A method and apparatus for controlling the air intake volume in a fluidized bed drying process.

Abstract

This application relates to the field of fluidized bed technology, specifically to a method and apparatus for controlling the inlet air volume during a fluidized bed drying process. The method includes: collecting historical key relevant parameters and historical inlet air volume shut-off times from multiple batches during the fluidized bed drying process; these historical key relevant parameters include inlet air temperature, material temperature, and outlet air temperature; performing dimensionality reduction processing on these historical key relevant parameters to obtain the dimensionality-reduced historical key relevant parameters; performing inter-group mean difference processing on the dimensionality-reduced historical key relevant parameters, and obtaining a standard inlet air volume control value T based on the inter-group mean difference processing result and the historical inlet air volume shut-off times; and monitoring the drying process of the material to be dried based on the standard inlet air volume control value T to control the inlet air volume shut-off time of the material to be dried in real time. The above scheme achieves automatic control of the inlet air volume shut-off point and ensures the accuracy of the blowing control.

Description

Technical Field

[0001] This application relates to the field of fluidized bed technology, specifically to a method and apparatus for controlling the air intake volume in a fluidized bed drying process. Background Technology

[0002] Fluidized bed drying technology is a novel drying technology. The process involves placing bulk materials on an orifice plate and supplying gas from below, causing the material particles to move on the gas distribution plate and remain suspended in the airflow. This creates a mixed bottom layer of material particles and gas, in which the material particles come into full contact with the gas, facilitating heat and moisture transfer between the material and the gas.

[0003] Fluidized bed drying technology is widely used in the pharmaceutical and chemical industries. In the fluidized bed drying process, controlling the airflow rate is a crucial aspect of the equipment, directly impacting the drying efficiency and the safe and economical operation of the equipment. Currently, to prevent uneven temperature distribution during drying, the airflow rate is typically adjusted based on operator experience. For example, the airflow may be stopped (i.e., forced airflow) for 1 minute to allow the material to fall, then the material may be turned over to ensure uniform drying.

[0004] However, in the above scheme, changing the air intake shut-off point based on workers' experience will lead to increased manpower consumption, low efficiency, and a high risk of errors. Summary of the Invention

[0005] This application provides a method and apparatus for controlling the air intake volume in a fluidized bed drying process, which can automatically control the air intake volume shut-off point and ensure the accuracy of the blowing control. The technical solution is as follows.

[0006] In a first aspect, this application provides a method for controlling the air intake volume in a fluidized bed drying process, the method comprising:

[0007] Collect historical key relevant parameters and historical air intake shutdown times for multiple batches during the fluidized bed drying process; the historical key relevant parameters include inlet air temperature, material temperature, and outlet air temperature;

[0008] The historical key related parameters are subjected to dimensionality reduction processing, and the dimensionality-reduced historical key related parameters are obtained;

[0009] The historical key parameters after dimensionality reduction are processed for inter-group mean difference processing, and the standard air intake T control value is obtained based on the inter-group mean difference processing result and the historical air intake shutdown time point.

[0010] The drying process of the material to be dried is monitored according to the standard air intake volume T control value, so as to control the air intake volume of the material to be dried in real time and the shut-off time point.

[0011] Based on the aforementioned technical means, this application combines the material spectrum after dimensionality reduction with the process parameters of the fluidized bed during the drying process, performs secondary dimensionality reduction, and analyzes the comprehensive temperature change law to obtain the inlet air temperature, material temperature, and outlet air temperature, which are most correlated with material changes. Then, it performs inter-group mean difference processing on key historical parameters and calculates the standard inlet air volume control value T based on the inter-group mean difference processing results and historical inlet air volume shut-off times. Using this standard inlet air volume control value T as a baseline, the drying process of the material to be dried is monitored. When the T-square value of the material to be dried drops to this standard inlet air volume control value throughout the entire drying process, the inlet air volume is shut off, and the material is turned over to ensure uniform drying. This achieves automatic control of the inlet air volume shut-off point and ensures the accuracy of the blowing control.

[0012] In conjunction with the first aspect, in one embodiment, before collecting historical key relevant parameters of multiple batches during the fluidized bed drying process and historical air intake shutdown times, the method further includes:

[0013] The material spectrum of the fluidized bed during the drying process is obtained, and the material spectrum is subjected to a dimension reduction process.

[0014] The material spectrum after a first dimensionality reduction process is combined with the process parameters of the fluidized bed during the drying process, and the combination result is subjected to a second dimensionality reduction process to determine the key relevant parameters of material changes from the process parameters; the process parameters include the expansion chamber pressure, air volume, air inlet temperature, material temperature, and air outlet temperature.

[0015] Based on the above technical means, this application combines the material spectrum after dimensionality reduction with the process parameters of the fluidized bed during the drying process, performs secondary dimensionality reduction, and analyzes the comprehensive temperature change law to obtain the inlet air temperature, material temperature, and outlet air temperature that have the highest correlation with material changes. At this time, the inlet air temperature, material temperature, and outlet air temperature with the highest correlation can represent material changes. Further analysis of the above three temperatures can predict the time point for shutting off the inlet air volume.

[0016] In conjunction with the first aspect, in one embodiment, the step of combining the material spectrum after a first dimensionality reduction process with the process parameters of the fluidized bed during the drying process, and then performing a second dimensionality reduction process on the combined result, to determine key relevant parameters of material changes from the process parameters, includes:

[0017] The material spectrum after a single dimensionality reduction process is combined with the process parameters of the fluidized bed during the drying process;

[0018] The combination results are subjected to a second dimensionality reduction process to convert the three-dimensional data corresponding to the combination results into two-dimensional data, and to obtain the comprehensive temperature change law of the fluidized bed during the drying process.

[0019] Based on the comprehensive temperature change pattern, key relevant parameters for material changes are determined from the process parameters.

[0020] In conjunction with the first aspect, in one implementation, before performing dimensionality reduction processing on the historical key-related parameters, the method further includes:

[0021] The historical key related parameters are standardized and preprocessed to convert them into a standard normal distribution.

[0022] Based on the aforementioned technical means, this application uses standardized preprocessing to adjust historical key parameters to a unified scale for subsequent data analysis.

[0023] In conjunction with the first aspect, in one implementation, the step of performing dimensionality reduction processing on the historical key-related parameters and obtaining the dimensionality-reduced historical key-related parameters includes:

[0024] The historical key parameters are converted into an initial parameter matrix by column.

[0025] The initial parameter matrix is ​​zero-mean processed to obtain the covariance matrix corresponding to the initial parameter matrix;

[0026] Obtain the eigenvalues ​​of the covariance matrix and the eigenvectors corresponding to the eigenvalues;

[0027] Based on the magnitude of the eigenvalues, multiple eigenvectors are selected as principal components;

[0028] The historical key-related parameters are represented by the principal components to obtain the historical key-related parameters after dimensionality reduction.

[0029] Based on the above-mentioned technical means, this application reduces the high-dimensional data of key related parameters to low-dimensionality through dimensionality reduction processing, thereby improving computational efficiency and extracting principal components of the data to achieve feature extraction.

[0030] In conjunction with the first aspect, in one implementation, the step of performing inter-group mean difference processing on the historical key related parameters after dimensionality reduction, and obtaining the standard air intake volume T-squared control value based on the inter-group mean difference processing result and the historical air intake volume shutdown time point, includes:

[0031] The inter-group mean difference of the historical key parameters after dimensionality reduction is calculated to obtain the T-squared value of the entire historical drying process for each batch.

[0032] Based on the historical air intake shutdown time points of each batch, the air intake T-square control value of each batch is obtained from the historical T-square value of the entire drying process of each batch.

[0033] The air intake volume T squared control values ​​of each batch are averaged to obtain the standard air intake volume T squared control value.

[0034] Based on the aforementioned technical methods, after dimensionality reduction, this application calculates Hotelling's T by processing the difference in mean between groups. 2 Statistical measures are used to assess the arrival of the intake air volume shutdown time point, Hotelling's T 2 Statistics can be used to determine whether there are significant differences in the data distribution at different stages of the drying process, thereby ensuring the accurate determination of the air intake shutdown time.

[0035] In conjunction with the first aspect, in one embodiment, monitoring the drying process of the material to be dried according to the standard airflow control value T, so as to control the airflow shut-off time of the material to be dried in real time, includes:

[0036] The key relevant parameters of the dried material to be tested are calculated twice to obtain the T-square value of the dried material throughout the drying process.

[0037] When the T-square value of the material to be dried reaches or falls below the standard air intake control value during the entire drying process, it is determined that the drying process of the material to be dried has reached the air intake shut-off time point.

[0038] Based on the above technical means, this application compares the T-square value of the drying process of the material to be tested with the standard air volume T-square control value to determine whether the air volume of the material to be tested has reached the shut-off time point.

[0039] Secondly, this application provides an airflow control device for a fluidized bed drying process, the device comprising:

[0040] The historical key relevant parameter acquisition module is used to collect historical key relevant parameters of multiple batches during the fluidized bed drying process, as well as historical air intake shutdown time points; the historical key relevant parameters include air intake temperature, material temperature, and air outlet temperature.

[0041] The dimensionality reduction module is used to perform dimensionality reduction processing on the historical key related parameters and obtain the dimensionality-reduced historical key related parameters;

[0042] The standard air intake volume T square control value acquisition module is used to process the inter-group mean difference of the historical key related parameters after the dimensionality reduction, and to obtain the standard air intake volume T square control value based on the inter-group mean difference processing result and the historical air intake volume shutdown time point.

[0043] The air intake shut-off time point acquisition module is used to monitor the drying process of the material to be dried according to the standard air intake T square control value, so as to control the air intake shut-off time point of the material to be dried in real time.

[0044] Thirdly, this application provides a computer device, which includes a processor and a memory, wherein the memory stores at least one instruction, which is loaded and executed by the processor to implement the above-described method for controlling the air intake volume in a fluidized bed drying process.

[0045] Fourthly, this application provides a computer-readable storage medium storing at least one instruction, which is loaded and executed by a processor to implement the above-described method for controlling the airflow in a fluidized bed drying process.

[0046] The technical solution provided in this application may include the following beneficial effects:

[0047] This application combines the material spectrum after dimensionality reduction with the process parameters of the fluidized bed during drying, performs secondary dimensionality reduction, and analyzes the comprehensive temperature change law to obtain the inlet air temperature, material temperature, and outlet air temperature, which are most correlated with material changes. Then, it performs inter-group mean difference processing on key historical parameters and calculates the standard inlet air volume control value T based on the inter-group mean difference processing results and historical inlet air volume shut-off times. Using this standard inlet air volume control value T as a baseline, the drying process of the material under test is monitored. When the T-square value of the material under test drops to this standard inlet air volume control value throughout the entire drying process, the inlet air volume is shut off, and the material is turned over to ensure uniform drying. This achieves automatic control of the inlet air volume shut-off point and ensures the accuracy of the blowing control. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0049] Figure 1This is a schematic diagram of the air intake control system in a fluidized bed drying process, according to an exemplary embodiment.

[0050] Figure 2 This is a flowchart illustrating an air intake control method in a fluidized bed drying process according to an exemplary embodiment.

[0051] Figure 3 This is a flowchart illustrating an air intake control method in a fluidized bed drying process according to an exemplary embodiment.

[0052] Figure 4 This is a schematic diagram of a two-dimensional load for principal component analysis (PCA) dimensionality reduction processing according to an exemplary embodiment of this application.

[0053] Figure 5 This is a schematic diagram illustrating the monitoring of the air intake shut-off time of the material to be dried, according to an exemplary embodiment.

[0054] Figure 6 This is a structural block diagram of an air intake control device in a fluidized bed drying process, according to an exemplary embodiment.

[0055] Figure 7 A structural block diagram of a computer device illustrated in an exemplary embodiment of this application is shown. Detailed Implementation

[0056] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0057] Figure 1 This is a schematic diagram illustrating the structure of an airflow control system in a fluidized bed drying process according to an exemplary embodiment. The system includes a fluidized bed 110, a near-infrared spectrometer 120, and a server 130.

[0058] Furthermore, the near-infrared spectrometer 120 is installed on the fluidized bed 110. The near-infrared spectrometer 120 is used to collect the material spectrum on the fluidized bed 110 during the drying process in order to screen out the variables most related to the material changes during the drying process, namely key relevant parameters, including inlet air temperature, material temperature and outlet air temperature.

[0059] Furthermore, the fluidized bed 110 is used to dry materials. During the drying process, the fluidized bed 110 places the bulk material on an orifice plate and supplies gas from below, causing the material particles to move on the gas distribution plate and remain suspended in the airflow, creating a mixed bottom layer of material particles and gas. The material particles are in full contact with the gas in this mixed bottom layer, allowing for heat and moisture transfer between the material and the gas. In addition, during the drying process, by controlling the airflow of the fluidized bed 110 to turn it off or on, the material can be allowed to fall and be turned over, ensuring uniform drying.

[0060] Furthermore, the fluidized bed 110 and the near-infrared spectrometer 120 communicate with the server 130. The server 130 performs a dimensionality reduction process on the material spectrum collected by the near-infrared spectrometer 120. Then, it combines the material spectrum after the dimensionality reduction process with the process parameters of the fluidized bed during the drying process to perform a second dimensionality reduction process and analyze the comprehensive temperature change law to obtain the inlet air temperature, material temperature, and outlet air temperature, which are most correlated with material changes. After that, the inter-group mean difference processing is performed on the historical key relevant parameters, and the standard inlet air volume T square control value can be calculated based on the inter-group mean difference processing results and the historical inlet air volume shutdown time points. This standard inlet air volume T square control value is equivalent to the inlet air volume shutdown monitoring baseline.

[0061] During subsequent monitoring, the server 130 can calculate the T-square value of the drying process of the material to be tested. When the T-square value of the drying process of the material to be tested reaches or falls below the standard air volume control value T-square, the server 130 feeds back to the SCADA system (data acquisition and monitoring control system) to close the air volume valve of the fluidized bed 110, thereby controlling the air volume shut-off point in real time, realizing automatic control of the air volume shut-off point, and ensuring the accuracy of the blowing control.

[0062] Figure 2 This is a flowchart illustrating an airflow control method in a fluidized bed drying process according to an exemplary embodiment. The method is executed by a computer device, which may be, for example... Figure 1 Server 130 is shown in the image. (As shown...) Figure 2 As shown, the method may include the following steps:

[0063] Step S201: Collect historical key relevant parameters and historical air intake shutdown time points for multiple batches during the fluidized bed drying process; these historical key relevant parameters include air intake temperature, material temperature, and air outlet temperature.

[0064] In one possible implementation, before collecting historical key relevant parameters in multiple batches, this embodiment first analyzes the process parameters of the fluidized bed during the drying process, i.e., the SCADA parameters. The SCADA parameters to be monitored during the fluidized bed drying process include expansion chamber pressure, inlet air volume, inlet air temperature, material temperature, and outlet air temperature. However, not all SCADA parameters are highly correlated with material changes. In order to screen out the SCADA parameters most relevant to material changes during the drying process, this embodiment obtains the three key relevant parameters with the highest correlation to material changes from the SCADA parameters through material spectrum acquisition and dimensionality reduction processing of SCADA parameters, namely, inlet air temperature, material temperature, and outlet air temperature. Therefore, the material changes of the fluidized bed during the drying process can be analyzed based on the key relevant parameters, and historical key relevant parameters and historical inlet air volume shutdown times can be collected in multiple batches during the fluidized bed drying process.

[0065] Each batch of materials, or rather, each batch of historical key parameters, corresponds to a historical air intake shutdown time. This historical air intake shutdown time is obtained based on worker experience. Subsequently, using the historical key parameters and historical air intake shutdown time of multiple batches as a dataset for data analysis and feature extraction, the standard air intake T control value can be obtained.

[0066] For example, the materials corresponding to the historical key parameters collected in multiple batches can be cinnamon twigs and poria cocos, or other materials that need to be turned over during the drying process to ensure uniform drying.

[0067] Step S202: Perform dimensionality reduction on the historical key related parameters and obtain the dimensionality-reduced historical key related parameters.

[0068] In one possible implementation, after collecting multiple batches of historical key related parameters, the historical key related parameters are first subjected to dimensionality reduction processing to reduce the high-dimensional data of the key related parameters to a low dimension in order to extract the feature values ​​of the historical key related parameters and improve the computational efficiency.

[0069] Step S203: Perform inter-group mean difference processing on the historical key related parameters after dimensionality reduction, and obtain the standard air intake T control value based on the inter-group mean difference processing result and the historical air intake shutdown time point.

[0070] In one possible implementation, this embodiment compares the differences in means between different groups through inter-group mean difference processing, such as Hotelling's T. 2(Hotling T-squared distribution) By processing the difference in mean between groups, the historical T-squared value of the entire drying process for each batch is first obtained. Then, based on the historical T-squared value of the entire drying process and the historical air intake shutdown time point, the standard air intake T-squared control value is calculated. The standard air intake T-squared control value is equivalent to the air intake shutdown monitoring baseline.

[0071] Step S204: Monitor the drying process of the material to be dried according to the standard air intake T control value, so as to control the air intake shut-off time of the material to be dried in real time.

[0072] In one possible implementation, this embodiment performs a secondary inter-group mean difference calculation on the dimensionality reduction of the material to be tested, thereby obtaining the T-square value of the entire drying process corresponding to the material to be tested. By comparing the T-square value of the entire drying process corresponding to the material to be tested with the standard air intake T-square control value, the air intake shut-off time point of the material to be tested can be controlled in real time. Generally, when the T-square value of the entire drying process corresponding to the material to be tested reaches or falls below the drying endpoint judgment threshold, it is determined that the drying process of the material to be tested has reached the air intake shut-off time point.

[0073] In summary, this application combines the material spectrum after dimensionality reduction with the process parameters of the fluidized bed during drying, performs secondary dimensionality reduction, and analyzes the comprehensive temperature change law to obtain the inlet air temperature, material temperature, and outlet air temperature, which are most correlated with material changes. Then, it performs inter-group mean difference processing on key historical parameters and calculates the standard inlet air volume control value T based on the inter-group mean difference processing results and historical inlet air volume shut-off times. Using this standard inlet air volume control value T as a baseline, the drying process of the material under test is monitored. When the T-square value of the material under test drops to this standard inlet air volume control value throughout the entire drying process, the inlet air volume is shut off, and the material is turned over to ensure uniform drying. This achieves automatic control of the inlet air volume shut-off point and ensures the accuracy of the blowing control.

[0074] Figure 3 This is a flowchart illustrating an airflow control method in a fluidized bed drying process according to an exemplary embodiment. The method is executed by a computer device, which may be, for example... Figure 1 Server 130 is shown in the image. (As shown...) Figure 3 As shown, the method may include the following steps:

[0075] Step S301: Obtain the material spectrum of the fluidized bed during the drying process, and perform a dimensionality reduction process on the material spectrum. This dimensionality reduction process is a principal component analysis (PCA) dimensionality reduction process.

[0076] Step S302: Combine the material spectrum after the first dimensionality reduction process with the process parameters of the fluidized bed during the drying process, and perform a second dimensionality reduction process on the combined result to determine the key relevant parameters of material changes from the process parameters; the process parameters include the expansion chamber pressure, air volume, air inlet temperature, material temperature, and air outlet temperature. This second dimensionality reduction process is a second principal component analysis (PCA) dimensionality reduction process.

[0077] In one possible implementation, step S302 includes:

[0078] The spectrum of the material after a single dimensionality reduction process is combined with the process parameters of the fluidized bed during the drying process;

[0079] The combination result is subjected to a second dimensionality reduction process to convert the three-dimensional data corresponding to the combination result into two-dimensional data, and to obtain the comprehensive temperature change law of the fluidized bed during the drying process.

[0080] Based on the overall temperature change pattern, the key relevant parameters for material changes are determined from the process parameters.

[0081] Furthermore, the SCADA parameters (i.e., the aforementioned process parameters) to be monitored during the fluidized bed drying process include the expansion chamber pressure, inlet air volume, inlet air temperature, material temperature, and outlet air temperature. To identify the variables most relevant to material changes during the drying process, this embodiment installs a near-infrared spectrometer on the fluidized bed to collect the material spectrum during the drying process. Simultaneously, the moisture content of the material during production is determined using a drying method, and a moisture model is established based on the interval partial least squares method. The moisture model R... 2 =0.9842, where R 2 To measure the predictive power of the model, it is demonstrated that the material spectrum can represent moisture for monitoring. When building a moisture model, various SCADA parameters (i.e., the aforementioned process parameters) are used to predict moisture values. By calculating the correlation between these SCADA parameters and moisture values, it is possible to determine which SCADA parameters contribute significantly to the prediction of moisture values.

[0082] After collecting the material spectrum during the drying process, this embodiment combines the material spectrum after the first principal component analysis (PCA) dimensionality reduction with the process parameters and performs another PCA dimensionality reduction. Please refer to [link to PCA dimensionality reduction documentation]. Figure 4 The diagram shows a two-dimensional loading of principal component analysis (PCA) dimensionality reduction. This two-dimensional loading diagram mainly reflects the importance of variables and the relationships between them. The horizontal axis represents the PCA first principal component score, and the vertical axis represents the PCA second principal component score. Figure 4The results show that the material changes are most strongly correlated with the inlet air temperature, the material temperature, and the outlet air temperature, and have very low correlation with the inlet air volume and the expansion chamber pressure. Therefore, only these three temperature parameters need to be analyzed in the future.

[0083] Step S303: Collect historical key relevant parameters of multiple batches during the fluidized bed drying process and historical air intake shutdown time points; these historical key relevant parameters include air intake temperature, material temperature and air outlet temperature.

[0084] Furthermore, since the historical air intake shutdown time points for each batch are determined by the workers, and considering the differences in operating time among different workers, the historical air intake shutdown time points determined by different workers will differ. Therefore, it is necessary to combine the moisture content at the historical air intake shutdown time points and select qualified batches with relatively uniform moisture content as the dataset. A complete production process is considered as one batch. In this embodiment, at least 25 batches with qualified moisture content are collected, along with key historical parameters (inlet air temperature, material temperature, and outlet air temperature). Temperature sensors are installed on the fluidized bed drying equipment, and data is collected by the SCADA system. The SCADA system can collect temperature sensor data in real time.

[0085] Step S304: Perform dimensionality reduction on the historical key relevant parameters and obtain the dimensionality-reduced historical key relevant parameters. The dimensionality reduction here is a three-stage dimensionality reduction, which is a three-stage principal component analysis (PCA) dimensionality reduction.

[0086] In one possible implementation, before performing dimensionality reduction on the historical key-related parameters, the historical key-related parameters are standardized preprocessed to convert them into a standard normal distribution.

[0087] Furthermore, before performing dimensionality reduction on the historical key related parameters, this embodiment also needs to perform standardization preprocessing on the collected historical key related parameters (inlet air temperature, outlet air temperature, and material temperature). The calculation method for standardization preprocessing is as follows:

[0088]

[0089] Where Z represents the standardized preprocessed data, X represents the original historical key relevant parameters, μ represents the mean, and σ represents the standard deviation.

[0090] In one possible implementation, step S304 includes:

[0091] Convert the relevant parameters of this historical key point into an initial parameter matrix by column;

[0092] The initial parameter matrix is ​​zero-mean normalized to obtain the covariance matrix corresponding to the initial parameter matrix;

[0093] Obtain the eigenvalues ​​of the covariance matrix and the corresponding eigenvectors;

[0094] Based on the magnitude of the eigenvalue, multiple eigenvectors are selected as principal components;

[0095] The historical key-related parameters are represented by the principal component to obtain the historical key-related parameters after dimensionality reduction.

[0096] Furthermore, this embodiment uses the PCA algorithm to reduce the dimensionality of the standardized preprocessed historical key-related parameters, including:

[0097] 1) Arrange the historical key parameters into an n x m matrix Z (i.e., the initial parameter matrix mentioned above);

[0098] 2) Zero-mean value is applied to each row of matrix Z, i.e., the mean of that row is subtracted;

[0099] 3) Calculate the covariance matrix;

[0100] 4) Find the eigenvalues ​​and corresponding eigenvectors of the covariance matrix;

[0101] 5) Arrange the eigenvectors into a matrix (i.e., the eigenmatrix above) from top to bottom according to the size of their corresponding eigenvalues, and take the first k rows to form a matrix P as the principal components;

[0102] 6) The historical key relevance parameters are represented by principal components to obtain the historical key relevance parameters after PCA dimensionality reduction.

[0103] Step S305: Perform an inter-group mean difference calculation on the historical key related parameters after dimensionality reduction to obtain the T-square value of the entire historical drying process for each batch.

[0104] Furthermore, in this embodiment, Hotelling's T2 is calculated as the T-squared value for the entire historical drying process using the score data after dimensionality reduction processing by principal component analysis (PCA). This T-squared value for the entire historical drying process is obtained using the following formula:

[0105] T 2 =X T PΛ -1 P T X;

[0106] Λ=diag{λ1,…λ k};

[0107] Step S306: Based on the historical air intake shutdown time point of each batch, obtain the air intake T control value of each batch from the historical T-square value of the entire drying process of each batch; that is, obtain the air intake T control value corresponding to the historical air intake shutdown time point of each batch from the historical T-square value of the entire drying process of each batch.

[0108] Furthermore, following the previous example, this embodiment collects at least 25 batches of historical key relevant parameters. Through a single inter-group mean difference calculation, the historical T-square value of the entire drying process for each batch is obtained. At the same time, the historical air intake shut-off time (i.e., blowing time) of each batch can be known using worker experience. Then, based on the historical air intake shut-off time, the T-square value of the blowing point corresponding to the historical air intake shut-off time (i.e., the aforementioned air intake T-square control value) is found from the historical T-square value of the entire drying process for each batch.

[0109] Step S307: Average the air intake volume T control value of each batch to obtain the standard air intake volume T control value.

[0110] Furthermore, following the previous example, this embodiment can average the air intake volume T control values ​​of 25 batches (assuming the air intake volume T control value is 0.49984) to obtain the standard air intake volume T control value 0.49984 / 25 = 0.01999.

[0111] Step S308: Perform a secondary inter-group mean difference calculation on the key relevant parameters of the material to be tested to obtain the T-square value of the material to be tested throughout the drying process.

[0112] Furthermore, in this embodiment, based on the same formula as above, a secondary inter-group mean difference calculation is performed on the key relevant parameters of the material to be tested for drying to obtain the T-square value of the material to be tested for drying throughout the entire drying process. This will not be elaborated further here.

[0113] Step S309: When the T-square value of the drying process reaches or falls below the standard air volume T-square control value, it is determined that the drying process of the material to be dried has reached the air volume shut-off time point.

[0114] Furthermore, after obtaining the T-square value of the entire drying process of the material to be tested, comparing this T-square value with the standard inlet air volume control value allows it to determine whether the drying process of the material to be tested has reached the inlet air volume shut-off time point. If the T-square value of the entire drying process is greater than the standard inlet air volume control value, it is an abnormal value. When the T-square value of the entire drying process falls below the standard inlet air volume control value, feedback is sent back to the SCADA system to close the inlet air volume valve of the fluidized bed, thereby controlling the inlet air volume shut-off point in real time. In other words, in subsequent use, this application can directly determine the blowing time based on three temperature data (inlet air temperature, material temperature, and outlet air temperature).

[0115] Following the example above, please refer to [link / reference]. Figure 5 The diagram shown illustrates the monitoring of the air intake shut-off time points for the material to be dried. Figure 5 The production process of three batches of materials to be tested was followed up. During the production process, the T-square value of the key relevant parameters of each batch during the entire drying process was monitored. When the T-square value of the entire drying process reached 0.01999, adjustments were made. It can be seen that this application can adjust the blowing time according to the different properties of materials in different batches and the different temperature data fed back.

[0116] In summary, this application combines the material spectrum after dimensionality reduction with the process parameters of the fluidized bed during drying, performs secondary dimensionality reduction, and analyzes the comprehensive temperature change law to obtain the inlet air temperature, material temperature, and outlet air temperature, which are most correlated with material changes. Then, it performs inter-group mean difference processing on key historical parameters and calculates the standard inlet air volume control value T based on the inter-group mean difference processing results and historical inlet air volume shut-off times. Using this standard inlet air volume control value T as a baseline, the drying process of the material under test is monitored. When the T-square value of the material under test drops to this standard inlet air volume control value throughout the entire drying process, the inlet air volume is shut off, and the material is turned over to ensure uniform drying. This achieves automatic control of the inlet air volume shut-off point and ensures the accuracy of the blowing control.

[0117] Figure 6 This is a structural block diagram illustrating an airflow control device in a fluidized bed drying process according to an exemplary embodiment. The device includes:

[0118] The historical key relevant parameter acquisition module 601 is used to collect historical key relevant parameters of multiple batches during the drying process of the fluidized bed, as well as the historical air intake shutdown time points; these historical key relevant parameters include air intake temperature, material temperature, and air outlet temperature.

[0119] The dimensionality reduction module 602 is used to perform dimensionality reduction processing on the historical key related parameters and obtain the dimensionality-reduced historical key related parameters;

[0120] The standard air intake volume T square control value acquisition module 603 is used to process the inter-group mean difference of the historical key related parameters after the dimensionality reduction, and to obtain the standard air intake volume T square control value based on the inter-group mean difference processing result and the historical air intake volume shutdown time point.

[0121] The air intake shut-off time acquisition module 604 is used to monitor the drying process of the material to be dried according to the standard air intake T control value, so as to control the air intake shut-off time of the material to be dried in real time.

[0122] In one possible implementation, the device is also used for:

[0123] Obtain the material spectrum of the fluidized bed during the drying process, and perform a dimension reduction process on the material spectrum;

[0124] The material spectrum after a first dimensionality reduction process is combined with the process parameters of the fluidized bed during the drying process, and the combined result is subjected to a second dimensionality reduction process to determine the key relevant parameters of material change from the process parameters. The process parameters include the expansion chamber pressure, air volume, air inlet temperature, material temperature, and air outlet temperature.

[0125] In one possible implementation, the device is also used for:

[0126] The spectrum of the material after a single dimensionality reduction process is combined with the process parameters of the fluidized bed during the drying process;

[0127] The combination result is subjected to a second dimensionality reduction process to convert the three-dimensional data corresponding to the combination result into two-dimensional data, and to obtain the comprehensive temperature change law of the fluidized bed during the drying process.

[0128] Based on the overall temperature change pattern, the key relevant parameters for material changes are determined from the process parameters.

[0129] In one possible implementation, the device is also used for:

[0130] The relevant parameters of this historical key are standardized and preprocessed to transform them into a standard normal distribution.

[0131] In one possible implementation, the dimensionality reduction processing module 602 is further configured to:

[0132] Convert the relevant parameters of this historical key point into an initial parameter matrix by column;

[0133] The initial parameter matrix is ​​zero-mean normalized to obtain the covariance matrix corresponding to the initial parameter matrix;

[0134] Obtain the eigenvalues ​​of the covariance matrix and the corresponding eigenvectors;

[0135] Based on the magnitude of the eigenvalue, multiple eigenvectors are selected as principal components;

[0136] The historical key-related parameters are represented by the principal component to obtain the historical key-related parameters after dimensionality reduction.

[0137] In one possible implementation, the standard air intake volume T control value acquisition module 603 is further used for:

[0138] A cross-group mean difference calculation was performed on the historical key parameters after dimensionality reduction to obtain the T-squared value of the entire historical drying process for each batch.

[0139] Based on the historical air intake shutdown time of each batch, the air intake T-square control value of each batch is obtained from the historical T-square value of the entire drying process of each batch.

[0140] The air intake volume T squared control values ​​of each batch are averaged to obtain the standard air intake volume T squared control value.

[0141] In one possible implementation, the air intake shutdown time point acquisition module 604 is further configured to:

[0142] The key relevant parameters of the material to be tested are calculated twice to obtain the T-square value of the material during the entire drying process.

[0143] When the T-square value of the material to be dried reaches or falls below the standard air volume control value of the entire drying process, it is determined that the drying process of the material to be dried has reached the air volume shut-off time point.

[0144] In summary, this application combines the material spectrum after dimensionality reduction with the process parameters of the fluidized bed during drying, performs secondary dimensionality reduction, and analyzes the comprehensive temperature change law to obtain the inlet air temperature, material temperature, and outlet air temperature, which are most correlated with material changes. Then, it performs inter-group mean difference processing on key historical parameters and calculates the standard inlet air volume control value T based on the inter-group mean difference processing results and historical inlet air volume shut-off times. Using this standard inlet air volume control value T as a baseline, the drying process of the material under test is monitored. When the T-square value of the material under test drops to this standard inlet air volume control value throughout the entire drying process, the inlet air volume is shut off, and the material is turned over to ensure uniform drying. This achieves automatic control of the inlet air volume shut-off point and ensures the accuracy of the blowing control.

[0145] Please see Figure 7 This is a schematic diagram of a computer device provided according to an exemplary embodiment of the present application. The computer device includes a memory and a processor. The memory is used to store a computer program. When the computer program is executed by the processor, it implements the above-described method for controlling the air intake volume in a fluidized bed drying process.

[0146] The processor can be a central processing unit (CPU). It can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations thereof.

[0147] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the methods in the embodiments of this application. The processor executes various functional applications and data processing by running the non-transitory software programs, instructions, and modules stored in the memory, thereby implementing the methods in the above-described embodiments.

[0148] The memory may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created by the processor, etc. Furthermore, the memory may include high-speed random access memory and non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory may optionally include memory remotely located relative to the processor, which can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0149] In one exemplary embodiment, a computer-readable storage medium is also provided for storing at least one computer program, which is loaded and executed by a processor to implement all or part of the steps in the above-described method. For example, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, or optical data storage device, etc.

[0150] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0151] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for controlling the air intake volume in a fluidized bed drying process, characterized in that, The method includes: Collect historical key relevant parameters and historical air intake shutdown times for multiple batches during the fluidized bed drying process; the historical key relevant parameters include inlet air temperature, material temperature, and outlet air temperature; The historical key related parameters are subjected to dimensionality reduction processing, and the dimensionality-reduced historical key related parameters are obtained; The historical key parameters after dimensionality reduction are processed for inter-group mean difference processing, and the standard air intake T control value is obtained based on the inter-group mean difference processing result and the historical air intake shutdown time point. The drying process of the material to be tested is monitored according to the standard air intake volume T-square control value to control the air intake volume shut-off time point of the material to be tested in real time. This includes: performing secondary inter-group mean difference calculation on key relevant parameters of the material to be tested to obtain the T-square value of the material to be tested throughout the drying process; when the T-square value of the material to be tested throughout the drying process reaches or falls below the standard air intake volume T-square control value, it is determined that the drying process of the material to be tested has reached the air intake volume shut-off time point. The processing of inter-group mean differences and the calculation of the second inter-group mean differences are used to calculate Hotelling's T² statistic. The standard air intake volume T² control value is the average value of Hotelling's T² statistics for each batch at the historical air intake volume shutdown time point. The T-square value for the entire drying process is Hotelling's T² statistic, calculated based on the key relevant parameters of the material to be dried during the drying process.

2. The method according to claim 1, characterized in that, Before collecting historical key parameters and historical air intake shutdown times from multiple batches during the fluidized bed drying process, the method further includes: The material spectrum of the fluidized bed during the drying process is obtained, and the material spectrum is subjected to a dimension reduction process. The material spectrum after a first dimensionality reduction process is combined with the process parameters of the fluidized bed during the drying process, and the combination result is subjected to a second dimensionality reduction process to determine the key relevant parameters of material changes from the process parameters; the process parameters include the expansion chamber pressure, air volume, air inlet temperature, material temperature, and air outlet temperature.

3. The method according to claim 2, characterized in that, The process involves combining the material spectrum after a first dimensionality reduction with the process parameters of the fluidized bed during drying, and then performing a second dimensionality reduction on the combined result to determine key relevant parameters of material changes from the process parameters, including: The material spectrum after a single dimensionality reduction process is combined with the process parameters of the fluidized bed during the drying process; The combination results are subjected to a second dimensionality reduction process to convert the three-dimensional data corresponding to the combination results into two-dimensional data, and to obtain the comprehensive temperature change law of the fluidized bed during the drying process. Based on the comprehensive temperature change pattern, key relevant parameters for material changes are determined from the process parameters.

4. The method according to claim 1, characterized in that, Before performing dimensionality reduction on the historical key-related parameters, the method further includes: The historical key related parameters are standardized and preprocessed to convert them into a standard normal distribution.

5. The method according to claim 1, characterized in that, The step of performing dimensionality reduction processing on the historical key-related parameters and obtaining the dimensionality-reduced historical key-related parameters includes: The historical key parameters are converted into an initial parameter matrix by column. The initial parameter matrix is ​​zero-mean processed to obtain the covariance matrix corresponding to the initial parameter matrix; Obtain the eigenvalues ​​of the covariance matrix and the eigenvectors corresponding to the eigenvalues; Based on the magnitude of the eigenvalues, multiple eigenvectors are selected as principal components; The historical key-related parameters are represented by the principal components to obtain the historical key-related parameters after dimensionality reduction.

6. The method according to claim 1, characterized in that, The step involves processing the inter-group mean differences of the historical key parameters after dimensionality reduction, and obtaining the standard airflow control value T based on the inter-group mean difference processing results and the historical airflow shutdown time points, including: The inter-group mean difference of the historical key parameters after dimensionality reduction is calculated to obtain the T-squared value of the entire historical drying process for each batch. Based on the historical air intake shutdown time points of each batch, the air intake T-square control value of each batch is obtained from the historical T-square value of the entire drying process of each batch. The air intake volume T squared control values ​​of each batch are averaged to obtain the standard air intake volume T squared control value.

7. An airflow control device for a fluidized bed drying process, characterized in that, The device includes: The historical key relevant parameter acquisition module is used to collect historical key relevant parameters of multiple batches during the fluidized bed drying process, as well as historical air intake shutdown time points; the historical key relevant parameters include air intake temperature, material temperature, and air outlet temperature. The dimensionality reduction module is used to perform dimensionality reduction processing on the historical key related parameters and obtain the dimensionality-reduced historical key related parameters; The standard air intake volume T square control value acquisition module is used to process the inter-group mean difference of the historical key related parameters after the dimensionality reduction, and to obtain the standard air intake volume T square control value based on the inter-group mean difference processing result and the historical air intake volume shutdown time point. The air intake shut-off time point acquisition module is used to monitor the drying process of the material to be dried according to the standard air intake T square control value, so as to control the air intake shut-off time point of the material to be dried in real time. The air intake shut-off time point acquisition module is also used to perform secondary inter-group mean difference calculation on key relevant parameters of the material to be dried in order to obtain the T-square value of the entire drying process of the material to be dried. When the T-square value of the material to be dried reaches or falls below the standard air volume control value of the entire drying process, it is determined that the drying process of the material to be dried has reached the air volume shut-off time point. The processing of inter-group mean differences and the calculation of the second inter-group mean differences are used to calculate Hotelling's T² statistic. The standard air intake volume T² control value is the average value of Hotelling's T² statistics for each batch at the historical air intake volume shutdown time point. The T-square value for the entire drying process is Hotelling's T² statistic, calculated based on the key relevant parameters of the material to be dried during the drying process.

8. A computer device, characterized in that, The computer device includes a processor and a memory, the memory storing at least one instruction, which is loaded and executed by the processor to implement an air intake control method in a fluidized bed drying process as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The storage medium stores at least one instruction, which is loaded and executed by a processor to implement the air intake control method in a fluidized bed drying process as described in any one of claims 1 to 6.

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