of the bed to mainly vaporize phenolic resin, and a high-temperature zone of 700°C is established at the top of the bed to mainly vaporize polyimide. As the mixture rises layer by layer along the height direction in the fluidized bed, each component preferentially undergoes a vaporization reaction within its corresponding temperature range according to its vaporization temperature characteristics.

[0074] When the mass fraction of epoxy resin or polyimide resin in the component concentration vector is lower than a preset mass fraction threshold, the height of the corresponding region boundary is determined by directly setting the preset height to cover the temperature range corresponding to that component. The preset height is a preset multiple of the total bed height, with a default value of 0.15. The total bed height is the vertical height from the bottom air distribution plate to the top of the bed in the circulating fluidized bed reactor. At this time, the bottom region boundary height is the preset height, and the top region boundary height is the total bed height minus the preset height. The default value of the preset mass fraction threshold is 0.25, or 25%. This ensures that even with a low component content, there is still sufficient reaction space for effective gasification. The same mass fraction threshold is used for both epoxy resin and polyimide resin because the minimum effective reaction space requirements for both resins in the fluidized bed are essentially the same, requiring at least 15% of the bed height to ensure sufficient gas-solid contact and heat and mass transfer. A unified mass fraction threshold simplifies the control algorithm logic and avoids the complexity and potential conflicts caused by multiple threshold judgments.

[0075] When the mass fraction of epoxy resin or polyimide resin in the component concentration vector is greater than or equal to a preset mass fraction threshold, the corresponding region boundary height is adjusted in real time according to the component mass fraction. The bottom region boundary height is calculated as follows: the total bed height multiplied by the product of the epoxy resin mass fraction and a preset region adjustment coefficient. The default value of the preset region adjustment coefficient is 0.6, and the value range is 0.3 to 0.8. The top region boundary height is calculated as follows: the total bed height, the polyimide mass fraction, and the preset region adjustment coefficient are multiplied, and the resulting product is subtracted from the total bed height to obtain the top region boundary height.

[0076] The remaining part is the medium-temperature zone, which is maintained at a height of no less than 20% of the bed layer between the bottom and top boundary heights.

[0077] By using the above allocation method, the temperature range boundary can be flexibly adjusted according to the actual content of each component, which not only ensures the full gasification of high-content components, but also reserves the necessary reaction space for low-content components, thereby improving the overall gasification efficiency and product quality.

[0078] The circulating fluidized bed gasifier has multiple temperature control zones, with n temperature measuring points set along the bed height direction, where n is the total number of temperature measuring points, ranging from 8 to 12. The temperature measuring points are evenly distributed, and the spacing between the temperature measuring points is represented by the height spacing, where i represents the sequence number from 1 to n.

[0079] After determining the temperature allocation scheme, a temperature measurement point allocation algorithm is used to assign target temperature values ​​to each of the n temperature measurement points along the bed height direction. The temperature measurement point allocation algorithm includes the following core steps:

[0080] Based on the relative height of each temperature measuring point within the bed and the temperature distribution scheme, the temperature range to which it belongs is determined. Let the total height of the bed be H, and the height position of the i-th temperature measuring point be . Where i ranges from 1 to n, The bottom of the bed, The top of the bed is considered the temperature measuring point. The height of the temperature measuring points is calculated according to the principle of uniform distribution along the bed height. That is, the height of the i-th temperature measuring point is equal to (temperature measuring point number minus 1) multiplied by the total bed height and then divided by (total number of temperature measuring points minus 1), ensuring that the temperature measuring points are evenly distributed along the bed height. When the height of a temperature measuring point is less than or equal to the boundary height of the bottom area, it belongs to the low-temperature zone; when the height of a temperature measuring point is greater than or equal to the boundary height of the top area, it belongs to the high-temperature zone; the remaining temperature measuring points belong to the medium-temperature zone.

[0081] The reference temperature for each temperature measurement point is determined based on the center temperature of that region. The reference temperature ensures that each component achieves the highest gasification conversion rate and optimal product distribution within its corresponding region.

[0082] Based on the baseline temperature, fine-tuning is performed according to the specific height location of the temperature measuring points to establish a reasonable temperature gradient distribution along the height. The height adjustment is calculated as follows: the difference between the actual height of the temperature measuring point and the height of the zone center is multiplied by a preset height linear correction coefficient, the default value of which is 15℃ / m. The zone center height is defined as the arithmetic mean of the boundary heights of the temperature interval to which the temperature measuring point belongs. Through height fine-tuning, different temperature measuring points within the same area have appropriate temperature differences, better adapting to the heat transfer characteristics and material distribution patterns inside the fluidized bed.

[0083] Target temperatures are set according to the temperature range to which each temperature measuring point belongs. The target temperature for the low-temperature measuring point is set to the epoxy resin vaporization temperature plus the height adjustment; the target temperature for the medium-temperature measuring point is set to the phenolic resin vaporization temperature plus the height adjustment; and the target temperature for the high-temperature measuring point is set to the polyimide resin vaporization temperature plus the height adjustment.

[0084] During the target temperature calculation process, safety constraint checks are automatically performed. The target temperature for each temperature measuring point must be within the range of 250℃ to 750℃; if it exceeds this range, automatic temperature limiting is applied. The temperature difference between adjacent temperature measuring points must not exceed the preset gradient, with a default value of 50℃, ensuring the smoothness of the bed temperature distribution and operational safety. When the target temperature difference between adjacent temperature measuring points is detected to exceed the preset gradient, the temperature smoothing adjustment for the out-of-limit range is automatically performed, prioritizing the adjustment of temperature measuring points in the medium-temperature zone. Specifically, if the out-of-limit range includes temperature measuring points in the medium-temperature zone, the target temperature of the medium-temperature zone temperature measuring points is first adjusted so that its temperature difference with adjacent temperature measuring points does not exceed the preset gradient. If multiple adjustments are required, the medium-temperature zone and its adjacent temperature measuring points are adjusted sequentially until the temperature difference between all adjacent temperature measuring points is within the preset gradient. If the out-of-limit range does not include temperature measuring points in the medium-temperature zone, the target temperature of the upper-layer temperature measuring points is adjusted to the target temperature of the lower-layer temperature measuring points plus the preset gradient, and this process is repeated upwards.

[0085] The target temperature vector consists of the target temperatures of each temperature measurement point. The data accuracy is retained to one decimal place and is output at a frequency of once every 10 seconds to guide subsequent temperature control and optimization.

[0086] The layered temperature control module integrates a high-precision temperature monitoring system, with multiple high-precision temperature sensors arranged along the height of the bed to collect the actual temperature at each measuring point in real time. The temperature sampling frequency is once every 5 seconds, and the collected temperature data from each measuring point forms an actual temperature vector, with data accuracy retained to one decimal place. The average bed temperature is calculated by taking the arithmetic mean of the actual temperatures at all measuring points, serving as a comprehensive indicator of the overall thermodynamic state of the system. To ensure the reliability of data transmission, feedback data employs a dual-channel redundancy mechanism: the primary channel transmits via industrial Ethernet, and the backup channel transmits via fieldbus. The actual temperature vector and the average bed temperature are transmitted in real time to the intelligent feeding module and product optimization module, with a transmission delay of less than 30ms, ensuring that subsequent control modules can obtain accurate and timely temperature status information, forming a complete temperature closed-loop control system.

[0087] Through the aforementioned zoned temperature guarantee algorithm and real-time temperature monitoring mechanism, the stratified temperature control module ensures that all resin components are completely vaporized at the optimal temperature, thoroughly solving the problem of incomplete vaporization caused by differences in component mass fraction. Whether the epoxy resin mass fraction is as high as 95% or the polyimide mass fraction is only 3%, the system can provide a dedicated temperature range for each component to ensure complete vaporization and meet the stringent requirements for complete vaporization of multi-component resins.

[0088] The intelligent feeding module 103 calculates the composition of the target product based on the component concentration vector and determines the feeding rate based on the component concentration vector and the composition of the target product.

[0089] The intelligent feeding module serves as the core unit for feeding control, such as... Figure 4 As shown, the system receives the component concentration vector provided by the component identification module and dynamically calculates the target product composition parameters based on the component concentration vector. It also dynamically adjusts the feed rate according to the resin component characteristics and product quality requirements to ensure the stability of the gasification process and the consistency of product quality. The target product composition includes the target mole fractions of carbon monoxide, hydrogen, and methane.

[0090] Because different circuit board resin components have different pyrolysis reaction mechanisms and product distribution characteristics, the target mole fraction should be dynamically calculated based on the component concentration vector. The calculation method for the target mole fraction of carbon monoxide is as follows: the product of the preset epoxy resin carbon monoxide contribution coefficient and the epoxy resin mass fraction, plus the product of the preset phenolic resin carbon monoxide contribution coefficient and the phenolic resin mass fraction, plus the product of the preset polyimide resin carbon monoxide contribution coefficient and the polyimide resin mass fraction; where the default value for the preset epoxy resin carbon monoxide contribution coefficient is 0.28, the default value for the preset phenolic resin carbon monoxide contribution coefficient is 0.24, and the default value for the preset polyimide resin carbon monoxide contribution coefficient is 0.22. The target mole fraction of hydrogen is calculated as follows: the product of the preset hydrogen contribution coefficient of epoxy resin and the mass fraction of epoxy resin, plus the product of the preset hydrogen contribution coefficient of phenolic resin and the mass fraction of phenolic resin, plus the product of the preset hydrogen contribution coefficient of polyimide resin and the mass fraction of polyimide resin; where the default values ​​for the preset hydrogen contribution coefficient of epoxy resin are 0.16, the default values ​​for the preset hydrogen contribution coefficient of phenolic resin are 0.19, and the default values ​​for the preset hydrogen contribution coefficient of polyimide resin are 0.20. The target mole fraction of methane is calculated as follows: the product of the preset methane contribution coefficient of epoxy resin and the mass fraction of epoxy resin, plus the product of the preset methane contribution coefficient of phenolic resin and the mass fraction of phenolic resin, plus the product of the preset methane contribution coefficient of polyimide resin and the mass fraction of polyimide resin; where the default values ​​for the preset methane contribution coefficient of epoxy resin are 0.09, the default values ​​for the preset methane contribution coefficient of phenolic resin are 0.08, and the default value for the preset methane contribution coefficient of polyimide resin are 0.06.

[0091] The contribution coefficients of each component product were determined based on the chemical reaction mechanism. Epoxy resin mainly produces more oxygen-containing precursors through ether bond cleavage and epoxy group ring-opening reactions, which is conducive to carbon monoxide generation, but the hydrogen yield is relatively low. Phenolic resin has a high hydrogen yield and a moderate carbon monoxide yield through phenolic hydroxyl dehydration and methylene bridge cleavage reactions. Polyimide resin has the highest hydrogen yield due to its nitrogen-containing molecular structure, through imide ring opening and main chain CN bond cleavage, but the methane yield is relatively low. The target product composition parameters were determined based on the pyrolysis characteristics of different components, downstream process requirements, and the principle of maximizing economic benefits, achieving adaptive matching between changes in raw material composition and product targets.

[0092] The product contribution coefficient refers to the contribution of a unit mass of a certain resin component to the mole fraction of a specific gas component after complete vaporization. It is determined through a combination of statistical analysis of extensive experimental data and theoretical calculations. The determination process involves conducting vaporization experiments on pure epoxy resin, pure phenolic resin, and pure polyimide resin under standard vaporization conditions, measuring the mole fraction of each gas component in the products, and taking the average value after multiple repeated experiments as the product contribution coefficient for that component. These coefficients reflect the intrinsic influence of different resin molecular structures on the distribution of vaporization products.

[0093] The standard gasification conditions are: a gasification temperature of 750℃±10℃, an operating pressure of atmospheric pressure (101.3 kPa±2 kPa), a gasifying agent composition including an air volumetric flow rate of 2.0 standard cubic meters per minute and a water vapor volumetric flow rate of 0.8 standard cubic meters per minute, a bed material of quartz sand, and a feed rate of 0.6 kg / min. The standard gasification conditions were determined based on typical operating parameters of the circulating fluidized bed gasification process and industrial experience. Under these conditions, various resin components can achieve stable gasification reactions, and the product composition distribution exhibits good reproducibility and comparability. The standard temperature of 750℃ ensures complete gasification conversion of all resin components; atmospheric pressure operation simplifies equipment design and operational complexity; the air-to-water vapor ratio achieves the optimal oxygen equivalence ratio and steam-to-carbon ratio, guaranteeing the completeness of the gasification reaction and product quality; quartz sand as the bed material provides good fluidization characteristics and heat transfer performance; and the standard feed rate ensures stable residence time and mass transfer conditions. The product contribution coefficient database established under standard gasification conditions provides reliable basic data for predicting and controlling product composition under different operating conditions.

[0094] The preset baseline feed rate is a fundamental parameter for feed control. The default value for the preset baseline feed rate is 0.6 kg / min. This value is determined based on the design capacity of the fluidized bed gasifier, the fluidization characteristics of the bed material, and the balance of heat and mass transfer. The determination of the preset baseline feed rate considers multiple factors: the gas-solid contact efficiency within the fluidized bed is optimal at this rate; the bed temperature distribution is uniform, and the temperature gradient is controlled within the design range; the composition of the gasification products is stable, with the mole fraction fluctuation of the main gas components less than ±2%; and the system's energy consumption to capacity ratio meets economic operation requirements. As the basis for correction calculations, the preset baseline feed rate ensures the stability and reliability of feed control under various operating conditions.

[0095] The component correction factor reflects the influence of different resin components on the gasification reaction rate and product distribution. The calculation method is as follows: the product of the preset epoxy resin correction factor and the epoxy resin mass fraction is added to 1, and then the product of the preset polyimide correction factor and the polyimide mass fraction is subtracted. The default value of the preset epoxy resin correction factor is 0.3, and the default value of the preset polyimide correction factor is 0.2. The component correction mechanism is based on the differences in the thermal decomposition characteristics of the three resin components. Epoxy resin has a relatively low vaporization temperature, at which ether bonds and epoxy groups are easily broken, resulting in a fast decomposition rate and large gas production. Therefore, when the mass fraction of epoxy resin increases, the feed rate needs to be appropriately increased to fully utilize its excellent vaporization performance. The preset epoxy resin correction coefficient reflects a positive promoting effect. Polyimide has a higher vaporization temperature, requiring high temperatures to achieve ring opening and main chain breakage, making decomposition difficult and the reaction rate slow. When the mass fraction of polyimide increases, the feed rate needs to be reduced to ensure sufficient reaction. The preset polyimide correction coefficient reflects a negative restrictive effect. Phenolic resin has a moderate vaporization temperature, vaporizing through methylene bridge cleavage and phenolic hydroxyl group dehydration. Its impact on the feed rate is relatively neutral, therefore no specific correction term is set. The component correction strategy achieves adaptive adjustment of the feed rate to changes in raw material composition.

[0096] The product target correction factor adjusts the feed rate based on dynamically calculated target product composition, achieving closed-loop control of product quality. The calculation method is: 1 + the sum of carbon monoxide deviation and hydrogen deviation; where the carbon monoxide deviation is calculated as: a preset carbon monoxide correction coefficient multiplied by the carbon monoxide difference, where the carbon monoxide difference is the difference between the target carbon monoxide mole fraction and the preset carbon monoxide baseline mole fraction; the hydrogen deviation is calculated as: a preset hydrogen correction coefficient multiplied by the hydrogen difference, where the hydrogen difference is the difference between the target hydrogen mole fraction and the preset hydrogen baseline mole fraction. The default values ​​for the preset carbon monoxide correction coefficient are 0.1, the preset hydrogen correction coefficient is 0.15, the preset carbon monoxide baseline mole fraction is 0.26, and the preset hydrogen baseline mole fraction is 0.18. The preset carbon monoxide and hydrogen reference molar fractions are reference target values ​​based on standard component ratios. These are used to assess the impact of deviations between the current component ratio and the standard ratio on the feed rate. The standard component ratio consists of 50% epoxy resin, 30% phenolic resin, and 20% polyimide. Correction coefficients reflect the responsiveness of the two gases to changes in the feed rate. When the dynamically calculated target product composition deviates from the reference target, adjusting the feed rate indirectly affects the residence time and reaction extent, achieving adaptive adjustment for different raw material component ratios. This strategy organically combines feed control with dynamic product targets, improving the overall control system's adaptability to changes in raw material composition.

[0097] The final feed rate is calculated by multiplying the baseline feed rate by two correction factors. The calculation method is as follows: the feed rate is the product of the baseline feed rate, the component correction factor, and the product target correction factor. This multi-factor correction method ensures that the feed rate can simultaneously respond to changes in raw material characteristics, fluctuations in operating conditions, and product quality requirements, achieving multi-objective coordinated optimization. The calculated feed rate data is retained to two decimal places, in kg / min. To ensure operational safety and equipment protection, the system sets safety constraints on the feed rate: a minimum feed rate of 0.1 kg / min to prevent bed instability; and a maximum feed rate of 1.2 kg / min to avoid overload operation. Automatic limiting and alarm signals are triggered when the feed rate exceeds the safety range.

[0098] The calculated feed rate control command is transmitted in real time to the coordination control module via the industrial control network, with a transmission delay of less than 30ms and a data update frequency of once every 10 seconds, maintaining synchronization with the component identification module. The system establishes a feed rate variation constraint mechanism, ensuring that the feed rate variation between adjacent time points does not exceed ±10%, guaranteeing the stability and continuity of the feeding process. Simultaneously, the system records the history of feed rate changes, providing data support for system optimization and fault diagnosis.

[0099] Through the above implementation, the intelligent feeding module can accurately calculate the optimal feeding rate based on real-time component information, temperature conditions, and product requirements, achieving intelligent and adaptive control of the feeding process. The system response time is ≤10 seconds, and the feeding rate control accuracy is ≤±5%, meeting the stable control requirements of the continuous gasification process.

[0100] The product optimization module 104 calculates the gas concentration deviation based on the actual product composition and the target product composition, and generates an operation parameter adjustment command based on the gas concentration deviation and the average bed temperature.

[0101] The product optimization module serves as the core unit for product quality control, such as... Figure 5 As shown, the system receives the actual product composition detected by the gas component analyzer, the average bed temperature monitored by the stratified temperature control module, and the target product composition calculated by the intelligent feeding module. Through a multi-parameter closed-loop feedback control algorithm, key operating parameters are dynamically adjusted to achieve precise control and continuous optimization of the gasification product composition towards the dynamic target value. The actual product composition data is continuously obtained through online gas component analyzer detection, including the actual mole fractions of carbon monoxide, hydrogen, and methane. The data update frequency is once every 20 seconds, and the data accuracy is retained to three decimal places. The gas component analyzer uses Fourier transform infrared spectroscopy technology, with a detection accuracy ≥98% and a response time ≤60 seconds, meeting the real-time control requirements.

[0102] Based on a comparative analysis of the actual product composition and the dynamic target composition, gas concentration deviations are calculated, including carbon monoxide mole fraction deviations and hydrogen mole fraction deviations. The carbon monoxide mole fraction deviation is calculated as follows: the dynamically calculated target carbon monoxide mole fraction minus the actual carbon monoxide mole fraction. The hydrogen mole fraction deviation is calculated as follows: the dynamically calculated target hydrogen mole fraction minus the actual hydrogen mole fraction. A positive deviation indicates that the actual concentration is lower than the target value, requiring adjustment of operating parameters to increase the formation of that component. A negative deviation indicates that the actual concentration is higher than the target value, requiring adjustment of parameters to suppress the excessive formation of that component.

[0103] Based on the gas concentration deviation results, a separate adjustment strategy is adopted to coordinate the adjustment of two key operating parameters. Methane treatment is not included in the operating parameter adjustment strategy. Air volumetric flow rate adjustment is for controlling carbon monoxide formation. The adjustment method is as follows: based on the preset air reference volumetric flow rate, add the product of the preset air flow rate adjustment coefficient and the carbon monoxide mole fraction deviation. The default value of the preset air reference volumetric flow rate is 2.0 standard cubic meters per minute, and the default value of the preset air flow rate adjustment coefficient is 1.0 standard cubic meters per minute. The air volumetric flow rate adjustment mechanism is based on the optimal equivalence ratio control of the gasification reaction. Appropriately increasing the air flow rate within the optimal equivalence ratio range promotes the carbon gasification reaction to generate carbon monoxide, which is beneficial to carbon monoxide formation. Excessive air is avoided to prevent over-oxidation and prevent carbon monoxide from being further oxidized to carbon dioxide. The optimal air equivalence ratio is usually controlled between 0.25 and 0.35 to ensure sufficient gasification while avoiding over-oxidation. Steam volumetric flow rate adjustment controls hydrogen production. The method is as follows: Add the product of a preset steam flow rate adjustment coefficient and the hydrogen mole fraction deviation to the preset steam baseline volumetric flow rate. The default value for the preset steam baseline volumetric flow rate is 0.8 standard cubic meters per minute, and the default value for the preset steam flow rate adjustment coefficient is 1.5 standard cubic meters per minute. Steam volumetric flow rate adjustment is based on the water-gas shift reaction and steam reforming reaction. Increasing the steam flow rate promotes these reactions, increasing hydrogen production. The steam reforming reaction involves carbon reacting with water vapor to produce carbon monoxide and hydrogen, while the water-gas shift reaction involves carbon monoxide reacting with water vapor to produce carbon dioxide and hydrogen. Decreasing the steam flow rate inhibits these reactions, controlling excessive hydrogen production. It should be noted that the water-gas shift reaction consumes carbon monoxide while increasing hydrogen production; therefore, a comprehensive consideration of product composition balance is necessary.

[0104] Methane generation control is primarily achieved through temperature regulation, and the stratified temperature control module provides comprehensive temperature control capabilities through a zoned temperature guarantee algorithm and a target temperature vector generation mechanism. The coordinated control module employs a distributed model predictive control algorithm for precise temperature optimization, dynamically adjusting the target temperature at each temperature measurement point based on the vaporization temperature of each component, thus achieving precise control of the methanation reaction temperature conditions. Furthermore, based on the pyrolysis mechanism and product distribution characteristics of each resin component, the methane generated during the circuit board resin vaporization process is relatively small, with a target molar fraction typically between 0.06 and 0.09. Compared to 0.22-0.28 for carbon monoxide and 0.16-0.20 for hydrogen, methane, as a minor product, has a relatively small impact on overall product quality due to concentration variations. Adding temperature adjustment to the main reaction zone in the product optimization module would conflict with the existing stratified temperature control system, potentially leading to instability. Therefore, methane component control relies entirely on the existing stratified temperature control system, eliminating the need for dedicated temperature adjustment parameters in the product optimization module, thus ensuring the consistency of the control strategy and system stability.

[0105] To ensure that the adjusted operating parameters are within the safe operating range of the equipment, a comprehensive safety constraint check should be established. The safety constraint check execution process includes:

[0106] The received operating parameters undergo data format verification and numerical validity pre-checks to ensure they are within reasonable physical ranges. Pre-set safety constraint ranges are read, including air volume flow rate and steam volume flow rate safety constraint ranges. The operating parameters are then sequentially checked for compliance with these constraints. For air volume flow rate adjustments, the range between minimum and maximum air flow rates is checked; for steam volume flow rate adjustments, the range between minimum and maximum steam flow rates is checked. Based on the check results, the operating parameters are categorized as compliant, slightly exceeding limits, or severely exceeding limits. Compliant parameters are those that do not exceed safety constraint ranges and are directly... Upon inspection, slightly excessive parameters are those that exceed the safety constraints but the extent of the excess is less than the preset excess threshold, which is 5% of the safety constraints. Severely excessive parameters are those that exceed the safety constraints and the extent of the excess is greater than or equal to the preset excess threshold. Automatic limiting is performed on the excessive parameters. Compliant parameters do not require processing. The adjustment range for slightly excessive parameters is a preset adjustment multiple of the excess amount, with a default value of 80% to retain a certain safety margin. The excess amount is the difference between the actual operating parameter and the safety constraints. Severely excessive parameters are directly adjusted to the corresponding safety constraints, i.e., the minimum or maximum value. The output, after constraint checking and limiting, is the operating parameter adjustment instruction.

[0107] The adjustment range refers to the degree to which out-of-limit parameters are adjusted back to the safe range. The specific calculation method is as follows: When the operating parameter is greater than the upper limit of the safe constraint range, the adjustment range equals the excess amount multiplied by a preset adjustment multiple, and the adjusted operating parameter value equals the upper limit of the safe constraint range minus the adjustment range. When the operating parameter is less than the lower limit of the safe constraint range, the adjustment range equals the excess amount multiplied by a preset adjustment multiple, and the adjusted operating parameter value equals the lower limit of the safe constraint range plus the adjustment range. Adjusting to the corresponding safe constraint range involves directly setting the out-of-limit operating parameter to the nearest safe boundary value. Specifically, when the operating parameter is greater than the upper limit of the safe constraint range, the operating parameter value is directly adjusted to the maximum value of the safe constraint range; when the operating parameter is less than the lower limit of the safe constraint range, the operating parameter value is directly adjusted to the minimum value of the safe constraint range, ensuring that the adjusted operating parameter strictly falls within the safe constraint range. The calculation method for exceeding the limit is as follows: when the operating parameter is greater than the upper limit, the exceeding limit is equal to the actual operating parameter value minus the maximum value of the safety constraint range; when the operating parameter is less than the lower limit, the exceeding limit is equal to the minimum value of the safety constraint range minus the actual operating parameter value. The exceeding limit is always a positive value, reflecting the degree to which the operating parameter deviates from the safety range.

[0108] The air volumetric flow rate safety constraints include minimum and maximum air flow rates. The default minimum air flow rate is 1.0 standard cubic meters per minute, and the default maximum air flow rate is 4.0 standard cubic meters per minute. These constraints are determined based on the uniformity of airflow distribution and combustion stability of the fluidized bed gasifier. Insufficient air flow rate may lead to incomplete gasification and carbon buildup, while excessive air flow rate may cause over-oxidation and hot spot formation. The steam volumetric flow rate safety constraints include minimum and maximum steam flow rates. The default minimum steam flow rate is 0.2 standard cubic meters per minute, and the default maximum steam flow rate is 2.0 standard cubic meters per minute. These constraints consider the optimal ratio of steam to feedstock and heat and mass transfer efficiency. Insufficient steam will affect the completeness of the gasification reaction, while excessive steam will reduce bed temperature and gasification efficiency.

[0109] Through the detailed safety constraint check and execution process described above, the system ensures the absolute safety and reliability of the operating parameter adjustment process, effectively preventing equipment damage and process abnormalities caused by parameter exceeding limits, and providing a reliable safety guarantee for the stable operation of the gasification process.

[0110] After safety constraint checks, the operating parameters are converted into operating parameter adjustment commands, including two control commands: air volumetric flow rate adjustment and steam volumetric flow rate adjustment. The adjustment command data is accurate to two decimal places, with units of standard cubic meters per minute. To ensure control stability and continuity, the system sets parameter change rate constraints, ensuring that the change in each parameter during adjacent adjustment cycles does not exceed ±10% of the baseline value, preventing drastic parameter fluctuations from adversely affecting the stability of the gasification process. The adjustment commands are transmitted in real-time to the coordination control module via the industrial control network, using the industrial Ethernet standard transmission protocol with a transmission delay of less than 50ms and a data update frequency of once every 30 seconds, synchronized with the product composition detection frequency. A redundant communication mechanism is used during transmission, with the main channel and backup channel transmitting simultaneously to ensure the reliability of command transmission. Upon receiving the adjustment commands, the coordination control module integrates them into the overall control strategy for execution, achieving coordinated unification of product composition optimization, temperature control, and feed control.

[0111] Through the above implementation method, the product optimization module can accurately adjust key operating parameters based on real-time product composition detection results, realize closed-loop optimization control of gasification product composition, and meet the stable production requirements of high-quality gasification products.

[0112] The coordination control module 105 executes the target temperature vector, feed rate and operating parameter adjustment instructions.

[0113] As the final execution core of the system, the coordination and control module receives the target temperature vector output by the layered temperature control module, the feeding rate determined by the intelligent feeding module, and the operation parameter adjustment instructions issued by the product optimization module. It adopts a unified execution strategy to directly transmit each control instruction to the corresponding actuator, ensuring the stable operation of the entire gasification process.

[0114] A distributed control unit (DCU) is a control system architecture based on fieldbus technology, consisting of multiple independent controller nodes. Each node is responsible for the control tasks of a specific area or device, and the nodes communicate and coordinate with each other via industrial Ethernet. In this system, the temperature control unit is responsible for controlling the heaters at each temperature measuring point, the feeding control unit is responsible for controlling the feeding screw and frequency converter, and the flow control unit is responsible for controlling the air valve and steam valve. Each unit operates independently but in a unified and coordinated manner, which improves the system's reliability and response speed.

[0115] Digital-to-analog conversion (D / A conversion) is the process of converting digital control signals into analog execution signals. Digital commands output by the control system, such as a target temperature of 450℃, are converted into corresponding analog voltage signals by a D / A converter and then transmitted to the heater's power controller. The controller adjusts the heating power according to the voltage level to achieve temperature control. The conversion accuracy is typically 12-bit or 16-bit, with a conversion time of less than 1ms, ensuring fast and accurate execution of control commands.

[0116] Control commands, after validity checks, are directly transmitted to the corresponding actuators via distributed control units. Temperature control commands, after digital-to-analog conversion, are transmitted to the heater control systems of each zone. Each temperature measuring point adjusts its heating power according to the corresponding value in the target temperature vector to achieve precise temperature control. Feeding control commands are directly transmitted to the frequency converter of the feeding system to adjust the speed of the feeding screw, achieving precise feeding rate control. Air and steam flow adjustment commands are transmitted to the flow control valves of the air supply system and steam supply system, respectively, to achieve precise regulation of the gasifying agent supply. All control execution delays are less than 2 seconds, meeting real-time control requirements.

[0117] A comprehensive execution status monitoring mechanism is established to monitor the working status, control accuracy, and response time of each actuator in real time. Monitoring content includes the execution status of temperature control, feeding system, and flow control. Specifically, temperature control execution status includes the deviation between the actual and target temperatures at each temperature measuring point, the heater power output status, and a temperature control accuracy of ±2℃. Feeding system execution status includes the deviation between the actual and target feeding rates, the feed screw operating status, and a feeding control accuracy of ±5%. Flow control execution status includes the deviation between the actual and target air and steam flow rates, control valve opening degree, and response time, with a flow control accuracy of ±3%. When the execution accuracy exceeds the allowable range, the system automatically triggers an alarm and initiates a fault diagnosis program.

[0118] The coordination and control module establishes a complete system operation parameter recording mechanism, recording control execution history, performance index changes, and abnormal event information in real time. The control execution history includes the execution time, result, and accuracy of various control commands; performance index changes include real-time changes in system response time, control accuracy, and stability indicators; and the abnormal event log provides detailed records of events such as command exceeding limits, actuator failures, and communication anomalies. System operation parameter data output includes control execution status and system operation parameters, transmitted every 5 seconds, for use in the host computer monitoring system and fault diagnosis system, providing data support for system optimization and maintenance. Data output adopts standard industrial communication protocols to ensure compatibility and reliability with the upper-level management system.

[0119] Through the implementation of the aforementioned unified execution strategy, the coordination control module can efficiently and safely execute the instructions of each control module, ensuring the stable operation of the entire gasification process. The system control response time is ≤2 seconds, and the execution accuracy of various control instructions meets the design requirements. The coordination of control instructions and system stability are significantly improved, meeting the reliable control requirements of continuous gasification processes. By organically combining multi-component identification, zoned temperature assurance, and intelligent optimization, a complete control system from basic control to intelligent optimization is constructed. A stable and reliable hierarchical gasification control framework is established, and a zoned temperature assurance algorithm is used to ensure that all resin components are completely gasified within the optimal temperature range. This completely solves the gasification control problem caused by the differences in multi-component characteristics and mass fractions of circuit board resins, achieving efficient, stable, and adaptive continuous gasification control, and providing important technical support for the resource utilization of waste circuit boards.

[0120] The embodiments of the present invention have been described above. However, the embodiments are not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make more equivalent embodiments under the guidance of the present embodiments, and all of them are within the protection scope of the present embodiments.

Claims

1. A continuous gasification control system for a circuit board resin based on a circulating fluidized bed, characterized by, The method comprises the following steps: A component identification module scans a feed line board resin to obtain a resin feature vector, and inputs the resin feature vector into an identification model to obtain a component concentration vector; A layered temperature control module determines the temperature and boundary height of each temperature interval according to the component concentration vector, calculates a target temperature vector according to the temperature interval, and collects an actual temperature to calculate a bed average temperature according to the actual temperature; The step of obtaining the target temperature vector comprises the following steps: Determine the gasification temperature of each component, including epoxy resin, phenolic resin and polyimide resin; Establish temperature intervals, including a low-temperature zone, a medium-temperature zone and a high-temperature zone, the default value of the center temperature of the low-temperature zone is the gasification temperature of the epoxy resin, the default value of the center temperature of the medium-temperature zone is the gasification temperature of the phenolic resin, and the default value of the center temperature of the high-temperature zone is the gasification temperature of the polyimide resin; Determine a temperature distribution scheme according to the component concentration vector; the temperature distribution scheme is the boundary height of the temperature interval; establish a low-temperature zone at the bottom of the bed, the low-temperature zone corresponds to the epoxy resin, and the interval range of the low-temperature zone is between the bed bottom and the bottom region boundary height; establish a medium-temperature zone in the middle of the bed, the medium-temperature zone corresponds to the phenolic resin, and the interval range of the medium-temperature zone is between the bottom region boundary height and the top region boundary height; establish a high-temperature zone at the top of the bed, the high-temperature zone corresponds to the polyimide resin, and the interval range of the high-temperature zone is between the top region boundary height and the bed top; When the mass fraction of the epoxy resin or the mass fraction of the polyimide resin in the component concentration vector is lower than a preset mass fraction threshold, the height of the corresponding temperature interval is set to a preset height, the preset height is a preset multiple of the total height of the bed, and the total height of the bed is the vertical height from the bottom air distribution plate to the bed top in the circulating fluidized bed reactor; at this time, the bottom region boundary height is the preset height, and the top region boundary height is the total height of the bed minus the preset height; When the mass fraction of the epoxy resin or the mass fraction of the polyimide resin in the component concentration vector is greater than or equal to the mass fraction threshold, the calculation method of the bottom region boundary height is that the total height of the bed is multiplied by the product of the mass fraction of the epoxy resin and a preset region adjustment coefficient; the calculation method of the top region boundary height is that the product of the total height of the bed, the mass fraction of the polyimide resin and the preset region adjustment coefficient is calculated, the total height of the bed is subtracted by the product to obtain the top region boundary height; A plurality of temperature measuring points are arranged along the height direction of the bed, and the temperature measuring points are uniformly distributed; according to the temperature distribution scheme and the height of the temperature measuring points, the temperature interval to which each temperature measuring point belongs is determined; According to the temperature interval to which each temperature measuring point belongs and the height of the temperature measuring point, the target temperature of the temperature measuring point is calculated; The target temperatures of the plurality of temperature measuring points form a target temperature vector; An intelligent feeding module calculates a target product composition according to the component concentration vector, and determines a feeding rate according to the component concentration vector and the target product composition; A product optimization module calculates a gas concentration deviation according to the actual product composition and the target product composition, and generates an operation parameter adjustment instruction according to the gas concentration deviation and the bed average temperature; A coordinated control module executes the target temperature vector, the feeding rate and the operation parameter adjustment instruction.

2. The circulating fluidized bed-based in-line board resin continuous gasification control system according to claim 1, characterized by, The step of calculating the target temperature of each temperature measuring point comprises the following steps: The target temperature of the temperature measuring point in the low-temperature zone is the gasification temperature of the epoxy resin plus a height adjustment amount, the target temperature of the temperature measuring point in the medium-temperature zone is the gasification temperature of the phenolic resin plus the height adjustment amount, and the target temperature of the temperature measuring point in the high-temperature zone is the gasification temperature of the polyimide resin plus the height adjustment amount; wherein the height adjustment amount is calculated by multiplying the difference between the actual height of the temperature measuring point and the height of the center of the zone by a preset height linear correction coefficient, wherein the height of the center of the zone is the arithmetic average of the boundary heights of the temperature interval to which the temperature measuring point belongs.

3. The circulating fluidized bed-based in-line board resin continuous gasification control system according to claim 1, characterized by, The feed rate is the product of a preset reference feed rate, a component correction factor and a product target correction factor, wherein the component correction factor is the product of a preset epoxy resin correction coefficient and the mass fraction of the epoxy resin plus 1, minus the product of a preset polyimide correction coefficient and the mass fraction of the polyimide; the product target correction factor is 1 plus the sum of a carbon monoxide deviation and a hydrogen deviation; wherein the carbon monoxide deviation is the product of a preset carbon monoxide correction coefficient and a carbon monoxide difference, the carbon monoxide difference being the difference between the target molar fraction of carbon monoxide in the target product composition and a preset reference molar fraction of carbon monoxide, and the hydrogen deviation is the product of a preset hydrogen correction coefficient and a hydrogen difference, the hydrogen difference being the difference between the target molar fraction of hydrogen in the target product composition and a preset reference molar fraction of hydrogen.

4. The circulating fluidized bed-based in-line board resin continuous gasification control system according to claim 3, characterized by, The target product composition includes a target molar fraction of carbon monoxide, a target molar fraction of hydrogen and a target molar fraction of methane; wherein the target molar fraction of carbon monoxide is calculated by multiplying a preset epoxy resin carbon monoxide contribution coefficient by the mass fraction of the epoxy resin, adding a preset phenolic resin carbon monoxide contribution coefficient multiplied by the mass fraction of the phenolic resin, and adding a preset polyimide resin carbon monoxide contribution coefficient multiplied by the mass fraction of the polyimide resin; the target molar fraction of hydrogen is calculated by multiplying a preset epoxy resin hydrogen contribution coefficient by the mass fraction of the epoxy resin, adding a preset phenolic resin hydrogen contribution coefficient multiplied by the mass fraction of the phenolic resin, and adding a preset polyimide resin hydrogen contribution coefficient multiplied by the mass fraction of the polyimide resin; and the target molar fraction of methane is calculated by multiplying a preset epoxy resin methane contribution coefficient by the mass fraction of the epoxy resin, adding a preset phenolic resin methane contribution coefficient multiplied by the mass fraction of the phenolic resin, and adding a preset polyimide resin methane contribution coefficient multiplied by the mass fraction of the polyimide resin.

5. The circulating fluidized bed-based in-line board resin continuous gasification control system according to claim 1, characterized by, The step of generating the operation parameter adjustment instruction includes: calculating a gas concentration deviation, the gas concentration deviation including a carbon monoxide molar fraction deviation and a hydrogen molar fraction deviation; calculating an operation parameter according to the gas concentration deviation, the operation parameter including an air volume flow adjustment and a steam volume flow adjustment; performing a safety constraint check on the operation parameter, the operation parameter after the safety constraint check forming an operation parameter adjustment instruction, the operation parameter adjustment instruction including an air volume flow adjustment amount and a steam volume flow adjustment amount.

6. The circulating fluidized bed-based in-line board resin continuous gasification control system according to claim 5, characterized by, The step of calculating the operation parameter includes: the calculation method of the air volume flow adjustment is to add the product of a preset air flow adjustment coefficient and the carbon monoxide molar fraction deviation to a preset air reference volume flow; The calculation method of the steam volume flow adjustment is: preset steam reference volume flow plus the product of preset steam flow adjustment coefficient and hydrogen molar fraction deviation.

7. The circulating fluidized bed-based in-line board resin continuous gasification control system according to claim 5, characterized by, The steps of the safety constraint check include: reading preset safety constraint ranges, the safety constraint ranges including air volume flow safety constraint ranges and steam volume flow safety constraint ranges; judging whether the operation parameters are within the safety constraint ranges, the compliant parameters being operation parameters not exceeding the safety constraint ranges, and the out-of-limit parameters being operation parameters exceeding the safety constraint ranges; performing amplitude limiting processing on the operation parameters, wherein the compliant parameters do not need to be processed, and the out-of-limit parameters are adjusted to the safety constraint ranges; and outputting the operation parameters after the amplitude limiting processing as operation parameter adjustment instructions.

8. The circulating fluidized bed-based in-line board resin continuous gasification control system according to claim 1, characterized by, The component concentration vector includes mass fractions of components, the mass fractions of components including an epoxy resin mass fraction, a phenolic resin mass fraction, and a polyimide resin mass fraction, and the mass fraction of each component representing a proportion of component mass to total circuit board resin mass.

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