Method for detecting particle suspension effect in stirred reactor

Abstract

The present application belongs to the field of chemical equipment monitoring and control technology, and particularly relates to a kind of detection methods of particle suspension effect in stirred reactor, comprising: preparing fluorescent small balls of different sizes;Liquid is injected into the stirred reactor, all fluorescent small balls are put into the liquid, and the liquid is stirred by the stirrer;The stirred reactor is irradiated by using ultraviolet lamp, so that the fluorescent small balls in the stirred reactor emit fluorescence;The real-time monitoring module is used to collect the image of the fluorescent small balls in the stirred reactor;The fluorescent small ball image is analyzed to obtain the motion trajectory, instantaneous speed and direction of the fluorescent small balls;According to the motion trajectory, instantaneous speed and direction of the fluorescent small balls, the particle suspension effect in the liquid is obtained;The high-speed camera and intelligent image processing module can capture the particle suspension state in real time, which significantly improves the detection efficiency.

Description

Technical Field

[0001] This invention belongs to the field of chemical equipment monitoring and control technology, specifically relating to a method for detecting particle suspension effect in a stirred reactor. Background Technology

[0002] Stirred reactors are crucial equipment in chemical, pharmaceutical, food, and environmental protection industries. Their core functions are to achieve uniform mixing of materials, promote reaction, and facilitate heat and mass transfer. Mixing performance is a key indicator of stirred reactor efficiency, directly impacting reaction rate, conversion rate, product quality uniformity, and production safety. Traditional monitoring methods rely on indirect signals such as pH, oxidation-reduction potential (ORP), pressure, and torque. These parameters are insufficient to comprehensively and in real-time reflect the true internal disturbance state and mixing efficiency of the fluid, exhibiting certain lag and inaccuracies. In recent years, solid-liquid mixing technology has been widely applied in chemical, pharmaceutical, food processing, and environmental protection fields. The evaluation of particle suspension is a critical process parameter in solid-liquid mixing, directly affecting mixing efficiency, reaction kinetics, product quality uniformity, and system energy efficiency. How to quickly and accurately monitor particle suspension state and particle motion characteristics, especially the instantaneous velocity of particle motion, has always been a hot topic for researchers. Traditional solid-liquid mixing monitoring methods mainly rely on the acquisition of indirect signals, such as measuring particle concentration gradients, changes in suspension viscosity, and changes in conductivity to indirectly reflect particle suspension state. However, these methods have limitations such as insufficient real-time performance and high complexity. How to monitor the suspension state and instantaneous velocity of particles in real time and accurately remains a challenging research direction. Summary of the Invention

[0003] To address the problems existing in the prior art, this invention proposes a method for detecting particle suspension effect in a stirred reactor. The method includes: preparing fluorescent microspheres of different sizes; injecting liquid into the stirred reactor, placing all the fluorescent microspheres in the liquid, and stirring the liquid using a stirrer; irradiating the stirred reactor with an ultraviolet lamp to cause the fluorescent microspheres in the stirred reactor to emit fluorescence; acquiring images of the fluorescent microspheres in the stirred reactor using a real-time monitoring module; analyzing the images of the fluorescent microspheres to obtain their motion trajectory, instantaneous velocity, and direction; and determining the particle suspension effect in the liquid based on the motion trajectory, instantaneous velocity, and direction of the fluorescent microspheres.

[0004] The beneficial effects of this invention are:

[0005] This invention utilizes a high-speed camera and an intelligent image processing module to capture the suspended state of particles in real time, significantly improving detection efficiency. Furthermore, it employs optical detection technology, eliminating the need for damage or intrusion into the equipment's interior, thus minimizing potential harm. The detection equipment of this invention is low-cost and suitable for large-scale industrial applications. Attached Figure Description

[0006] Figure 1 This is a schematic diagram of the fluorescent microspheres of the present invention;

[0007] Figure 2 This is a schematic diagram of the rapid monitoring device for particle suspension effect in the stirred reactor of the present invention;

[0008] Figure 3 This is an experimental diagram of the hybrid module of the present invention;

[0009] Figure 4 This is the overall flowchart of the present invention. Detailed Implementation

[0010] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0011] A method for detecting particle suspension effect in a stirred reactor, such as Figure 4 As shown, the method includes: preparing fluorescent microspheres of different sizes; injecting liquid into a stirred reactor, placing all the fluorescent microspheres into the liquid, and stirring the liquid with a stirrer; irradiating the stirred reactor with an ultraviolet lamp to make the fluorescent microspheres in the stirred reactor emit fluorescence; acquiring images of the fluorescent microspheres in the stirred reactor using a real-time monitoring module; analyzing the images of the fluorescent microspheres to obtain the motion trajectory, instantaneous velocity, and direction of the fluorescent microspheres; and obtaining the particle suspension effect in the liquid based on the motion trajectory, instantaneous velocity, and direction of the fluorescent microspheres.

[0012] Example 1

[0013] Fluorescent microspheres: Prepared using high-quality luminescent powders available on the market to ensure stable and reliable luminescent performance, enabling them to emit fluorescence stably under ultraviolet light irradiation. A schematic diagram of the fluorescent microspheres is shown below. Figure 1 As shown.

[0014] Mixing module: An agitator shaft and rigid impellers are installed inside the container. The agitator shaft is connected to the prime mover via a coupling, and the rigid impellers are mounted on the nodes of the agitator shaft.

[0015] Real-time monitoring module: A high-resolution, high-frame-rate high-speed camera is selected to ensure it can clearly capture the dynamic behavior of the fluorescent spheres under ultraviolet light. Simultaneously, a high-performance computer is prepared to receive and store the image data acquired by the high-speed camera.

[0016] Intelligent Image Analysis Module: Install MATLAB processing software on the computer and write corresponding image processing and analysis algorithms to analyze images acquired by high-speed cameras in real time and extract the perturbation pattern features of fluorescent spheres.

[0017] In this embodiment, a schematic diagram of the rapid monitoring device for particle suspension effect in the stirred reactor is shown below. Figure 2 As shown, the system includes: a UV lamp 2, a rigid turbine blade 3, a stirring shaft 4, a container 5, a prime mover 6, a high-speed camera 7, a computer device 8, and an image analysis and calculation module 9. The UV lamps 2 are distributed around the container 5 to provide UV light to the fluorescent spheres 1 inside the container. The rigid turbine blade 3 is connected to one end of the stirring shaft 4, and the other end of the stirring shaft 4 is connected to the prime mover 6. The prime mover 6 rotates the stirring shaft 4, thereby rotating the rigid turbine blade 3, which in turn ensures that the fluorescent spheres 1 are evenly mixed in the solution. The high-speed camera 7 is used to capture the position of the fluorescent spheres 1 in the container 5 and generate images that are input to the computer device 8. The image analysis and calculation module 9 is located inside the computer device 8. It analyzes and calculates multiple images of the fluorescent spheres to obtain the particle suspension effect within the stirred reactor.

[0018] Example 2

[0019] Preparation of fluorescent microspheres of different sizes includes: obtaining appropriate amounts of ZnS:Cu 2+ @Al2O3 luminescent powder is mixed with polydimethylsiloxane (PDMS) at a mass ratio of 1:10 to obtain a mixed colloid. The mixed colloid is poured into spherical molds of different sizes and dried. After the colloid is cured, the spheres are modified to obtain fluorescent spheres of different sizes.

[0020] Specifically, select luminescent powder with excellent luminescent properties. According to design requirements, mix the luminescent powder with a colloid and shape it using a mold. Treat the colloid within the mold, primarily removing impurities such as air bubbles and dust. Place the molded flexible blades in a well-ventilated, dust-free environment to dry or air dry naturally. Control the drying temperature and time to avoid incomplete curing or cracking. After curing, check the fluorescence effect of the fluorescent microspheres. If necessary, use the colloid for localized repairs or re-prepare to ensure durable fluorescence and high sphericity. The fluorescent microspheres produced at this stage are fluorescent microsphere 1.

[0021] Fluorescent spheres were placed in a stirring container, and images were captured using a high-speed camera.

[0022] Example 3

[0023] Assemble the mixing modules according to design requirements and add a transparent fluid to container 5. Start motor 6 and set the rotation speed of the stirring shaft. After starting the stirring, turn on the ultraviolet light source to irradiate the fluorescent spheres. Start the high-speed camera to acquire image data inside the stirred reactor in real time and transmit it to the computer for storage. Use the intelligent image analysis module to process and analyze the acquired image data, extracting the disturbance pattern characteristics of the fluorescent spheres, such as their movement speed and trajectory. Based on the analysis results, evaluate the suspension effect of the particles and adjust the rotation speed of the stirring shaft or optimize the design of the stirred reactor as needed.

[0024] Example 4

[0025] This invention was applied to a stirred reactor in a large chemical enterprise. The reactor is mainly used for mixing and reacting two chemical raw materials, one being solid particles and the other a liquid.

[0026] The system was constructed and run according to the methods in Examples 1, 2, and 3, and the motion patterns of small balls with different rotation speeds and different sizes, numbers, and densities were compared and analyzed. Based on the collected data and analysis results, the rotation speed, particle size, and number of particles that can improve the mixing performance of the stirred reactor and make the material mixing more uniform and efficient were selected.

[0027] This invention employs a non-invasive monitoring method, avoiding interference and damage to the stirring process caused by traditional measurement methods, thereby reducing production costs and improving production efficiency.

[0028] Example 4

[0029] In this embodiment, the analysis of the fluorescent microsphere image includes:

[0030] Step 1: The fluorescent sphere image is denoised using nonlocal mean denoising and wavelet threshold filtering to obtain the enhanced fluorescent sphere image.

[0031] Step 2: The enhanced fluorescent sphere image is processed using a recognition algorithm based on grayscale threshold and shape factor to identify the spatial coordinates of the particles;

[0032] Step 3: Obtain images at different times and use them as time series images; extract images of the same position and pixels at different times from the time series images, and use the cross-correlation method to obtain pixel-level displacement; establish a polynomial model for known displacement pixels within a local window, solve the coefficients using the least squares method, and then substitute the coordinates of unknown displacement pixels to complete the interpolation.

[0033] Defining the local window involves: selecting a 7×7 local window centered on the pixel whose displacement to be completed (the number of effective displacement pixels within the window should not be less than the number of polynomial coefficients). Then, filtering the pixels within the window whose reliable displacements (displacements in the u and y directions) have been obtained through cross-correlation, and recording their coordinates (x, y). i ,y i ) and corresponding displacement value (u) i ,v i To establish the mapping relationship between coordinates and displacements in the xy-plane, a polynomial equation is constructed. Taking the displacement u in the x-direction as an example, the model is as follows:

[0034]

[0035] Where a0-a5 are the coefficients to be determined (the same applies to the direction of v).

[0036] Substituting the (x_i, y_i, u_i) values ​​of the effective pixels within the window into the model, an overdetermined system of equations is constructed. The sum of squared errors is minimized using the least squares method to solve for all polynomial coefficients. Finally, substituting the pixel coordinates (x0, y0) of the pixel whose displacement to be completed within the window into the solved polynomials, the fitted displacement value (u0, v0) of that pixel can be directly calculated, yielding the sub-pixel level displacement. The velocity vector is then calculated based on the pixel-level displacement, the sub-pixel-level displacement, and the time interval.

[0037] x-direction velocity component:

[0038]

[0039] y-direction velocity component:

[0040]

[0041] in, It represents the pixel-level displacement (integer) in the x-direction. The pixel-level displacement (integer) in the y-direction. For subpixel-level displacement in the x-direction (decimal, range [0,1)), Δt is the subpixel displacement in the y-direction (decimal, range [0,1)), Δt is the time interval (the time difference between two adjacent frames, unit: s), and k is the physical pixel equivalent (the actual physical length corresponding to 1 pixel, such as μm / px).

[0042] Step 4: Set a velocity threshold. Calculate the region in the pixel flow field that is less than the velocity threshold based on the velocity vector, and turn this region white, while the remaining regions are processed in grayscale. Calculate the area of ​​the white region using image measurement functions: First, count the number of white pixels in the image, then convert it to the actual physical area using pixel equivalents. Mainstream image processing libraries (OpenCV, MATLAB) have ready-made functions that can be directly called. Taking the rgb2gray function in MATLAB as an example: First, calculate the pixel area and actual physical area of ​​the white region in the image; Input: img - single-channel grayscale image (white=255, grayscale=0-254); pixel_scale - physical pixel equivalent; Output: pixel_area - pixel area (px²); physical_area - actual physical area; Verify single-channel: if it is color, convert to grayscale; size(img, 3) = 3, then img = rgb2gray(img); Generate a binary image (white=1, others=0), and count the number of white pixels; binary_img = img = 255; pixel_area = sum(binary_img(:)); physical_area = pixel_area * (pixel_scale ^ 2). Then, by comparing the proportion of gray and white areas under different conditions, the floating effect of the particles is obtained. The floating effect is as follows: Figure 3 As shown.

[0043] Step 5: Denoise the time series image, calculate the two-dimensional box fractal dimension of the denoised time series image, and binarize the image; determine the box size based on the image pixel size; calculate the number of objects in each box in the image; the imboxft function in MATLAB can be directly used to perform box filtering on the image, and the sum function is used to calculate the number of pixels with a value of 1 in each box, and the relationship between box size and box number is plotted on a log-log plot; linearly fit the data in the log-log plot and calculate the slope; determine the image complexity based on the slope.

[0044] Fractal dimension F d It can be estimated by performing a least-squares fit on log(Nr) and log(1 / r):

[0045]

[0046] Where y = log(Nr), x = log(1 / r), r is the partition ratio, and Nr is the total number of boxes in all grids.

[0047] Step 6: Establish the correlation function between suspension state and stirring speed, and determine the critical suspension speed N by fitting the curve. c .

[0048] In this embodiment, the denoising and filtering of the fluorescent ball image using nonlocal mean denoising and wavelet threshold filtering includes: similarity measurement (Euclidean distance): for a pixel (i,j) to be denoised in image I (denoised as the center block P(i,j)), the similarity between the image block P(k,l) corresponding to any pixel (k,l) within the search window and the pixel block P(k,l) is measured by normalized Euclidean distance.

[0049]

[0050] Where m is the size of the similarity block (in fluorescence images, m = 3~5; if it is too small, the similarity is easily misjudged, and if it is too large, the computational load increases dramatically).

[0051] Weight calculation:

[0052]

[0053] Where h: smoothing parameter (h=5~15 in fluorescence image, the larger h is, the stronger the noise reduction but the more blurred the details, and it needs to be adjusted according to the noise intensity); Ω: search window (Ω=7~11 in fluorescence image, just cover the local similar area of ​​the ball, and avoid introducing background noise by searching too large a range).

[0054] Pixel values ​​after noise reduction:

[0055]

[0056] The final pixel value is the weighted sum of all pixels within the search window, with similar blocks contributing a larger weight.

[0057] In this embodiment, the enhanced fluorescent sphere image is processed using a recognition algorithm based on grayscale threshold and shape factor, including:

[0058] Input: A single-channel grayscale image I of the fluorescent spheres after denoising / enhancement (sphere grayscale value > background grayscale value): Based on the image grayscale histogram, manually select a threshold T (usually the valley between the "background peak" and the "sphere peak"). Set an adaptive / manual threshold to binarize the image, obtaining candidate regions containing spheres and a small amount of impurities.

[0059] Binarization rules:

[0060]

[0061] Post-segmentation processing: Small noise points are removed and internal holes of the spheres are filled using morphological operations (dilation + erosion); all independent candidate connected regions are marked, and the geometric features of each region (area, perimeter, bounding rectangle, etc.) are calculated; all independent white connected regions in the binary image are found (each region corresponds to one candidate target), and the geometric features of the region are extracted. Erosion is performed first, followed by dilation (opening operation), with the formula based on pixel traversal of the structuring element.

[0062] Corrosion formula:

[0063]

[0064] Expansion formula:

[0065]

[0066] Shape factor filtering: Set a shape factor threshold to eliminate non-circular regions and retain real fluorescent spheres; output the target region mask of the sphere, the center coordinates of each sphere, radius and other core parameters (to prepare for subsequent displacement calculations).

[0067] Circularity C: Measures how closely a region approximates a perfect circle. The smaller the perimeter and the larger the area, the closer the circularity is to 1. Non-circular regions (such as rectangles and irregular shapes) have larger perimeters and lower circularity. Its ideal value is C=1. Its expression is:

[0068]

[0069] Aspect Ratio (AR): Measures the aspect ratio of the circumscribed rectangle of an area. A perfectly circular circumscribed rectangle is a square, with an aspect ratio of 1. Slender impurities have an aspect ratio much less than 1. Its ideal value is AR = 1. Its expression is:

[0070]

[0071] Compactness (Comp): Measures the proportion of a region to its circumscribed rectangle: For a perfect circle, compactness ≈ 0.785; irregular regions have lower compactness. The ideal value is Comp = 0.785. Its expression is:

[0072]

[0073] Solidity S: Measures the degree of solidity of the region: Fluorescent spheres are solid, with a solidity of ≈1; hollow impurities or broken regions have lower solidity. The ideal value is S=1. Its expression is:

[0074]

[0075] in, Let be the area of ​​the convex hull of the region.

[0076] In this embodiment, obtaining pixel-level displacement using the cross-correlation method includes: using the fluorescent sphere in the reference frame as a template, finding the best matching position within the search area of ​​the target frame through normalized cross-correlation (NCC), calculating the difference in center coordinates between the two spheres, and obtaining the pixel-level displacement.

[0077] (1) Template and search area definition:

[0078] Template ROI (Reference Frame I) t Center of the ball equivalent radius )

[0079] Side length Coordinate range

[0080] (2) Normalized cross-correlation (NCC)

[0081]

[0082] in The average gray level of the template. The average gray level of the sub-regions in the search area.

[0083] In this embodiment, establishing the correlation function between the suspension state and the stirring speed includes: quantifying experimental data on the suspension state (suspension rate) and the stirring speed to obtain a nonlinear correlation function, which is commonly described by logarithmic or S-shaped curves.

[0084] (1) Logarithmic correlation function (applicable to low to medium speed range, with suspension rate gradually increasing)

[0085]

[0086] Where η is the suspension rate (area of ​​white suspended region / total area, 0~1), n ​​is the stirring speed (r / min), n0 is the critical speed (the minimum speed at which suspension just begins), and a and b are experimental fitting coefficients (positive numbers, reflecting the rate at which the suspension rate increases with the stirring speed).

[0087] (2) S-shaped correlation function (adapted to the entire speed range, including the saturation stage)

[0088]

[0089] Where η max Where n is the saturation suspension rate (maximum suspension level, ≈1), k is the growth rate coefficient, and n is the saturation suspension rate (maximum suspension level, ≈1). c Characteristic rotational speed (suspension rate reaches η) max Rotational speed at / 2).

[0090] The above-described embodiments further illustrate the purpose, technical solution, and advantages of the present invention. It should be understood that the above-described embodiments are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made to the present invention within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for detecting particle suspension effect in a stirred reactor, characterized in that, include: Prepare fluorescent microspheres of different sizes; Liquid is injected into the stirred reactor, all the fluorescent beads are placed in the liquid, and the liquid is stirred by a stirrer; An ultraviolet lamp is used to irradiate a stirred reactor, causing fluorescent spheres inside the reactor to fluoresce. A real-time monitoring module is used to acquire images of the fluorescent spheres inside the stirred reactor. The images of the fluorescent spheres are analyzed to obtain their trajectory, instantaneous velocity, and direction. Based on the trajectory, instantaneous velocity, and direction of the fluorescent spheres, the effect of particle suspension in the liquid is obtained. Analysis of the fluorescent sphere images includes: Step 1: The fluorescent sphere image is denoised using nonlocal mean denoising and wavelet threshold filtering to obtain the enhanced fluorescent sphere image. Step 2: The enhanced fluorescent sphere image is processed using a recognition algorithm based on grayscale threshold and shape factor to identify the spatial coordinates of the particles; Step 3: Acquire images at different times and use them as time series images; extract images of the same position and pixels at different times from the time series images, and use the cross-correlation method to obtain pixel-level displacement; establish a polynomial model for known displacement pixels within a local window, and solve the polynomial model using the least squares method to obtain the coefficients; substitute the coordinates of unknown displacement pixels into the polynomial model to complete the interpolation; construct an overdetermined system of equations based on the interpolated data, and solve for all polynomial coefficients by minimizing the sum of squared errors using the least squares method; substitute the pixel coordinates (x0, y0) of the displacement to be completed within the window into the solved polynomial to obtain the fitted displacement value (u0, v0) of that pixel, thus obtaining the sub-pixel-level displacement; calculate the velocity vector based on the pixel-level displacement, the sub-pixel-level displacement, and the time interval. Step 4: Set a velocity threshold. Calculate the region in the pixel flow field that is less than the velocity threshold based on the velocity vector, and turn this region white, while the remaining regions are processed in grayscale. Use an image measurement function to calculate the area of ​​the white region. By comparing the proportion of gray and white regions under different conditions, the suspension effect of the particles is obtained. Step 5: Denoise the time series image, calculate the two-dimensional box fractal dimension of the denoised time series image, and binarize the image; determine the box size based on the image pixel size; calculate the number of objects in each box in the image; use the imboxft function to perform box filtering on the image, use the sum function to calculate the number of pixels with a value of 1 in each box, and plot the relationship between box size and box number on a log-log plot; linearly fit the data in the log-log plot and calculate the slope; determine the image complexity based on the slope. Step 6: Establish a correlation function between the suspension state and the stirring speed based on the complexity of the image, and determine the critical suspension speed N by fitting the curve. c .

2. The method for detecting particle suspension effect in a stirred reactor according to claim 1, characterized in that, Preparation of fluorescent microspheres of different sizes includes: obtaining ZnS:Cu 2+ @Al2O3 luminescent powder is mixed with polydimethylsiloxane (PDMS) at a mass ratio of 1:10 to obtain a mixed colloid. The mixed colloid is poured into spherical molds of different sizes and dried. After the colloid is cured, the spheres are modified to obtain fluorescent spheres of different sizes.

3. The method for detecting particle suspension effect in a stirred reactor according to claim 1, characterized in that, The agitator includes a prime mover, an agitator shaft, and a rigid turbine blade; the rigid turbine blade is connected to one end of the agitator shaft, and the other end of the agitator shaft is connected to the prime mover. When the prime mover rotates, it drives the agitator shaft and the rigid turbine blade to rotate.

4. The method for detecting particle suspension effect in a stirred reactor according to claim 1, characterized in that, The real-time monitoring module uses a high-resolution, high-frame-rate high-speed camera.

5. The method for detecting particle suspension effect in a stirred reactor according to claim 1, characterized in that, The denoising and filtering process for fluorescent sphere images using nonlocal mean denoising and wavelet threshold filtering includes: for a pixel (i,j) to be denoised in image I, the image block P(k,l) corresponding to any pixel (k,l) within the search window is used to calculate the similarity between the two; weights are constructed based on the similarity; the pixels are filtered based on the weights to obtain the denoised pixel values; and the pixel values ​​are the weighted sum of all pixels within the search window to obtain the final denoised image.

6. The method for detecting particle suspension effect in a stirred reactor according to claim 1, characterized in that, The enhanced fluorescent sphere image is processed using a recognition algorithm based on grayscale thresholding and shape factor. The process includes: binarizing the denoised single-channel grayscale image I of the fluorescent sphere to obtain candidate regions containing the sphere and a small amount of impurities; removing small noise points and filling internal holes in the sphere through morphological operations; labeling all independent candidate connected regions and calculating the geometric features of each region; finding all independent white connected regions in the binary image and extracting their geometric features; setting a shape factor threshold to eliminate non-circular regions and retaining the true fluorescent spheres; and outputting the sphere target region mask, the center coordinates of each sphere, and its radius parameter.

7. The method for detecting particle suspension effect in a stirred reactor according to claim 1, characterized in that, Obtaining pixel-level displacement using the cross-correlation method involves: using the fluorescent ball in the reference frame as a template, finding the best matching position within the search area of ​​the target frame through normalized cross-correlation, calculating the difference in center coordinates between the two frames, and obtaining the pixel-level displacement.

8. The method for detecting particle suspension effect in a stirred reactor according to claim 1, characterized in that, Establishing the correlation function between suspension state and stirring speed involves: quantifying experimental data on suspension state and stirring speed, and fitting a nonlinear correlation function; where the nonlinear correlation function is either a logarithmic correlation function or a sigmoid correlation function.

9. The method for detecting particle suspension effect in a stirred reactor according to claim 8, characterized in that, The logarithmic correlation function is: ； Where η is the suspension rate, n is the stirring speed, n0 is the critical speed, and a and b are experimental fitting coefficients; The S-type correlation function is: ； Where η max Where n is the saturation suspension ratio, k is the growth rate coefficient, and n is the saturation suspension ratio. c The characteristic rotational speed.

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