CN111896617A - Method and system for detecting particle attribute acoustic emission in fluidized bed traditional Chinese medicine particle preparation process - Google Patents
Method and system for detecting particle attribute acoustic emission in fluidized bed traditional Chinese medicine particle preparation process Download PDFInfo
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- CN111896617A CN111896617A CN202010734809.2A CN202010734809A CN111896617A CN 111896617 A CN111896617 A CN 111896617A CN 202010734809 A CN202010734809 A CN 202010734809A CN 111896617 A CN111896617 A CN 111896617A
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Abstract
The invention discloses a particle attribute acoustic emission detection system in a fluidized bed traditional Chinese medicine particle preparation process, which is characterized by comprising an acoustic emission sensor arranged on a bed body of a fluidized bed and used for receiving acoustic signal data of the fluidized bed; the amplifier and the acquisition card are used for amplifying and acquiring acoustic signal data at fixed time intervals; the computer system comprises a data preprocessing program module and a multivariate quantitative correction model program module.
Description
Technical Field
The invention relates to the technical field of on-line monitoring of a traditional Chinese medicine granule production process, in particular to a granule attribute acoustic emission detection method and system in a fluidized bed traditional Chinese medicine granule preparation process.
Background
The fluidized bed granulation technology is a novel granulation technology which is completed by mixing, granulating and drying operations in one step [1 ]. The fluid bed granulation process directly affects the quality of the final product. The quality indexes of the granules, such as water content, particle size distribution, bulk density and the like, are closely related to a plurality of process parameters, such as air inlet temperature, air quantity, liquid spraying rate, atomizing pressure and the like, in the granulating process, so that the establishment of a reasonable process parameter regulation strategy is the key for producing high-quality traditional Chinese medicine formula granule products. The traditional process control strategy is to take samples at intervals during the granulation process and adjust the process parameters by observing or analyzing the samples off-line. The method has the problems of strong subjectivity, delayed process monitoring and the like, and the quality consistency among product batches needs to be improved. Therefore, the development of an on-line monitoring method for various quality indexes in the fluidized bed granulation process of the traditional Chinese medicine formula granules has great significance for improving the quality stability of products.
The acoustic emission technology analyzes process information by monitoring acoustic signals in a production process, and is a passive acoustic detection technology. In the fluid bed granulation process, the collision and friction between the granules and between the granules and the inner wall give off acoustic signals containing information about the physical properties of the granules, such as water content, particle size distribution, etc.
Therefore, those skilled in the art are devoted to develop an on-line detection method and system for the particle properties in the fluidized bed traditional Chinese medicine preparation process based on the acoustic emission technology.
Disclosure of Invention
In view of the above defects in the prior art, the technical problem to be solved by the present invention is to provide an online detection method and system for particle properties in a fluidized bed traditional Chinese medicine preparation process based on an acoustic emission technology.
In order to achieve the above object, the present invention provides in a first aspect a method for detecting particle attribute acoustic emission in a fluidized bed traditional Chinese medicine particle preparation process, comprising the steps of:
(1) collecting acoustic signal data through an acoustic emission sensor arranged on a fluidized bed body;
(2) collecting acoustic signal data of n batches of fluidized beds in a working state and corresponding particle attribute measured values comprising particle water content, average particle diameter D50 and bulk density as a correction set;
(3) converting the acquired acoustic signal data from a time domain signal to a frequency domain signal through fast Fourier transform, equally dividing the frequency spectrum into N sections, and calculating the average value of each section to obtain a segmented average frequency spectrum containing N variables;
(4) using the correction set to establish a multivariate quantitative correction model of the particle attributes;
(5) the multivariate quantitative correction model is applied to the detection of the particle attributes in the preparation process of the traditional Chinese medicine particles in the fluidized bed.
Further, in step (1), the acoustic emission sensor is disposed at a level of a sampling port of the charging chamber of the fluidized bed.
Further, in step (2), an acoustic signal is acquired after each sampling in the fluidized bed granulation process.
Further, in the step (2), n is 6 or more.
Further, in the step (3), the frequency spectrum range is 50-320 KHz.
Further, the multivariate quantitative calibration model in the step (4) is established by using a partial least squares regression (PLS) algorithm.
The invention provides a particle attribute acoustic emission detection system in a fluidized bed traditional Chinese medicine particle preparation process, which is characterized by comprising an acoustic emission sensor arranged on a fluidized bed body and used for receiving acoustic signal data of the fluidized bed; the amplifier and the acquisition card are used for amplifying and acquiring acoustic signal data at fixed time intervals; the computer system comprises a data preprocessing program module and a multivariate quantitative correction model program module, wherein the data preprocessing program module converts acoustic signal data from a time domain signal into a frequency domain signal through fast Fourier transform, equally divides a frequency spectrum into N sections, and calculates an average value of each section to obtain a segmented average frequency spectrum containing N variables; the multivariate quantitative correction model program module predicts the particle properties including particle moisture content, average particle size D50, and bulk density based on the segmented average spectrum.
Further, the acoustic emission sensor is arranged at the level of the sampling opening of the filling chamber of the fluidized bed.
Further, the spectral range is 50-320 KHz.
Further, the multivariate quantitative calibration model is established by using a partial least squares regression (PLS) algorithm.
The invention establishes the relationship between the acoustic signal and the particle attribute in the process of granulating the traditional Chinese medicine in the fluidized bed, and can carry out real-time online detection on the particle attribute through the acoustic signal.
The conception, the specific structure and the technical effects of the present invention will be further described with reference to the accompanying drawings to fully understand the objects, the features and the effects of the present invention.
Drawings
FIG. 1 is a schematic view of an acoustic emission detection system for particle properties in a fluidized bed herbal granule manufacturing process in accordance with a preferred embodiment of the present invention;
FIG. 2 is a graph of the spectrum of different acquisition durations of an acoustic signal in a preferred embodiment of the present invention; (ii) a
FIG. 3 is a graph of the original mean frequency spectrum of an acoustic signal in a preferred embodiment of the present invention; (ii) a
FIG. 4 is a block averaged frequency spectrum of an acoustic signal in a preferred embodiment of the present invention;
FIG. 5 shows the batch moisture content, D50, and bulk density trend in a preferred embodiment of the present invention;
FIG. 6 is a spectrum plot of 50-400kHz in a preferred embodiment of the present invention;
FIG. 7 is a graph of the spectrum of 200-400kHz in a preferred embodiment of the present invention;
FIG. 8 is a graph of the predicted value of each index and the reference value according to a preferred embodiment of the present invention;
Detailed Description
The technical contents of the preferred embodiments of the present invention will be more clearly and easily understood by referring to the drawings attached to the specification. The present invention may be embodied in many different forms of embodiments and the scope of the invention is not limited to the embodiments set forth herein.
In another embodiment according to the invention, as shown in fig. 1, 1 is a filter bag of a fluidized bed, 2 is a spray gun, 3 is a sampling port, 4 is an acoustic emission sensor, 5 is an amplifier, 6 is an acquisition card, and 7 is a computer system. An acoustic emission sensor 4 is arranged at the height of a sampling port 3 of the fluidized bed charging chamber, and the vertical distance between the acoustic emission sensor and the gas flow distribution plate is 23.5 cm. The acoustic emission sensor 4 is of a resonant type, the resonant frequency is 150kHz, the effective response range is 50-400kHz, and the gain of the amplifier is 40 dB. The acoustic signal is received by the sensor 4 and converted into an electric signal, the electric signal is amplified by the amplifier 5 and converted into a digital signal by the DS5-8B full information acoustic emission signal analyzer, and finally, the acoustic signal data is recorded by the matched acquisition card 6 and acquisition software. The sampling frequency of the acoustic emission signals is 3MHz, and the acoustic emission signals are collected at fixed time intervals in the fluidized bed granulation process until the granulation is finished.
Taking 5.1kg of stomach nourishing granule extract, adding 10kg of dextrin and 14kg of purified water, stirring uniformly, and heating to 60 ℃ to be used as an adhesive for later use. And placing 6.5kg of dextrin and 3.35kg of sucrose in a fluidized bed as base powder for fluidization, spraying the extract in a top spraying manner after the auxiliary materials are heated to a certain temperature, and finishing the granulation process according to a preset process flow. Sampling is carried out every 6 minutes after the air inlet is started until the granulation process is finished. All samples collected in this example were from a fluid bed granulation process with 10 batches of stomach granules, approximately 27 samples were collected per batch for a total of 269. The first 9 batches of extract were numbered 19047, and the 10 th batch of extract was numbered 19080.
Given the similarity of spectral peak patterns for different acquisition durations, as shown in fig. 2. In the embodiment, the signal acquisition time length is selected to be 2 s. And acquiring an acoustic signal after sampling every time in the fluidized bed granulation process until the granulation is finished. Before modeling, a time domain signal needs to be converted into a frequency domain signal, and the specific scheme is that 2s of the time domain signal is divided into 50 sections, each section of the time domain signal is subjected to Fast Fourier Transform (FFT) to obtain a frequency spectrum, and 50 frequency spectrums are averaged to obtain an average spectrogram of 2s of an acoustic signal, as shown in fig. 3. The frequency range of the spectrum is 50-400kHz, the resolution is 25Hz, and each spectrum contains 14001 frequency variables. In order to reduce the frequency spectrum noise, reduce the number of variables and improve the operation speed, the frequency spectrum is equally divided into N sections on the frequency domain, and the average value of each section is calculated to obtain a segmented average frequency spectrum containing N variables [9 ]. The present embodiment selects a piecewise average spectrum modeling with the variable number of 700, as shown in fig. 4. Acoustic signal processing was done in MATLAB 2019b (MathWorks, usa).
The samples were divided into calibration and validation sets using concentration gradient methods. And observing and selecting the acoustic signal frequency band containing effective information for modeling. And establishing a multivariate correction model between the acoustic signal segmented average frequency spectrum and each quality index of the particles by adopting a partial least squares regression algorithm. And (4) investigating the influence of the normalization method and the centralization method on the model prediction performance, and selecting the optimal data normalization method. And determining the optimal principal component number by adopting a 10-fold cross validation method so as to correct the error root mean square, the decision coefficient, the prediction error root mean square and the decision coefficient to evaluate the prediction performance of the model. Data analysis was performed in the chemometric analysis software SIMCA 14.1 (umemetrics, sweden).
The results of the analysis of the individual batches of particles are shown in FIG. 5, and the sample numbers and ranges for the moisture content, average particle size D50 and bulk density of the particles are shown in Table 1. The samples of different quality indexes are different due to the fact that samples or acoustic signals are not collected at certain moments and analysis results of partial samples are missing.
TABLE 1 analysis results of quality index of samples of each lot
The spectrum of the acoustic signal for the pelletization process is shown in fig. 6 and 7, taking batch 10 as an example. It can be known from the figure that, in the granulation process, the acoustic signal intensity in the frequency band of 50-320kHz has a certain fluctuation, and may include effective signals reflecting the changes of properties such as the moisture content, the particle size distribution, the bulk density, and the like of the particles, so the acoustic signal frequency band used for modeling in this embodiment is selected from 50-320kHz, corresponding to the variable 1-540 of the segment average spectrum.
The samples are divided by a concentration gradient method, the ratio of the number of the samples in the correction set to the number of the samples in the verification set is 3:1, and the division result of the samples of each quality index is shown in Table 2.
Table 2 sample partitioning results
And establishing a quantitative correction model of the acoustic signal segmented average frequency spectrum, the water content of the particles, the average particle size D50 and the bulk density. The optimal model parameters and prediction performance are shown in table 3, and fig. 8 is a graph relating the model prediction values and reference values (measured values) of the quality indexes of the validation set samples.
TABLE 3 modeling results for each index
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (10)
1. A method for detecting particle attribute acoustic emission in a fluidized bed traditional Chinese medicine particle preparation process is characterized by comprising the following steps:
1) collecting acoustic signal data through an acoustic emission sensor arranged on a fluidized bed body;
2) collecting acoustic signal data of n batches of fluidized beds in a working state and corresponding particle attribute measured values comprising particle water content, average particle diameter D50 and bulk density as a correction set;
3) converting the acquired acoustic signal data from a time domain signal to a frequency domain signal through fast Fourier transform, equally dividing a frequency spectrum into N sections, and calculating an average value of each section to obtain a segmented average frequency spectrum containing N variables;
4) using the correction set to establish a multivariate quantitative correction model of the particle properties;
5) the multivariate quantitative correction model is applied to the detection of the particle attributes in the preparation process of the traditional Chinese medicine particles in the fluidized bed.
2. The acoustic emission testing method for particle properties in fluidized bed traditional Chinese medicine granule preparation process as claimed in claim 1, wherein in step (1), said acoustic emission sensor is disposed at the level of the sampling port of the loading chamber of said fluidized bed.
3. The method for acoustic emission detection of granule attributes in fluidized bed traditional Chinese medicine granule preparation process as claimed in claim 1, wherein in step (2), said acoustic signal is collected after each sampling in the fluidized bed granulation process.
4. The acoustic emission detection method for granule attributes in the fluidized bed traditional Chinese medicine granule preparation process according to claim 1, wherein in the step (2), n is greater than or equal to 6.
5. The acoustic emission detection method for particle attributes in the fluidized bed traditional Chinese medicine particle preparation process as claimed in claim 1, wherein in the step (3), the frequency spectrum range is 50-320 KHz.
6. The method for acoustic emission detection of granule attributes in fluidized bed traditional Chinese medicine granule preparation process as claimed in claim 1, wherein said multivariate quantitative calibration model in step (4) is established by partial least squares regression (PLS) algorithm.
7. A fluidized bed traditional Chinese medicine granule preparation process granule attribute acoustic emission detection system is characterized by comprising
The acoustic emission sensor is arranged on the fluidized bed body and used for receiving acoustic signal data of the fluidized bed;
the amplifier and the acquisition card are used for amplifying and acquiring the acoustic signal data at fixed time intervals;
the computer system comprises a data preprocessing program module and a multivariate quantitative correction model program module, wherein the data preprocessing program module converts the acoustic signal data from a time domain signal into a frequency domain signal through fast Fourier transform, equally divides a frequency spectrum into N sections, and calculates an average value of each section to obtain a segmented average frequency spectrum containing N variables; the multivariate quantitative correction model program module predicts the particle properties including the moisture content of the particles, the average particle size D50 and the bulk density based on the segmented average frequency spectrum.
8. The fluidized bed herbal granule preparation process particle attribute acoustic emission detection system of claim 6 wherein said acoustic emission sensor is positioned at the level of a thief hatch of a loading chamber of said fluidized bed.
9. The acoustic emission detection system for particle attributes in the fluidized bed traditional Chinese medicine particle preparation process as claimed in claim 6, wherein said spectral range is 50-320 KHz.
10. The acoustic emission detection system for particle attributes generated during the fluidized bed process of preparing chinese herbal medicine particles as claimed in claim 6, wherein said multivariate quantitative calibration model program module is established using a partial least squares regression (PLS) algorithm.
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Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN1496479A (en) * | 2001-03-08 | 2004-05-12 | ŵ��ø�ɷ�����˾ | Method of analysing granular composition by acoustic emission |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN1496479A (en) * | 2001-03-08 | 2004-05-12 | ŵ��ø�ɷ�����˾ | Method of analysing granular composition by acoustic emission |
Non-Patent Citations (1)
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| MICHELLE K等: "Monitoring of High-shear Granulation using Acoustic Emission: Predicting Granule Properties", 《J PHARM INNOV》 * |
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