Method and device for acoustically monitoring the operating state of a catalytic cracking unit

Abstract

This invention provides an acoustic monitoring method and device for the operating status of a catalytic cracking unit. The catalytic cracking unit includes a multi-stage wing valve, each stage comprising multiple wing valves. The method includes: acquiring acoustic signals of the wing valves during a certain operating time; performing data preprocessing on the acoustic signals to obtain processed acoustic signals; determining the flow rate percentage of each wing valve during the operating time based on the processed acoustic signals; and determining the operating status of the wing valves during the operating time based on the flow rate percentages of the wing valves. The operating status of the wing valves during the operating time is used to characterize the operating status of the catalytic cracking unit. The acoustic monitoring method and device for the operating status of the catalytic cracking unit of this invention have the advantage of accurately determining the operating status of the wing valves through the flow rate percentages of each wing valve, thereby guiding the adjustment of process parameters of the catalytic cracking unit, providing a basis for production operation, and enabling the catalytic cracking unit to maintain stable production.

Description

Technical Field

[0001] This invention relates to the field of acoustic monitoring technology for petrochemical plants, specifically to an acoustic monitoring method for the operating status of a catalytic cracking unit, an acoustic monitoring device for the operating status of a catalytic cracking unit, a machine-readable storage medium, and a terminal device. Background Technology

[0002] Currently, the trend towards larger settling tanks and regenerators in catalytic cracking units of refining and chemical enterprises has highlighted the problem of fluidized bed flow deviation, which can easily lead to issues such as wear of swirl valves, cracking of feed legs, and wear of the main air distributor. By monitoring the status of the swirl valves and analyzing and comparing the working intensity of each valve, the state of catalyst flow deviation can be inferred, thereby guiding the adjustment of process parameters and reducing the risk of flow deviation.

[0003] Currently, there is no real-time monitoring method for the operating status of the discharge valves in the settler and regenerator of catalytic cracking units. Existing technologies often use infrared cameras to detect the valve status. However, due to the high internal temperatures and fluidized catalyst circulation in the settler and regenerator, internal monitoring methods such as video surveillance cannot effectively monitor the operating status of the valves in the catalytic cracking unit, thus failing to accurately obtain the operating status of the catalytic cracking unit and provide a reference for adjusting process parameters. Summary of the Invention

[0004] The purpose of this invention is to provide an acoustic monitoring method and device for the operating status of a catalytic cracking unit, in order to solve the problem that the aforementioned internal monitoring methods such as video surveillance cannot effectively monitor the operating status of the flank valves of the catalytic cracking unit, and cannot accurately obtain the operating status of the catalytic cracking unit, thus failing to provide a reference for the adjustment of process parameters of the catalytic cracking unit.

[0005] To achieve the above objectives, embodiments of the present invention provide an acoustic monitoring method for the operating status of a catalytic cracking unit. The catalytic cracking unit includes a multi-stage wing valve, each stage of which includes multiple wing valves. The method includes:

[0006] Acquire the acoustic signal of the wing valve during a certain operating time;

[0007] The acoustic signal is preprocessed to obtain the processed acoustic signal;

[0008] Based on the processed acoustic signal, the flow rate percentage of each wing valve during this operating period is determined.

[0009] Based on the flow rate ratio of the wing valve, the operating status of the wing valve during this operating period is determined, and the operating status of the wing valve during this operating period is used to characterize the operating status of the catalytic cracking unit.

[0010] Optionally, the acoustic signal is the energy parameter of the wing valve during a certain operating time.

[0011] Optionally, the acoustic signal is preprocessed to obtain a processed acoustic signal, including:

[0012] For each wing valve:

[0013] Parameters in the acoustic signal whose energy value is greater than the first energy threshold are defined as the first valid signal;

[0014] The first effective signal is corrected using clustering algorithms, the Laida criterion, the quartile method, and cross-validation to obtain the processed acoustic signal.

[0015] Optionally, the first effective signal is corrected using clustering algorithms, the Laida criterion, the quartile method, and cross-validation to obtain the processed acoustic signal, including:

[0016] For each wing valve:

[0017] Using the K-means clustering algorithm, the first effective signal of the wing valve is classified to obtain the first type of energy parameters and the second type of energy parameters of the wing valve, wherein the energy value of each energy parameter in the first type of energy parameters is greater than the energy parameter with the largest energy value in the second type of energy parameters;

[0018] All energy parameters in the first category of energy parameters of the wing valve are sorted in ascending order of energy value to form a box plot. The upper limit value is determined in the box plot as the second energy threshold of the wing valve.

[0019] Based on the first valid signal of the wing valve, the third energy threshold of the wing valve is determined using the Laida criterion;

[0020] The energy parameters with energy values ​​greater than the second and third energy thresholds in the first category of energy parameters of the wing valve are removed, and the remaining energy parameters are used as the first acoustic characteristic parameters.

[0021] The original acoustic signal corresponding to the second type of energy parameter of the wing valve is divided into K subsets. Based on K-fold cross-validation, an autoencoder is used for training and testing. The subsets with reconstruction errors greater than a set threshold after testing are deleted to obtain the remaining energy feature parameters, which are used as the second acoustic feature parameters.

[0022] The first acoustic characteristic parameter and the second acoustic characteristic parameter are used as the processed acoustic signal of the wing valve.

[0023] Optionally, all energy parameters in the first category of energy parameters of the wing valve are sorted in ascending order of energy value to form a box plot. The upper threshold value in the box plot is determined as the second energy threshold of the wing valve, including:

[0024] The energy values ​​of the energy parameters located at the 25th percentile of the box plot are taken as the upper quartile, and the energy values ​​of the energy parameters located at the 75th percentile of the box plot are taken as the lower quartile.

[0025] The upper limit value is calculated using the following formula and used as the second energy threshold of the wing valve:

[0026] E2 = Q3 + 1.5(Q3 - Q1)

[0027] Q3 is the upper quartile; Q1 is the lower quartile.

[0028] Optionally, based on the first valid signal of the wing valve, a third energy threshold of the wing valve is determined using the Raida criterion, including:

[0029] Obtain the mean energy and standard deviation of all energy parameters in the first valid signal of the wing valve;

[0030] The third energy threshold of the wing valve is calculated using the following formula:

[0031] E3=μ+3σ

[0032] Where E3 is the third energy threshold; μ is the mean energy of all energy parameters in the first valid signal; and σ is the standard deviation of the energy of all energy parameters in the first valid signal.

[0033] Optionally, based on the processed acoustic signal, the flow rate percentage of each wing valve during this operating period can be determined, including:

[0034] The flow rate percentage of each wing valve in each stage of the wing valve can be calculated using the following formula:

[0035]

[0036] Among them, Q ij E represents the flow rate percentage of the j-th wing valve in the i-th stage. ij is the sum of the processed acoustic signal energy values ​​of the j-th wing valve in the i-th stage, and n is the number of wing valves in the i-th stage.

[0037] A second aspect of the present invention provides an acoustic monitoring device for the operating status of a catalytic cracking unit, the catalytic cracking unit comprising a multi-stage wing valve, each stage comprising multiple wing valves, the device comprising:

[0038] The data acquisition module is used to acquire the acoustic signals of the wing valve during a certain operating time.

[0039] The data processing module is used to preprocess the acoustic signal to obtain the processed acoustic signal;

[0040] The flow percentage determination module is used to determine the flow percentage of each wing valve during the operating time based on the processed acoustic signal.

[0041] The operating status determination module is used to determine the operating status of the wing valve within a certain operating time based on the flow ratio of the wing valve. The operating status of the wing valve within this operating time is used to characterize the operating status of the catalytic cracking unit.

[0042] Optionally, the data processing module is specifically used for:

[0043] For each wing valve:

[0044] The parameters in the acoustic signal whose energy value is greater than the first energy threshold are determined as the first valid signal;

[0045] The first effective signal is corrected using clustering algorithms, the Laida criterion, the quartile method, and cross-validation to obtain the processed acoustic signal.

[0046] Optionally, the first effective signal is corrected using clustering algorithms, the Laida criterion, the quartile method, and cross-validation to obtain the processed acoustic signal, including:

[0047] For each wing valve:

[0048] Using the K-means clustering algorithm, the first effective signal of the wing valve is classified to obtain the first type of energy parameters and the second type of energy parameters of the wing valve, wherein the energy value of each energy parameter in the first type of energy parameters is greater than the energy parameter with the largest energy value in the second type of energy parameters;

[0049] All energy parameters in the first category of energy parameters of the wing valve are sorted in ascending order of energy value to form a box plot. The upper limit value is determined in the box plot as the second energy threshold of the wing valve.

[0050] Based on the first valid signal of the wing valve, the third energy threshold of the wing valve is determined using the Laida criterion;

[0051] The energy parameters with energy values ​​greater than the second and third energy thresholds in the first category of energy parameters of the wing valve are removed, and the remaining energy parameters are used as the first acoustic characteristic parameters.

[0052] The original acoustic signal corresponding to the second type of energy parameter of the wing valve is divided into K subsets. Based on K-fold cross-validation, an autoencoder is used for training and testing. The subsets with reconstruction errors greater than a set threshold after testing are deleted to obtain the remaining energy feature parameters, which are used as the second acoustic feature parameters.

[0053] The first acoustic characteristic parameter and the second acoustic characteristic parameter are used as the processed acoustic signal of the wing valve.

[0054] A third aspect of the present invention provides a machine-readable storage medium storing instructions for causing a machine to perform the acoustic monitoring method for the operating status of a catalytic cracking unit described above.

[0055] On the other hand, the present invention provides a terminal device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described acoustic monitoring method for the operating status of a catalytic cracking unit.

[0056] This technical solution collects acoustic signals from the wing valves of a catalytic cracking unit during operation. The acoustic signals are processed to ensure accuracy. Then, the flow rate percentage of each wing valve in each stage is determined using the processed acoustic signals, thus identifying the wing valve's operating status during that time period. By analyzing and monitoring multiple wing valves in the same stage using acoustic monitoring methods, the operating status between the wing valves can be accurately determined, thereby identifying the state of the catalytic cracking unit. This guides the adjustment of process parameters, providing a basis for production operation and ensuring stable production.

[0057] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0058] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0059] Figure 1 This is a flowchart of the acoustic monitoring method for the operating status of a catalytic cracking unit provided by the present invention;

[0060] Figure 2 This is a schematic diagram showing the change of energy parameters over time during the operation of the wing valve provided by the present invention;

[0061] Figure 3 This is a schematic diagram of the box plot division position provided by the present invention;

[0062] Figure 4 This is a schematic diagram of the acoustic monitoring device for the operating status of a catalytic cracking unit provided by the present invention;

[0063] Figure 5 This is a schematic diagram of the energy distribution of different wing valves in Embodiment 5 provided by the present invention;

[0064] Figure 6This is a schematic diagram of the flow rate ratio of the same-stage wing valve in Embodiment 5 provided by the present invention.

[0065] Explanation of reference numerals in the attached figures

[0066] 10 - Data Acquisition Module; 20 - Data Processing Module; 30 - Traffic Ratio Determination Module;

[0067] 40 - Module for determining running status. Detailed Implementation

[0068] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0069] In the embodiments of the present invention, unless otherwise stated, directional terms such as "up," "down," "left," and "right" generally refer to the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship in which the product of the invention is usually placed when in use.

[0070] The terms “first,” “second,” “third,” etc., are used only to distinguish descriptions and should not be interpreted as indicating or implying relative importance.

[0071] The terms "parallel" and "perpendicular" do not mean that the components must be absolutely parallel or perpendicular, but rather that they can be slightly tilted. For example, "parallel" simply means that its direction is more parallel than "perpendicular," not that the structure must be completely parallel, but that it can be slightly tilted.

[0072] The terms "horizontal," "vertical," and "sag" do not imply that a component must be absolutely horizontal, vertical, or sagging, but rather that it can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal than "vertical," not that the structure must be completely horizontal, but can be slightly tilted.

[0073] Furthermore, terms like "roughly" and "basically" are used to indicate that the content does not require absolute precision, but rather allows for a certain degree of deviation. For example, "roughly equal" does not simply mean absolute equality; in actual production and operation, achieving absolute "equality" is difficult, and a certain degree of deviation is generally present. Therefore, besides absolute equality, "roughly equal to" also includes the aforementioned situation where a certain degree of deviation exists. Using this as an example, in other cases, unless otherwise specified, terms like "roughly" and "basically" have similar meanings.

[0074] In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0075] Figure 1 This is a flowchart of the acoustic monitoring method for the operating status of a catalytic cracking unit provided by the present invention; Figure 2 This is a schematic diagram showing the change of energy parameters over time during the operation of the wing valve provided by the present invention; Figure 3 This is a schematic diagram of the box plot division position provided by the present invention; Figure 4 This is a schematic diagram of the acoustic monitoring device for the operating status of a catalytic cracking unit provided by the present invention; Figure 5 This is a schematic diagram of the energy distribution of different wing valves in Embodiment 5 provided by the present invention; Figure 6 This is a schematic diagram of the flow rate ratio of the same-stage wing valve in Embodiment 5 provided by the present invention.

[0076] Example 1

[0077] like Figure 1 As shown, this embodiment of the invention provides an acoustic monitoring method for the operating status of a catalytic cracking unit. The catalytic cracking unit includes a multi-stage wing valve, each stage of which includes multiple wing valves. The method includes:

[0078] Step 101: Acquire the acoustic signal of the wing valve during a certain operating time;

[0079] Step 102: Perform data preprocessing on the acoustic signal to obtain the processed acoustic signal;

[0080] Step 103: Based on the processed acoustic signal, determine the flow rate percentage of each wing valve during the operating time segment;

[0081] Step 104: Based on the flow rate ratio of the wing valve, determine the operating status of the wing valve during this operating period. The operating status of the wing valve during this operating period is used to characterize the operating status of the catalytic cracking unit.

[0082] Specifically, the catalytic cracking unit includes a settling tank and a regenerator, and both the settling tank and the regenerator are equipped with multiple stages of wing valve groups for discharge. Each stage of the wing valve group includes multiple wing valves. During the production process of the catalytic cracking unit, the amount of material discharged from the unit is closely related to the opening and closing of the wing valves and the amount of material discharged in each opening and closing cycle. Since the wing valves are located inside the unit, there is no real-time monitoring method for the working status of the wing valves in the existing technology. Therefore, it is impossible to provide accurate guidance for subsequent processes such as the amount of material discharged. Therefore, this invention provides an acoustic monitoring method for the operating status of a catalytic cracking unit. First, acoustic signals are acquired for each wing valve during a specific operating period. Sensors for acquiring these acoustic signals can be placed near the wing valve plate on the feed leg of the catalytic cracking unit, and sensors can also be placed at the lifting bolts of the catalytic cracking unit to jointly monitor the catalyst's operating status in the feed leg. The acoustic signals are then preprocessed to obtain processed acoustic signals. Preprocessing removes abnormal and useless signals from the acoustic signal acquisition process, ensuring more accurate acoustic characteristic parameters in the processed signals. This allows for a more precise determination of the flow ratio of each wing valve in each stage of the catalytic cracking unit during that operating period, based on the processed acoustic signals. The operating status of the catalytic cracking unit during that period is then determined based on the flow ratio between each stage of the wing valves, after calculating the flow ratio of each wing valve. If the catalyst flow rate of a certain wing valve is high, contributing significantly to production, the catalyst's scouring effect on the feed leg and valve plate of that wing valve is excessive, increasing the risk of catalyst runoff. The calculation of the flow ratio between each stage of the wing valves includes the flow ratio between each stage of the settling tank and the flow ratio between each stage of the regenerator. Operating status includes normal and abnormal operation. Specifically: for the same stage of wing valves, if the calculated flow ratios between the same stage of wing valves are relatively close, and the difference between the flow ratios of the same stage of wing valves is less than or equal to a preset difference value, then the wing valve is determined to be operating normally, confirming that the catalytic cracking unit (settling tank or regenerator) is operating normally; if the calculated difference between the flow ratios of the same stage of wing valves is greater than or equal to a preset difference value, then the wing valve is determined to be operating abnormally, confirming that the catalytic cracking unit (settling tank or regenerator) is not operating normally.

[0083] In this embodiment, the catalyst flows slowly into the feed leg. Due to the obstruction of the wing valve plate, it accumulates inside the feed leg. When the wing valve plate opens, a large amount of catalyst flows out. During the outflow process, friction is generated on the feed leg. In this embodiment, this friction signal is used as the acoustic signal during the operation of each wing valve, rather than the acoustic signal of the valve plate hitting the feed leg during the opening and closing of the wing valve. The acoustic signal of the valve plate hitting the feed leg will be rejected as an abnormal signal to ensure the accuracy of the calculation results.

[0084] The method further includes: when the catalytic cracking unit (sedimenter or regenerator) is operating normally, there is no need to adjust the process parameters of the unit; when the catalytic cracking unit (sedimenter or regenerator) is not operating normally, the operating parameters of the catalytic cracking unit (sedimenter or regenerator), including working pressure and catalyst flow rate, are adjusted to adjust the opening and closing cycle of the corresponding wing valve, so as to ensure the stable operation of the catalytic cracking unit.

[0085] Furthermore, the acoustic signal is the energy parameter of the wing valve during a certain operating time.

[0086] Specifically, in this embodiment, the acoustic signal includes energy parameters. During the opening and closing process of the wing valve, the energy parameters have distinct characteristics and are easily distinguishable, such as... Figure 2 As shown, this allows for accurate monitoring of the catalyst flow process, thereby determining whether the wing valve is open or closed.

[0087] For each wing valve, its energy parameters over a certain operating period can be stored as a separate energy parameter set for easy differentiation and subsequent data processing. The data preprocessing in step 102 above is performed on the energy parameter set corresponding to each wing valve to ensure data accuracy.

[0088] Because a single reactor in a catalytic cracking unit contains multiple wing valves (e.g., a settling tank with six wing valves in one stage), an excessively short opening and closing cycle (high frequency) of any wing valve indicates excessive catalyst flow at that location. This results in excessive scouring of the feed leg and valve plate by the catalyst, potentially causing catalyst runoff and unplanned shutdowns if the feed leg or valve plate wears through. Therefore, ideally, the flow rates of the six wing valves should be equal or nearly equal. This necessitates obtaining the energy parameters for each wing valve within the reactor and using these parameters to determine the flow rate ratios among the wing valves in each stage, thus establishing a comparative relationship between wing valves within the same stage.

[0089] Further, the acoustic signal is preprocessed to obtain a processed acoustic signal, including:

[0090] For each wing valve:

[0091] Energy parameters in acoustic signals with energy values ​​greater than a first energy threshold are defined as the first valid signals.

[0092] The first effective signal is corrected using clustering algorithms, the Laida criterion, the quartile method, and cross-validation to obtain the processed acoustic signal.

[0093] Specifically, by performing preliminary statistical analysis on the collected data, a suitable threshold can be obtained as the first energy threshold. This threshold can filter out the carpet background noise signal in the energy distribution. Because the settling tank generates strong background noise during production and is in a fully enclosed state, coupled with the influence of the waveguide rod on sound wave propagation, it is impossible to obtain the background noise signal and the valve cover opening / closing signal reflecting the flow state of the wing valve. If wavelet denoising is used, the wavelet node coefficients can only be set to the same value, which is equivalent to treating the noise as white noise with a certain energy and performing full-band noise reduction processing. Therefore, by performing statistical analysis on the collected signals, a suitable threshold is defined. This threshold can filter out the carpet background noise signal in the energy distribution, achieving a filtering effect.

[0094] In this embodiment, for a certain wing valve, referring to the wing valve test angle and the amount of balancing catalyst in the settling tank wing valve quality inspection report, a mechanical analysis of the wing valve yields a theoretical opening and closing cycle of 115s. During this time, the wing valve experiences approximately 1928 ÷ 115 ≈ 17 opening and closing cycles. Experiments show that each opening and closing of the wing valve cover generates an average of 2-3 acoustic emission signals. Therefore, the number of effective signals reflecting the opening and closing state of the wing valve is defined as (2-3) × 17 ≈ 50. Since the background noise energy is low and distributed in a carpet-like pattern, while the high-energy acoustic emission signals are generally considered effective signals caused by the opening and closing of the wing valve, the collected acoustic signals are sorted from highest to lowest energy value, and the top 50 signals are taken as effective signals. Thus, the energy threshold is 1.3697 × 10⁻⁶. -4 This allows background noise signals with energy below the first energy threshold to be filtered out, and energy parameters with energy above the first energy threshold to be determined as the first valid signal. The first energy threshold can adaptively separate the valid signal from the carpet-like background noise at the bottom under different environments, different acquisition parameters, and different sampling times.

[0095] Simultaneously, anomalous acoustic emission signals were removed using statistical and clustering methods. The energy value was 3.4838 × 10⁻⁶. -4 The signal belongs to an abnormal state signal, which can be regarded as being generated due to some unexpected impact during the production process. As a singular value, it is judged as an abnormal valid signal. In order to remove the influence of the singular value, the K-means clustering algorithm combined with the Laida criterion is used to remove it. The first valid signal is corrected to obtain the processed acoustic signal.

[0096] Furthermore, the first effective signal is corrected using clustering algorithms, the Laida criterion, the quartile method, and cross-validation to obtain the processed acoustic signal, including:

[0097] For each wing valve:

[0098] Using the K-means clustering algorithm, the first effective signal of the wing valve is classified to obtain the first type of energy parameters and the second type of energy parameters of the wing valve, wherein the energy value of each energy parameter in the first type of energy parameters is greater than the energy parameter with the largest energy value in the second type of energy parameters;

[0099] All energy parameters in the first category of energy parameters of the wing valve are sorted in ascending order of energy value to form a box plot. The upper limit value is determined in the box plot as the second energy threshold of the wing valve.

[0100] Based on the first valid signal of the wing valve, the third energy threshold of the wing valve is determined using the Laida criterion;

[0101] The energy parameters with energy values ​​greater than the second and third energy thresholds in the first category of energy parameters of the wing valve are removed, and the remaining energy parameters are used as the first acoustic characteristic parameters.

[0102] The original acoustic signal corresponding to the second type of energy parameter of the wing valve is divided into K subsets. Based on K-fold cross-validation, an autoencoder is used for training and testing. The subsets with reconstruction errors greater than a set threshold after testing are deleted to obtain the remaining energy feature parameters, which are used as the second acoustic feature parameters.

[0103] The first acoustic characteristic parameter and the second acoustic characteristic parameter are used as the processed acoustic signal of the wing valve.

[0104] Specifically, for each wing valve, due to the slight differences between wing valves, even for wing valves of the same level, the second energy threshold and the third energy threshold corresponding to each wing valve are different. Therefore, it is necessary to calculate the second energy threshold and the third energy threshold corresponding to the gas for each wing valve.

[0105] Taking a specific wing valve as an example: First, for the 50 first valid signals of this wing valve, the K-means clustering algorithm is used, setting K=2 groups, to automatically cluster the signals into two parts: high energy and low energy. The high energy part corresponds to the first type of energy parameter, and the low energy part corresponds to the second type of energy parameter. Since abnormal signals are generated by sudden events such as impacts and crack propagation, and have large energy values, abnormal signal detection is only performed on the high-energy clusters formed by k-means clustering. For the high-energy clusters formed by clustering, the quantiles of the high-energy cluster data are used to identify outliers. All energy values ​​are arranged from smallest to largest and divided into four equal parts (rounded up if less than the minimum), and a box plot is drawn, as shown below. Figure 3As shown, the upper beard value is determined in the box plot as the second energy threshold. When the energy value in a cluster is greater than the upper beard value in the box plot, such data points can be considered as suspected outliers. Simultaneously, to avoid errors caused by the randomness of the K-means clustering algorithm, the Raida criterion in statistics is used to verify the outlier signals. Based on the first valid signal, the Raida criterion is used to determine the third energy threshold. Energy parameters in the first class of energy parameters whose energy values ​​are all greater than the second and third energy thresholds are identified as outliers and removed. The remaining energy parameters in the first class of energy parameters are used as the first acoustic feature parameters. Furthermore, the original acoustic signal corresponding to the second class of energy parameters of the wing valve is divided into K subsets. Based on K-fold cross-validation, an autoencoder is used for training and testing. Subsets with reconstruction errors greater than a set threshold after testing are deleted, resulting in the remaining energy feature parameters, which are used as the second acoustic feature parameters. Finally, the first and second acoustic feature parameters are used as the processed acoustic signal of the wing valve. To ensure that the number of valid signals in each channel is the same, the number of signals m that are removed needs to be recorded, and the m signals with the highest energy values ​​in the background noise are selected to supplement the valid signals.

[0106] Furthermore, all energy parameters in the first category of energy parameters of the wing valve are sorted in ascending order of energy value to form a box plot. The upper threshold value in the box plot is determined as the second energy threshold of the wing valve, including:

[0107] The energy values ​​of the energy parameters located at the 25th percentile of the box plot are taken as the upper quartile, and the energy values ​​of the energy parameters located at the 75th percentile of the box plot are taken as the lower quartile.

[0108] The upper limit value is calculated using the following formula and used as the second energy threshold of the wing valve:

[0109] E2 = Q3 + 1.5(Q3 - Q1)

[0110] Q3 is the upper quartile; Q1 is the lower quartile.

[0111] Furthermore, based on the first effective signal of the wing valve, the third energy threshold of the wing valve is determined using the Raida criterion, including:

[0112] Obtain the mean energy and standard deviation of all energy parameters in the first valid signal of the wing valve;

[0113] The third energy threshold of the wing valve is calculated using the following formula:

[0114] E3=μ+3σ

[0115] Where E3 is the third energy threshold; μ is the mean of all energy parameters in the first valid signal; and σ is the standard deviation of all energy parameters in the first valid signal.

[0116] Furthermore, based on the processed acoustic signal, the flow rate percentage of each wing valve during this operating period is determined, including:

[0117] The flow rate percentage of each wing valve in each stage of the wing valve can be calculated using the following formula:

[0118]

[0119] Among them, Q ij E represents the flow rate percentage of the j-th wing valve in the i-th stage. ij Let n be the sum of the energies of the j-th wing valve in the i-th stage, and n be the total number of wing valves in the i-th stage.

[0120] Because the operating status of the wing valves cannot be observed during production, and it is impossible to correlate effective signals with the opening and closing status of the wing valves in real time, and considering that the flow rate of the wing valves is related to the activity of the wing valve cover, energy parameters are used to describe the activity level of each wing valve's opening and closing over a period of time. However, there is no necessary connection between wing valves at different stages. Therefore, in this embodiment, based on the processed acoustic signal, the flow rate percentage of each wing valve in the same stage is determined during the operating time. By adding the energy values ​​of the effective signals of each wing valve in the same stage, the ratio of the energy value of each wing valve in that stage to the sum of the energy values ​​of all wing valves in that stage is obtained. This ratio is used as the flow rate percentage of each wing valve in the same stage, reflecting the activity level of each wing valve in the same stage under the current state, thereby obtaining the operating status during that operating time.

[0121] In another implementation, if the acoustic signal generating component is the same as the working principle of the wing valve, the above-mentioned scheme can also be used for fault analysis, identification and diagnosis, so as to obtain the operating status of the component with the same working principle as the wing valve.

[0122] Example 2

[0123] like Figure 4 As shown, the present invention provides an acoustic monitoring device for the operating status of a catalytic cracking unit. The catalytic cracking unit includes a multi-stage wing valve, each stage of which includes multiple wing valves. The device includes:

[0124] The data acquisition module 10 is used to acquire the acoustic signal of the wing valve during a certain operating time.

[0125] Data processing module 20 is used to preprocess the acoustic signal to obtain the processed acoustic signal;

[0126] The flow percentage determination module 30 is used to determine the flow percentage of each wing valve during the operating time based on the processed acoustic signal.

[0127] The operating status determination module 40 is used to determine the operating status of the wing valve during a certain operating time based on the flow ratio of the wing valve. The operating status of the wing valve during this operating time is used to characterize the operating status of the catalytic cracking unit.

[0128] Furthermore, the data processing module 20 is specifically used for:

[0129] For each wing valve:

[0130] The energy parameters in the acoustic signal whose energy value is greater than the first energy threshold are determined as the first valid signals;

[0131] The first effective signal is corrected using clustering algorithms, the Laida criterion, the quartile method, and cross-validation to obtain the processed acoustic signal.

[0132] Furthermore, for each wing valve:

[0133] Using the K-means clustering algorithm, the first effective signal of the wing valve is classified to obtain the first type of energy parameters and the second type of energy parameters of the wing valve, wherein the energy value of each energy parameter in the first type of energy parameters is greater than the energy parameter with the largest energy value in the second type of energy parameters;

[0134] All energy parameters in the first category of energy parameters of the wing valve are sorted in ascending order of energy value to form a box plot. The upper limit value is determined in the box plot as the second energy threshold of the wing valve.

[0135] Based on the first valid signal of the wing valve, the third energy threshold of the wing valve is determined using the Laida criterion;

[0136] The energy parameters with energy values ​​greater than the second and third energy thresholds in the first category of energy parameters of the wing valve are removed, and the remaining energy parameters are used as the first acoustic characteristic parameters.

[0137] The original acoustic signal corresponding to the second type of energy parameter of the wing valve is divided into K subsets. Based on K-fold cross-validation, an autoencoder is used for training and testing. The subsets with reconstruction errors greater than a set threshold after testing are deleted to obtain the remaining energy feature parameters, which are used as the second acoustic feature parameters.

[0138] The first acoustic characteristic parameter and the second acoustic characteristic parameter are used as the processed acoustic signal of the wing valve.

[0139] Example 3

[0140] The present invention provides a machine-readable storage medium storing instructions for causing a machine to execute the acoustic monitoring method for the operating status of a catalytic cracking unit described above.

[0141] Example 4

[0142] The present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the above-described acoustic monitoring method for the operating status of a catalytic cracking unit.

[0143] Example 5

[0144] In this embodiment 5, taking a group of same-level wing valves (a total of six wing valves) as an example, for the first wing valve, the threshold value of the appropriate sampling parameters is determined to be 49dB, the number of sampling points is 4096, the peak positioning time PDT is 200, the end definition time HDT is 3800μs, the system lock-up time is 300μs, and the maximum duration is 1000ms.

[0145] Referring to the test angle and equilibrium catalyst amount in the quality inspection report of the settling tank wing valve, a mechanical analysis of the wing valve yields a theoretical opening and closing cycle of 115s. During this time, the wing valve undergoes approximately 1928 ÷ 115 ≈ 17 theoretical opening and closing cycles. Experiments show that each opening and closing of the wing valve cover generates an average of 2-3 acoustic emission signals. Therefore, the number of effective signals reflecting the opening and closing state of the wing valve is defined as (2-3) × 17 ≈ 50. Taking the first 50 signals as effective signals, the first energy threshold is thus determined to be 1.3697 × 10⁻⁶. -4 Background noise signals with energy below this first energy threshold are filtered out. Under this parameter set, the signal-to-noise ratio is maximized while acquiring all wing valve signals, and the waveform most favorable for analysis is obtained. This channel continuously acquired data for 1928 seconds, collecting a total of 28324 acoustic emission signals.

[0146] Data cleaning was performed on the valid signals, and anomalous acoustic emission signals were removed using statistical and clustering methods. First, for the 50 valid signals, the K-means clustering algorithm was used, with K=2, to automatically cluster the signals into two parts: high energy (first type of energy parameter) and low energy (second type of energy parameter).

[0147] For the first type of energy parameters, all energy parameters in the first type of energy parameters are sorted from smallest to largest to form a box plot. The upper limit value is determined in the box plot as the second energy threshold. The Raida criterion in statistics is used to check the abnormal signals. Based on the first effective signal, the Raida criterion is used to determine the third energy threshold. The third energy threshold is obtained, and the signal with the largest energy value in the background noise is selected to supplement the effective signal.

[0148] The final effective signal energy upper and lower threshold values ​​for the No. 1 wing valve were 2.5493 ​​× 10⁻⁶. -4 and 1.3697×10 -4 and will be located at 2.5493×10 -4 and 1.3697×10 -4 The energy parameters between them are selected as effective processed acoustic signals and used for subsequent energy ratio calculations, thereby obtaining the activity of the wing valve and the flow rate of the material flowing through the same stage wing valve during that period.

[0149] Similarly, for the remaining five wing valves, the effective signal was determined using the above method, and the processed acoustic signals corresponding to the remaining wing valves 2-6 were obtained, as shown below. Figure 5 The diagram shows the energy distribution of energy parameters for six different wing valves. The energy values ​​of the effective signals from each of the six wing valves are summed, and the total energy value is normalized to calculate the flow rate percentage reflecting the wing valve activity level under the current condition. A pie chart of the flow rate percentage is then plotted. Figure 6 As shown, it can be clearly concluded that the flow rate share of wing valve No. 6 is 5.68%, which is significantly different from the other five wing valves. The flow rate share of the other five wing valves is as follows: wing valve No. 1: 21.88%, wing valve No. 2: 16.54%, wing valve No. 3: 14.9%, wing valve No. 4: 21.63%, and wing valve No. 5: 19.38%. Therefore, although the contribution of wing valve No. 6 is low, the flow rate share of the other five wing valves is relatively even, and the overall risk of agent leakage of the unit is low.

[0150] The optional embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details in the above embodiments. Within the scope of the technical concept of the embodiments of the present invention, various simple modifications can be made to the technical solutions of the embodiments of the present invention, and these simple modifications all fall within the protection scope of the embodiments of the present invention.

[0151] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the embodiments of the present invention will not describe the various possible combinations separately.

[0152] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a microcontroller, chip, or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0153] Furthermore, various different implementations of the present invention can be combined arbitrarily, as long as they do not violate the spirit of the present invention, they should also be regarded as the content disclosed in the present invention.

Claims

1. A method of acoustically monitoring the operating state of a catalytic cracking unit, said catalytic cracking unit comprising a plurality of stages of wing valves, each stage of wing valves comprising a plurality of wing valves, characterised in that, The method includes: Acquire the acoustic signal of the wing valve during a certain operating time; The acoustic signal is preprocessed to obtain the processed acoustic signal; Based on the processed acoustic signal, the flow rate percentage of each wing valve during this operating period is determined. Based on the flow rate ratio of the wing valve, the operating status of the wing valve during this operating period is determined, and the operating status of the wing valve during this operating period is used to characterize the operating status of the catalytic cracking unit.

2. The method according to claim 1, characterized in that, The acoustic signal includes the energy parameters of the wing valve during a certain operating time.

3. The method according to claim 2, characterized in that, The acoustic signal is preprocessed to obtain a processed acoustic signal, including: For each wing valve: Acoustic signal parameters with energy values ​​greater than a first energy threshold are defined as the first valid signals; The first effective signal is corrected using clustering algorithms, the Laida criterion, the quartile method, and cross-validation to obtain the processed acoustic signal.

4. The method according to claim 3, characterized in that, Using clustering algorithms, the Laida criterion, quartile method, and cross-validation, the first effective signal is corrected to obtain the processed acoustic signal, including: For each wing valve: Using the K-means clustering algorithm, the first effective signal of the wing valve is classified to obtain the first type of energy parameters and the second type of energy parameters of the wing valve, wherein the energy value of each energy parameter in the first type of energy parameters is greater than the energy parameter with the largest energy value in the second type of energy parameters; All energy parameters in the first category of energy parameters of the wing valve are sorted in ascending order of energy value to form a box plot. The upper limit value is determined in the box plot as the second energy threshold of the wing valve. Based on the first valid signal of the wing valve, the third energy threshold of the wing valve is determined using the Laida criterion; The energy parameters with energy values ​​greater than the second and third energy thresholds in the first category of energy parameters of the wing valve are removed, and the remaining energy parameters are used as the first acoustic characteristic parameters. The original acoustic signal corresponding to the second type of energy parameter of the wing valve is divided into K subsets. Based on K-fold cross-validation, an autoencoder is used for training and testing. The subsets with reconstruction errors greater than a set threshold after testing are deleted to obtain the remaining energy feature parameters, which are used as the second acoustic feature parameters. The first acoustic characteristic parameter and the second acoustic characteristic parameter are used as the processed acoustic signal of the wing valve.

5. The method according to claim 4, characterized in that, All energy parameters in the first category of energy parameters of the wing valve are sorted in ascending order of energy value to form a box plot. The upper threshold value in the box plot is determined as the second energy threshold of the wing valve, including: The energy values ​​of the energy parameters located at the 25th percentile of the box plot are taken as the upper quartile, and the energy values ​​of the energy parameters located at the 75th percentile of the box plot are taken as the lower quartile. The upper limit value is calculated using the following formula and used as the second energy threshold of the wing valve: E2 = Q3 + 1.5(Q3 - Q1) Q3 is the upper quartile; Q1 is the lower quartile.

6. The method according to claim 4, characterized in that, Based on the first valid signal of the wing valve, the third energy threshold of the wing valve is determined using the Raida criterion, including: Obtain the mean energy and standard deviation of all energy parameters in the first valid signal of the wing valve; The third energy threshold of the wing valve is calculated using the following formula: E3=μ+3σ Where E3 is the third energy threshold; μ is the mean energy of all energy parameters in the first valid signal; and σ is the standard deviation of the energy of all energy parameters in the first valid signal.

7. The method according to claim 1, characterized in that, Based on the processed acoustic signal, the flow rate percentage of each wing valve during this operating period is determined, including: The flow rate percentage of each wing valve in each stage of the wing valve can be calculated using the following formula: wherein Q ij is the flow rate proportion of the jth wing valve in the ith stage, E ij is the sum of the energy values of the processed acoustic signals of the jth wing valve in the ith stage, and n is the number of wing valves in the ith stage.

8. An acoustic monitoring device for the operating status of a catalytic cracking unit, the catalytic cracking unit comprising a multi-stage wing valve, each stage comprising multiple wing valves, characterized in that, The device includes: The data acquisition module is used to acquire the acoustic signals of the wing valve during a certain operating time. The data processing module is used to preprocess the acoustic signal to obtain the processed acoustic signal; The flow percentage determination module is used to determine the flow percentage of each wing valve during the operating time based on the processed acoustic signal. The operating status determination module is used to determine the operating status of the wing valve within a certain operating time based on the flow ratio of the wing valve. The operating status of the wing valve within this operating time is used to characterize the operating status of the catalytic cracking unit.

9. The apparatus according to claim 8, characterized in that, The data processing module is specifically used for: For each wing valve: The parameters in the acoustic signal whose energy value is greater than the first energy threshold are determined as the first valid signal; The first effective signal is corrected using clustering algorithms, the Laida criterion, the quartile method, and cross-validation to obtain the processed acoustic signal.

10. The apparatus according to claim 9, characterized in that, Using clustering algorithms, the Laida criterion, quartile method, and cross-validation, the first effective signal is corrected to obtain the processed acoustic signal, including: For each wing valve: Using the K-means clustering algorithm, the first effective signal of the wing valve is classified to obtain the first type of energy parameters and the second type of energy parameters of the wing valve, wherein the energy value of each energy parameter in the first type of energy parameters is greater than the energy parameter with the largest energy value in the second type of energy parameters; All energy parameters in the first category of energy parameters of the wing valve are sorted in ascending order of energy value to form a box plot. The upper limit value is determined in the box plot as the second energy threshold of the wing valve. Based on the first valid signal of the wing valve, the third energy threshold of the wing valve is determined using the Laida criterion; The energy parameters with energy values ​​greater than the second and third energy thresholds in the first category of energy parameters of the wing valve are removed, and the remaining energy parameters are used as the first acoustic characteristic parameters. The original acoustic signal corresponding to the second type of energy parameter of the wing valve is divided into K subsets. Based on K-fold cross-validation, an autoencoder is used for training and testing. The subsets with reconstruction errors greater than a set threshold after testing are deleted to obtain the remaining energy feature parameters, which are used as the second acoustic feature parameters. The first acoustic characteristic parameter and the second acoustic characteristic parameter are used as the processed acoustic signal of the wing valve.

11. A machine-readable storage medium storing instructions for causing a machine to perform the acoustic monitoring method for the operating status of a catalytic cracking unit as described in any one of claims 1-7.

12. A terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the acoustic monitoring method for the operating status of the catalytic cracking unit as described in any one of claims 1-7.

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