Ventilation quantity intelligent control method and device for sludge drying system

Abstract

The present application relates to the technical field of sludge treatment, and particularly relates to a ventilation volume intelligent control method and device for a sludge drying system. The method comprises the following steps: collecting multi-source monitoring data in a sludge drying process; analyzing state deviation of sludge in an upper layer drying process of a cyclone separator, and calculating model mismatch degree caused by sludge properties; analyzing dynamic coupling characteristics of ventilation volume in the sludge drying process according to changes of the sludge in the cyclone separator, and generating a ventilation volume compensation coefficient; dynamically correcting a control model for controlling ventilation volume of a lower layer of the cyclone separator based on the ventilation volume compensation coefficient, and generating a control instruction; and adjusting actual ventilation volume of the sludge drying system according to the control instruction. The present application solves the problem of sludge drying quality decline in the prior art, and significantly reduces sludge drying energy consumption.

Description

Technical Field

[0001] This invention relates to the field of sludge treatment technology, and more specifically to a method and apparatus for intelligent control of ventilation volume in sludge drying systems. Background Technology

[0002] Sludge drying is a crucial process that removes moisture and reduces water content through heating and ventilation, thereby reducing sludge volume and facilitating subsequent disposal. The ventilation system, by adjusting airflow to promote moisture evaporation, has a decisive impact on drying efficiency and energy consumption. Traditional drying systems often use fixed airflow or experience-based settings, failing to adjust in real-time according to dynamic changes in sludge moisture content, thickness, and ambient temperature and humidity. This frequently leads to problems such as excessive energy consumption, uneven drying, or prolonged processing time.

[0003] Although dynamic ventilation control methods based on real-time monitoring data have been introduced, in practical applications, the viscosity, composition and initial moisture content of sludge can fluctuate significantly due to changes in source, treatment process or time. This directly affects the heat and mass transfer efficiency and the dynamic response characteristics of the system, causing the predicted output to deviate from the actual operating conditions, resulting in inaccurate ventilation volume regulation, increased energy consumption and decreased drying quality. Summary of the Invention

[0004] To address the technical problem of declining sludge drying quality, the present invention aims to provide an intelligent ventilation control method and device for sludge drying systems. The specific technical solution adopted is as follows:

[0005] In a first aspect, embodiments of the present invention provide an intelligent control method for ventilation volume in a sludge drying system, applied to a sludge drying system including a cyclone separator, the method comprising the following steps:

[0006] Collect multi-source monitoring data during the sludge drying process;

[0007] Based on multi-source monitoring data, the state deviation of sludge during the drying process in the upper layer of the cyclone separator was analyzed, and the model mismatch caused by sludge properties was calculated.

[0008] Based on the changes in sludge in the cyclone separator, the dynamic coupling characteristics of ventilation volume in the sludge drying process are analyzed, and the ventilation volume compensation coefficient is generated by combining the model mismatch.

[0009] Based on the ventilation volume compensation coefficient, the control model used to control the ventilation volume of the lower layer of the cyclone separator is dynamically modified, and control commands are generated.

[0010] Adjust the actual ventilation volume of the sludge drying system according to the control commands.

[0011] In some embodiments, the multi-source monitoring data includes ventilation volume data and sludge status data.

[0012] In some embodiments, the collection of multi-source monitoring data during the sludge drying process includes:

[0013] Air volume data were collected from the air inlet duct, air outlet, and each branch air duct.

[0014] Sludge status data were collected at each layer of the cyclone separator and at the sludge outlet.

[0015] All collected data are filtered, and / or denoised, and / or time-synchronized, and / or normalized.

[0016] In some embodiments, analyzing the state deviation of sludge during the drying process in the upper layer of the cyclone separator based on multi-source monitoring data includes:

[0017] Obtain the actual drying rate of the upper layer of the cyclone separator;

[0018] The predicted drying rate of the upper layer of the cyclone separator is obtained through a preset prediction model.

[0019] Compare the actual drying rate with the predicted drying rate to determine the drying rate error;

[0020] Analyze the trend of drying rate error over time;

[0021] The sludge drying deviation rate of the upper drying process of the cyclone separator is calculated based on the drying rate error and its changing trend.

[0022] In some embodiments, calculating the model mismatch caused by sludge properties includes:

[0023] Determine the sludge drying deviation rate of each monitoring data point in the multi-source monitoring data;

[0024] The correlation between the sludge drying deviation rate in the upper drying process of the cyclone separator and the corresponding changes in the sludge drying deviation rate of each monitoring data was calculated.

[0025] Obtain the actual values ​​of each monitoring data point, and obtain the predicted values ​​of each monitoring data point through a preset prediction model;

[0026] Compare the actual values ​​with the predicted values ​​of each monitoring data point to determine the cumulative deviation;

[0027] The model mismatch caused by sludge properties is calculated based on the corresponding change correlation and the cumulative deviation.

[0028] In some embodiments, analyzing the dynamic coupling characteristics of ventilation volume in the sludge drying process based on the changes in sludge within the cyclone separator includes:

[0029] The correlation between ventilation volume and sludge drying rate in multi-source monitoring data was analyzed to determine the degree of influence of ventilation volume on sludge drying rate.

[0030] By comparing the effect of current ventilation volume on sludge drying rate with the effect of historical ventilation volume on sludge drying rate, the deviation of ventilation volume on sludge drying rate can be determined.

[0031] Based on the action bias and model mismatch, the response intensity of ventilation volume to sludge property disturbance is calculated.

[0032] In some embodiments, generating the ventilation volume compensation coefficient by integrating the model mismatch includes:

[0033] Determine the intensity of the response of ventilation volume to disturbance of sludge properties;

[0034] The response strength of ventilation volume to sludge property disturbances is combined with the model mismatch caused by sludge properties;

[0035] The ventilation volume compensation coefficient is calculated based on the response intensity and model mismatch.

[0036] In some embodiments, the step of dynamically modifying the control model for controlling the lower-level ventilation of the cyclone separator based on the ventilation volume compensation coefficient and generating control commands includes:

[0037] Input the ventilation volume compensation coefficient of the current control cycle into the preset control model used to control the ventilation volume of the lower layer of the cyclone separator;

[0038] Adjust the target ventilation volume setpoint for the next control cycle based on the ventilation volume compensation coefficient;

[0039] Control commands are generated based on the adjusted target ventilation volume setpoint;

[0040] The generated control commands are sent to the blower frequency converter, and / or damper actuator, and / or air valve actuator of the sludge drying system.

[0041] In some embodiments, adjusting the target ventilation volume setpoint for the next control cycle based on the ventilation volume compensation coefficient further includes:

[0042] Using the sum of constant 1 and ventilation volume compensation coefficient as weights, the ventilation volume of the sludge drying system in the lower layer of the cyclone separator under the current control cycle is weighted to obtain the target ventilation volume setpoint for the next control cycle.

[0043] Secondly, embodiments of the present invention provide an intelligent ventilation control device for a sludge drying system. The device includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the method described in the above technical solution.

[0044] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores executable code, and when the processor executes the executable code, it implements the various possible implementations of the first aspect.

[0045] Fourthly, embodiments of the present invention provide a computer program product comprising: computer program code, which, when executed on a computer, causes the computer to perform the method described in the first aspect or any possible implementation thereof.

[0046] Fifthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the various possible implementations of the first aspect.

[0047] The embodiments of the present invention have at least the following beneficial effects:

[0048] This technical solution analyzes the deviation in the drying state of sludge in the upper layer of the cyclone separator in real time using multi-source monitoring data, and calculates the model mismatch caused by fluctuations in sludge properties. Then, by analyzing the dynamic coupling characteristics of ventilation volume and the drying process, a precise ventilation volume compensation coefficient is generated. This coefficient is used to dynamically correct the lower-level ventilation control model, enabling the ventilation volume setpoint to adapt to changes in sludge properties. Finally, closed-loop feedback continuously optimizes control parameters, forming an adaptive adjustment mechanism. This method effectively overcomes the model mismatch problem caused by changes in sludge properties in traditional control, significantly improves the accuracy and adaptability of ventilation control, and ultimately achieves synergistic optimization of improved drying quality and reduced energy consumption. Attached Figure Description

[0049] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a flowchart of a method for intelligent control of ventilation volume in a sludge drying system according to an embodiment of the present invention.

[0051] Figure 2 This is a schematic diagram of the structure of a computer device provided in one embodiment of the present invention;

[0052] Figure 3 A schematic diagram of a sludge drying system;

[0053] Figure 4 This is a schematic diagram illustrating the change in sludge drying rate.

[0054] Reference numerals in the attached drawings: 1-Plate and frame filter press; 2-Storage bin; 3-Star feeder; 4-Motor; 5-Crusher; 6-Conveyor; 9-Cyclone separator; 7-Heat pump; 8-High pressure blower; 11-Collector; 10-Heat exchanger; 12-Induced draft fan; 13-Water tank; 14-Scrubbing tower; 15-Water pump. Detailed Implementation

[0055] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an intelligent ventilation control method and device for a sludge drying system proposed according to the present invention.

[0056] In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments may be combined in any suitable form.

[0057] In the description of the embodiments of the present invention, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of the present invention, "multiple" means two or more.

[0058] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0060] Please see Figure 3 , Figure 3 This is a schematic diagram of a sludge drying system.

[0061] Specifically, sludge enters the storage tank 2 from the plate and frame filter press 1, passes through the star feeder 3, and is fed into the crusher 5 by the motor 4. After crushing, it enters the conveyor 6 and is transported to the two-stage cyclone separator 9. At the same time, the heat pump 7 heats the air and sends it into the cyclone separator 9 through the high-pressure blower 8. The hot material generated after the two-stage cyclone separation enters the collector 11. Above the collector 11 is the heat exchanger 10. The induced draft fan 12 at the top of the cyclone separator 9 draws the gas into the scrubbing tower 14. The gas is then de-dusted, deodorized, and dehumidified by the liquid in the water tank 13 through the water pump 15. The gas is then drawn back into the heat exchanger 10 by the induced draft fan 12, and the hot air then enters the heat pump 7 for circulation, thus achieving sludge drying.

[0062] The embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art will recognize that, with technological advancements and the emergence of new scenarios, the technical solutions provided by the embodiments of the present invention are also applicable to similar technical problems.

[0063] The following description, in conjunction with the accompanying drawings, details the specific scheme of the intelligent ventilation control method and device for sludge drying systems provided by this invention.

[0064] Example 1:

[0065] Please see Figure 1 The diagram illustrates a flowchart of a method for intelligent control of ventilation volume in a sludge drying system according to an embodiment of the present invention. The method includes the following steps:

[0066] S10. Collect multi-source monitoring data during the sludge drying process.

[0067] Specifically, the multi-source monitoring data includes ventilation volume data and sludge state data. The system can collect real-time airflow, static pressure, and dynamic pressure data for each section of the system using thermal mass flow meters and pressure sensors installed in the inlet duct, outlet, and branch ducts to form ventilation volume data, reflecting the gas transport capacity and resistance changes of the sludge drying system. Simultaneously, near-infrared sensors can collect real-time sludge moisture content at each layer of the cyclone separator and at the sludge outlet; temperature sensors monitor the surface and internal temperature of the sludge; and indirect sensors obtain particle size, density, and viscosity information to form sludge state data. It should be noted that the ventilation volume data and sludge state data to be collected, as well as the specific collection methods, are not unique and can be selected according to actual needs.

[0068] Furthermore, all collected data is transmitted to the control platform via an industrial bus. Based on the actual situation, one or more preprocessing methods are selected, such as Kalman filtering for noise reduction, time synchronization processing with timestamp alignment, and maximum / minimum value normalization, to preprocess all collected data to form a complete dynamic dataset, providing a data foundation for subsequent analysis.

[0069] It should be noted that the multi-source monitoring data used in subsequent steps are all pre-processed data.

[0070] S11. Based on multi-source monitoring data, analyze the state deviation of sludge during the drying process in the upper layer of the cyclone separator, and calculate the model mismatch caused by sludge properties.

[0071] The upper layer of the cyclone separator is a crucial area where sludge first comes into contact with hot air. Its drying state directly reflects the sludge properties and system response characteristics. As the initial reaction zone, the sludge in the upper layer of the cyclone separator is unprocessed, directly exposing the original physical properties of the current batch of sludge. Furthermore, the heat and mass exchange is most intense here, and the system responds extremely quickly to changes in parameters such as airflow and sludge properties. This means that any deviation in the drying state can sensitively reflect the degree of matching between the system's dynamic characteristics and the sludge properties. Therefore, analyzing the state deviations during the drying process in the upper layer of the cyclone separator can identify sludge property disturbances and thus quantify the model mismatch.

[0072] Since the physical properties of sludge, as well as airflow and temperature conditions, change continuously during the sludge drying process, the predicted values ​​obtained through the preset prediction model cannot fully reflect the actual drying rate. Therefore, it is necessary to analyze the deviation between the actual and predicted sludge drying rate at each moment to provide real-time feedback and a basis for dynamic compensation.

[0073] First, the drying rate error at each moment within each control cycle is obtained using the following formula. :

[0074]

[0075] in, Indicates the first Within the first control cycle, the first The drying rate error at each moment; Indicates the first Within the first control cycle, the first The actual drying rate at each moment Indicates the first Within the first control cycle, the first The predicted drying rate at each time point is the theoretical deduction result of the drying process based on fixed parameters using a preset prediction model. The preset prediction model can be a Model Predictive Control (MPC) model, or it can be adjusted according to actual needs.

[0076] Among them, the Within the first control cycle, the first Actual drying rate at time 1 Calculated using the following formula:

[0077]

[0078] in, This represents the sludge moisture content at time j. Indicates the sampling interval time. Indicates time The moisture content of the sludge.

[0079] Please see Figure 4 , Figure 4 This is a schematic diagram showing the change in sludge drying rate; the solid line represents the actual drying rate, and the dashed line represents the predicted drying rate.

[0080] Furthermore, within a single control cycle of the upper layer of the cyclone separator, the error between the actual and predicted sludge drying rate increases over time; that is, when the drying rate error shows an upward trend over time, it indicates that the actual evaporation efficiency of the drying process is consistently higher than the predicted value within that control cycle, possibly due to the physical properties of the sludge. Therefore, it is necessary to quantify the degree to which the actual drying rate deviates from the predicted drying rate throughout the entire control cycle. Specifically, firstly, the trend of the drying rate error over time can be analyzed. The least squares method can be used to fit a straight line to the drying rate error within each control cycle to obtain the slope value of the fitted line. The slope value represents the trend of the drying rate error within the i-th control cycle. If the slope is positive, it indicates that the error is increasing and the impact of sludge property disturbance on the drying process is intensifying.

[0081] Furthermore, based on the drying rate error and its changing trend, the sludge drying deviation rate of the upper drying process of the cyclone separator is calculated. Specifically, the sludge drying deviation rate for each control cycle is obtained using the following formula. :

[0082]

[0083] in, Indicates the first Sludge drying deviation rate for each control cycle; This indicates the number of data points within each control cycle; Indicates the first Within the first control cycle, the first The drying rate error at each moment; This indicates the trend of the drying rate error during the i-th control cycle.

[0084] During the drying process of different batches of sludge in the cyclone separator, due to differences in physical properties, the actual values ​​of the monitoring data may deviate from the predicted values. When the change of a certain monitoring data is highly correlated with the deviation of the sludge drying rate, it indicates that the abnormality of the monitoring data has a significant impact on the drying process. Its difference from the reference standard has caused a large interference with the model prediction accuracy, thus leading to an increase in model mismatch.

[0085] Furthermore, the model mismatch caused by the sludge properties in each control cycle is obtained using the following formula. :

[0086]

[0087] in, Indicates the first Model mismatch caused by sludge properties in each control cycle; This indicates the types and quantities of sludge-related monitoring data; Indicates the first The drying rate error sequence at all times within each control cycle; Indicates the first Sludge drying deviation rate for each control cycle; Indicates the first Within the first control cycle, the first A sequence of data values ​​from various monitoring data; Indicates the first Within the first control cycle, the first The cumulative deviation between the actual and predicted values ​​of various monitoring data; Indicates the first The sludge drying rate error within the first control cycle and the first The correlation between corresponding changes in monitoring data is specifically calculated using the Pearson correlation coefficient function. This function quantifies the degree of linear correlation between two variables. The calculation formula is the product of covariance and standard deviation, returning a correlation coefficient with a range of [-1, 1]. The larger the absolute value, the more significant the synchronous change trend between the sludge drying deviation rate and the monitoring data deviation rate. The larger the value, the closer the correlation between the sludge drying rate and this type of monitoring data. Multi-source monitoring data includes temperature, pressure, viscosity, etc.

[0088] S12. Based on the changes in sludge in the cyclone separator, analyze the dynamic coupling characteristics of ventilation volume in the sludge drying process, and generate ventilation volume compensation coefficient by combining the model mismatch.

[0089] Ventilation volume directly affects air velocity, heat exchange efficiency, and evaporation intensity, while sludge drying rate is influenced by differences in sludge properties; therefore, a dynamic coupling relationship exists between the two. Thus, by analyzing the corresponding changes in ventilation volume and sludge drying rate within each control cycle, the extent to which ventilation volume adjustment affects the drying rate can be clearly identified.

[0090] First, the correlation between ventilation volume and sludge drying rate in multi-source monitoring data was analyzed, and the effect of ventilation volume on sludge drying rate in each control period was determined by the following formula. :

[0091]

[0092] in, Indicates the first The extent to which ventilation volume affects the sludge drying rate within a control cycle; Indicates the first Ventilation volume sequence for each control cycle; Indicates the first A sequence of sludge drying rates for each control cycle; Indicates the first Correlation between changes in ventilation volume and sludge drying rate within a control cycle.

[0093] Furthermore, when the properties of the sludge differ from previous batches, the effect of ventilation volume on sludge drying also changes. A significant difference in the effect of ventilation volume on the sludge drying rate compared to previous batches indicates a greater deviation in the effect of ventilation volume on sludge drying due to sludge properties. Therefore, it is necessary to compare the effect of the current ventilation volume on the sludge drying rate with the historical effect of the ventilation volume, and determine the deviation in the effect of ventilation volume on sludge drying using the following formula. :

[0094]

[0095] in, Indicates the first Deviation in the effect of ventilation volume on sludge drying within a control cycle; Indicates the first The extent to which ventilation volume affects the sludge drying rate within a control cycle; This represents the average historical ventilation volume as a measure of the effect on sludge drying rate.

[0096] Furthermore, during the sludge drying process, the drying effect is not only affected by differences in the physical properties of the sludge, but may also be interfered with by environmental conditions, inlet air temperature and humidity, and airflow disturbances. By comparing the actual deviation of the ventilation volume on the drying rate with the model mismatch caused by sludge properties in each control cycle, the portion of the ventilation volume response truly caused by sludge property disturbances can be separated, thus obtaining the response intensity of the ventilation volume to sludge physical property disturbances in each control cycle. Specifically, based on the deviation and model mismatch, the response intensity of the ventilation volume to sludge property disturbances in each control cycle is calculated using the following formula. :

[0097]

[0098] in, Indicates the first The response intensity of ventilation volume to sludge property disturbance within a control cycle; Indicates the first Deviation in the effect of ventilation volume on sludge drying within a control cycle; Indicates the first Model mismatch caused by sludge properties in each control cycle.

[0099] Furthermore, the response intensity of ventilation volume to drying rate quantifies the direct impact of airflow changes on the drying process, while model mismatch reflects the magnitude of prediction bias caused by changes in sludge physical properties. By combining the response intensity with the model mismatch, the proportion that adjusted ventilation volume should bear in compensating for disturbances in sludge physical properties under the current sludge batch operating conditions is measured, thereby obtaining a reasonable ventilation volume compensation coefficient. Specifically, the response intensity of ventilation volume to disturbances in sludge properties is combined with the model mismatch caused by sludge properties, and the ventilation volume compensation coefficient is calculated using the following formula. :

[0100]

[0101] in, Indicates the first Ventilation volume compensation coefficient for each control cycle; Indicates the first The response intensity of ventilation volume to sludge property disturbance within a control cycle; Indicates the first Model mismatch caused by sludge properties in each control cycle.

[0102] S13. Based on the ventilation volume compensation coefficient, dynamically modify the control model used to control the ventilation volume of the lower layer of the cyclone separator, and generate control commands.

[0103] Fluctuations in the physical properties of sludge continuously alter the heat and mass transfer characteristics and dynamic response patterns of the sludge drying system, causing pre-set predictive models to fail to accurately reflect real-time operating conditions. Traditional model predictive control typically relies on static or empirical models to describe system behavior. However, under actual operating conditions, factors such as batch differences in sludge feed, changes in ambient temperature, and airflow disturbances can all cause system parameter drift, gradually deviating the predicted results from the actual process, leading to accumulated control errors and performance degradation. Therefore, it is necessary to introduce a ventilation volume compensation coefficient and correct the predictive model in real time. This allows the control algorithm to adaptively adjust the system's dynamic characteristics based on monitoring data in each control cycle, making the model predictions more closely reflect the current physical properties of the sludge and operating conditions.

[0104] Specifically, the ventilation volume compensation coefficient is dynamically obtained through the information on the sludge drying process in the upper layer of the cyclone separator, and then fed back into the lower layer drying control model of the cyclone separator for ventilation volume correction.

[0105] First, the ventilation volume compensation coefficient for the current control cycle is input into the preset control model used to control the ventilation volume in the lower layer of the cyclone separator. Based on the ventilation volume compensation coefficient, the target ventilation volume setpoint for the next control cycle is adjusted. Specifically, the ventilation volume of the sludge drying system in the lower layer of the cyclone separator within the next control cycle is calculated using the following formula. :

[0106]

[0107] in, Indicates the first The ventilation volume of the sludge drying system in the lower layer of the cyclone separator during each control cycle; Indicates the first The ventilation volume of the sludge drying system in the lower layer of the cyclone separator during each control cycle; Indicates the first Ventilation volume compensation coefficient for each control cycle.

[0108] Furthermore, control commands are generated based on the adjusted target ventilation volume setpoint. These commands can include fan speed adjustment commands, damper opening adjustment commands, and duct allocation ratio adjustment commands, each corresponding to a different actuator in the sludge drying system. Finally, the generated control commands are sent to the fan frequency converters, damper actuators, and door actuators of the sludge drying system, providing operational guidance for actual ventilation volume adjustments.

[0109] S14. Adjust the actual ventilation volume of the sludge drying system according to the control command.

[0110] After receiving control commands, the actuator of the sludge drying system adjusts the fan speed, air valve opening, or air duct distribution ratio according to the command requirements, thereby adjusting the actual ventilation volume of the sludge drying system and making the ventilation volume of the lower layer of the cyclone separator reach the corrected target value.

[0111] It should be noted that the device provided in the above embodiments is only an example of the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above.

[0112] Figure 2 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention. For example, as shown... Figure 2 As shown, the computer device 20 includes: a memory 21, a processor 22, and a computer program 23 stored in the memory 21 and running on the processor 22. When the processor 22 executes the computer program 23, the computer device can execute any of the aforementioned intelligent ventilation control methods and devices for sludge drying systems.

[0113] Furthermore, embodiments of the present invention also protect an apparatus that may include a memory and a processor, wherein the memory stores executable program code, and the processor is used to call and execute the executable program code to execute the intelligent ventilation control method and apparatus for sludge drying systems provided in embodiments of the present invention.

[0114] In this embodiment of the invention, the device can be divided into functional modules according to the above method example. For example, each module can correspond to a separate function, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware. It should be noted that the module division in this embodiment is illustrative and is only a logical functional division. In actual implementation, there may be other division methods.

[0115] It should be understood that the device provided in the embodiments of the present invention is used to execute the above-described intelligent ventilation control method and device for sludge drying systems, and therefore can achieve the same effect as the above-described implementation method.

[0116] When using integrated units, the device may include a processing module and a storage module. When applied to a device, the processing module can be used to control and manage the device's operations. The storage module can be used to support the device in executing program code, etc. The processing module may be a processor or a controller, which can implement or execute various exemplary logic blocks, modules, and circuits as described in this disclosure. The processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of Digital Signal Processing (DSP) and a microprocessor, etc., and the storage module may be a memory.

[0117] In addition, the device provided in the embodiments of the present invention may specifically be a chip, component or module. The chip may include a connected processor and a memory. The memory is used to store instructions. When the processor calls and executes the instructions, the chip can execute the intelligent ventilation control method and device for sludge drying system provided in the above embodiments.

[0118] This invention also provides a computer-readable storage medium storing computer program code. When the computer program code is run on a computer, the computer executes the aforementioned method steps to implement the intelligent ventilation control method and device for sludge drying systems provided in the above embodiments.

[0119] This invention also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned steps to realize the intelligent ventilation control method and device for sludge drying systems provided in the above embodiments.

[0120] In this invention, the apparatus, computer-readable storage medium, computer program product, or chip provided in the embodiments are all used to execute the corresponding methods described above. Therefore, the beneficial effects they achieve can be referred to the beneficial effects in the corresponding methods described above, and will not be repeated here. Through the above description of the embodiments, those skilled in the art will understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In the embodiments provided by this invention, it should be understood that the disclosed apparatus and method can be implemented in other ways.

[0121] The device embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0122] It should also be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0123] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0124] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0125] The above content is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the protection scope of the present invention.

Claims

1. An intelligent control method for ventilation rate of a sludge drying system, characterized in that, The method is applied to a sludge drying system comprising a cyclone separator, and comprises the following steps: Collecting multi-source monitoring data in a sludge drying process; Based on the multi-source monitoring data, analyzing the state deviation of the sludge in the upper layer of the cyclone separator during the drying process, and calculating the model mismatch degree caused by the properties of the sludge; According to the change of the sludge in the cyclone separator, the dynamic coupling characteristics of the ventilation volume in the sludge drying process are analyzed, and the ventilation volume compensation coefficient is generated based on the model mismatch degree; Based on the ventilation volume compensation coefficient, the control model for controlling the ventilation volume of the lower layer of the cyclone separator is dynamically corrected, and a control instruction is generated; According to the control instruction, the actual ventilation volume of the sludge drying system is adjusted; Wherein, the method for obtaining the model mismatch degree caused by the properties of the sludge comprises: obtaining the actual drying rate of the upper layer of the cyclone separator; obtaining the predicted drying rate of the upper layer of the cyclone separator through a preset prediction model; comparing the actual drying rate with the predicted drying rate to determine the drying rate error; analyzing the change trend of the drying rate error over time; based on the drying rate error and the change trend, the sludge drying deviation rate of the upper layer of the cyclone separator is calculated; The calculation formula of the model mismatch degree caused by the properties of the sludge is: wherein, represents the model mismatch degree caused by the sludge properties in the th control cycle; represents the number of kinds of sludge-related monitoring data; represents the drying rate error sequence at all times in the th control cycle; represents the sludge drying deviation rate in the th control cycle; represents the data value sequence of the th kind of monitoring data in the th control cycle; represents the cumulative deviation of the actual and predicted values of the th kind of monitoring data in the th control cycle; represents the corresponding change correlation of the sludge drying rate error and the th kind of monitoring data in the th control cycle.

2. The intelligent control method of ventilation rate for sludge dewatering system as claimed in claim 1 wherein, The multi-source monitoring data includes ventilation volume data and sludge state data.

3. The intelligent control method of ventilation rate for sludge dewatering system as claimed in claim 1 wherein, The multi-source monitoring data in the sludge drying process is collected, including: Collecting ventilation volume data in the air inlet pipe, air outlet and each branch air duct; Collecting sludge state data in each layer of the cyclone separator and the sludge outlet; Filtering, and / or denoising, and / or time synchronizing, and / or normalizing all collected data.

4. The intelligent control method of ventilation rate for sludge dewatering system as claimed in claim 1 wherein, According to the change of the sludge in the cyclone separator, the dynamic coupling characteristics of the ventilation volume in the sludge drying process are analyzed, including: Analyzing the corresponding change correlation between the ventilation volume and the sludge drying rate in the multi-source monitoring data to determine the degree of influence of the ventilation volume on the sludge drying rate; Comparing the difference between the degree of influence of the current ventilation volume on the sludge drying rate and the degree of influence of the historical ventilation volume on the sludge drying rate to determine the deviation of the influence of the ventilation volume on the sludge drying; Based on the deviation and the model mismatch degree, the response strength of the ventilation volume to the disturbance of the properties of the sludge is calculated.

5. The intelligent control method of ventilation rate for sludge dewatering system as claimed in claim 4 wherein, The model mismatch degree caused by the properties of the sludge is combined to generate the ventilation volume compensation coefficient, including: Determine the response strength of the ventilation volume to the disturbance of the properties of the sludge; Combine the response strength of the ventilation volume to the disturbance of the properties of the sludge with the model mismatch degree caused by the properties of the sludge to calculate the ventilation volume compensation coefficient based on the response strength and the model mismatch degree.

6. The intelligent control method of ventilation rate for sludge dewatering system as claimed in claim 1 wherein, Based on the ventilation volume compensation coefficient, the control model for controlling the ventilation volume of the lower layer of the cyclone separator is dynamically corrected, and a control instruction is generated, including: Input the ventilation volume compensation coefficient of the current control period into the preset control model for controlling the ventilation volume of the lower layer of the cyclone separator; Adjust the target ventilation volume set value of the next control period according to the ventilation volume compensation coefficient; Generate a control instruction based on the adjusted target ventilation volume set value; Send the generated control instruction to the fan frequency converter, and / or air valve actuator, and / or air door actuator of the sludge drying system.

7. The intelligent control method of ventilation rate for sludge dewatering system as claimed in claim 6 wherein, The adjusting the target ventilation quantity set value of the next control period according to the ventilation quantity compensation coefficient further comprises: The target ventilation quantity set value of the next control period is obtained by weighting the ventilation quantity of the lower layer of the cyclone separator in the sludge drying system in the current control period with the sum of the constant 1 and the ventilation quantity compensation coefficient as the weight.

8. A ventilation amount intelligent control device for a sludge drying system, characterized by, The device comprises a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the steps of the method according to any one of claims 1-7 when running the computer program.

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