A method and system for monitoring a pipeline online dynamic mixer

Abstract

The present application relates to the technical field of water treatment, and discloses a pipeline online dynamic mixer monitoring method and system, which comprises the following steps: calculating the shearing rate of the paddle of the pipeline online dynamic mixer according to the main shaft rotation frequency, and then calculating the apparent viscosity of the fluid, and calculating the fluid mixing intensity according to the apparent viscosity of the fluid; calculating the residence time of the fluid in the pipeline online dynamic mixer according to the inlet flow and the outlet flow, and calculating the mixing process uniformity index through a mixing process uniformity index calculation model according to the residence time and the fluid mixing intensity; and determining the regulation and control parameters of the pipeline online dynamic mixer according to the obtained fluid mixing intensity and the mixing process uniformity index. The present application realizes quantitative regulation and control, optimization and real-time monitoring and diagnosis of the flocculation mixing process by monitoring the pipeline flow, the driving motor torque and the main shaft rotation frequency and other conventional parameters, has a fast calculation speed, and is suitable for online application.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water treatment, in particular to a pipeline online dynamic mixer monitoring method and system. BACKGROUND

[0002] The statements in this section merely provide background information related to the present application and do not necessarily constitute the prior art.

[0003] In a water treatment system, sludge treatment is an important link to ensure the overall operation efficiency and effluent quality, especially in municipal wastewater treatment plants and industrial wastewater treatment systems, sludge dewatering and reduction directly affect the subsequent discharge and disposal costs. In order to improve the efficiency of sludge treatment, it is often necessary to carry out flocculation pretreatment before sludge dewatering, by adding flocculant to make suspended particles aggregate into flocs, so as to improve the dewatering performance of sludge. In practical application, it has become a common way to use pipeline mixing technology to fully mix the flocculant and sludge in a flowing state, especially in combination with the use of pipeline online dynamic mixer, which can improve the flocculation effect and enhance the automation and continuity of the treatment system.

[0004] However, there are still many problems in the mixing monitoring and control of pipeline sludge treatment at present:

[0005] 1) The traditional method usually relies on manual experience to set operation parameters, lacks real-time monitoring and dynamic adjustment of the flocculation process, and is easy to cause improper addition of flocculant, uneven mixing, fluctuation of treatment effect and other phenomena.

[0006] 2) Some technical solutions adjust the flow direction and backflow of the solution by setting multiple solution parameter sensors to measure the results, to ensure the uniformity of solution mixing; multiple sensors are installed on the inner wall of the mixer, due to the strong corrosiveness and adhesion of sludge medium, the sensors are easy to be affected by chemical corrosion and pollution deposition, resulting in distorted or failed readings. At the same time, the flow field characteristics of the inner wall area and the center area of the mixer are quite different, so the solution sensors installed on the inner wall cannot correctly reflect the solution mixing state.

[0007] 3) The logic of existing solutions to judge the mixing effect is relatively single, it is difficult to find out the running problems such as mixer blockage, paddle scaling, uneven distribution of flocculation groups, etc., which affects the stability of the system and the safe operation of the equipment. SUMMARY

[0008] The present application proposes a pipeline online dynamic mixer monitoring method and system to solve the above problems, fully considers the influence of fluid particle settling velocity, apparent viscosity and residence time on flocculation mixing process, defines fluid mixing intensity and mixing process uniformity index for the pipeline online dynamic mixer, realizes quantitative regulation and control, optimization of flocculation mixing process and real-time monitoring and diagnosis of equipment state by monitoring pipeline flow, driving motor torque and main shaft rotation frequency, etc., has fast calculation speed and is suitable for online application.

[0009] In order to achieve the above object, the present application adopts the following technical scheme:

[0010] One or more embodiments provide a pipeline online dynamic mixer monitoring method, comprising the following steps:

[0011] The inlet flow, outlet flow, driving motor output torque and main shaft rotation frequency of the pipeline online dynamic mixer are acquired;

[0012] The shear rate of the paddle of the pipeline online dynamic mixer is calculated according to the main shaft rotation frequency, and then the apparent viscosity of the fluid is calculated, and the fluid mixing intensity is calculated according to the apparent viscosity of the fluid;

[0013] A fluid mixing process uniformity index calculation model is constructed by taking the driving motor output torque and the main shaft rotation frequency as increasing functions and the flow as a decreasing function;

[0014] The residence time of the fluid in the pipeline online dynamic mixer is calculated according to the inlet flow and the outlet flow, and the mixing process uniformity index is calculated through the fluid mixing process uniformity index calculation model according to the residence time and the fluid mixing intensity;

[0015] The obtained fluid mixing intensity and mixing process uniformity index are used to determine the regulation and control parameters of the pipeline online dynamic mixer to control the operation of the online dynamic mixer.

[0016] One or more embodiments provide a pipeline online dynamic mixer monitoring system, comprising:

[0017] The acquisition module is configured to acquire the inlet flow, outlet flow, driving motor output torque and main shaft rotation frequency of the pipeline online dynamic mixer;

[0018] The mixing intensity calculation module is configured to calculate the shear rate of the paddle of the pipeline online dynamic mixer according to the main shaft rotation frequency, and then calculate the apparent viscosity of the fluid, and calculate the fluid mixing intensity according to the apparent viscosity of the fluid;

[0019] The construction module is configured to construct a fluid mixing process uniformity index calculation model by taking the driving motor output torque and the main shaft rotation frequency as increasing functions and the flow as a decreasing function;

[0020] The mixing process uniformity index calculation module is configured to calculate the residence time of the fluid in the pipeline online dynamic mixer according to the inlet flow and the outlet flow, and calculate the mixing process uniformity index through a fluid mixing process uniformity index calculation model according to the residence time and the fluid mixing intensity;

[0021] The regulation module is configured to determine the regulation parameter of the pipeline online dynamic mixer according to the obtained fluid mixing intensity and the mixing process uniformity index, so as to control the operation of the online dynamic mixer.

[0022] Compared with the prior art, the method has the following beneficial effects:

[0023] The method can realize real-time monitoring and regulation of the internal mixing process of the pipeline online dynamic mixer, and improves the intelligent level of flocculation mixing. The method introduces fluid shear rate and apparent viscosity parameters, comprehensively considers factors such as particle settling velocity and residence time, and effectively overcomes the error problem caused by traditional experience-based parameter setting. At the same time, the proposed mixing intensity and uniformity index two core indexes can reflect the mixing efficiency and flocculation state, realize the quantitative evaluation and optimization adjustment of the mixing state. The method uses a non-invasive measurement method, which only relies on the measurement of conventional external parameters, avoids the corrosion, pollution and measurement distortion problems caused by placing the sensor in the pipe, and improves the reliability and maintainability of the system. The scheme can also timely discover operation abnormalities caused by equipment blockage, paddle scaling or uneven distribution of flocculation groups, and enhance the safety and stability of the system.

[0024] The advantages of the present application and the advantages of the additional aspects will be described in detail in the following specific embodiments. BRIEF DESCRIPTION OF DRAWINGS

[0025] The drawings accompanying the specification of this application form a part thereof, serve to further provide a further understanding of the application, and together with the description of the exemplary embodiments of the application and the explanation thereof serve to explain the application, and do not constitute a limitation thereof.

[0026] Figure 1 is a flow chart of the pipeline online dynamic mixer monitoring method of embodiment 1 of the present application;

[0027] Figure 2 is a control logic block diagram of the pipeline online dynamic mixer monitoring method of embodiment 1 of the present application;

[0028] Figure 3 is a block diagram of the pipeline online dynamic mixer monitoring system of embodiment 2 of the present application;

[0029] Figure 4 is a structural diagram of the pipeline online dynamic mixer of embodiment 1 of the present application;

[0030] Figure 5 is an example driving motor output torque spectrum diagram of embodiment 1 of the present application; wherein (a) is a driving motor output torque spectrum diagram when the driving motor main shaft paddle is fouled or corroded; (b) is a driving motor output torque spectrum diagram when the flocculation group of the pipeline online dynamic mixer is unevenly distributed;

[0031] wherein 1 is a pipeline online dynamic mixer; 2 is a driving motor main shaft; 3 is a paddle; and 4 is a pipeline. DETAILED DESCRIPTION

[0032] The present application will be further described below in conjunction with the accompanying drawings and embodiments.

[0033] It should be noted that the following detailed description is exemplary in nature and is intended to provide further description of the present application. 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 application belongs.

[0034] It should be noted that the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, the singular form is intended to include the plural form unless the context clearly indicates otherwise, and it should be further understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of the features, steps, operations, devices, components and / or combinations thereof. It should be noted that the various embodiments and features in the present application can be combined with each other without conflict, and the embodiments will be described in detail below in conjunction with the accompanying drawings.

[0035] Embodiment 1

[0036] In the technical solutions disclosed in one or more embodiments, as shown in Figures 1 to 3 A pipeline online dynamic mixer monitoring method comprises the following steps:

[0037] Step 1, obtaining the inlet flow rate , outlet flow rate , driving motor output torque and main shaft rotation frequency of the pipeline online dynamic mixer 1;

[0038] Step 2, calculating the shear rate of the paddle 3 of the pipeline online dynamic mixer 1 according to the main shaft rotation frequency , and then calculating the apparent viscosity of the fluid, and calculating the fluid mixing intensity G according to the apparent viscosity of the fluid;

[0039] Step 3, calculating the driving motor output torque and the spindle rotation frequency is an increasing function, and is a decreasing function with respect to the flow rate, and a fluid mixing process uniformity index calculation model is constructed;

[0040] Step 4, according to the inlet flow rate and the outlet flow rate , the residence time of the fluid in the pipeline online dynamic mixer 1 is calculated , according to the residence time and the fluid mixing intensity G, the mixing process uniformity index MI is calculated through the fluid mixing process uniformity index calculation model;

[0041] Step 5, according to the obtained fluid mixing intensity G and the mixing process uniformity index MI, the control parameters of the pipeline online dynamic mixer 1 are determined to control the operation of the online dynamic mixer;

[0042] The embodiment is based on the monitoring and calculation of multiple parameters of the online mixing process, and establishes an evaluation system for reflecting the efficiency of flocculation mixing process. First, the flow rate sensors arranged at the inlet and outlet measure the inlet and outlet flow rates, and simultaneously collect the output torque of the driving motor and the rotation frequency of the spindle and other operating parameters to obtain the real-time operating state of the mixer. Then, the shear rate generated by the paddle 3 is calculated according to the rotation frequency of the spindle, the apparent viscosity under the current condition is calculated in combination with the shear rate and the fluid rheological property, and the mixing intensity G is further calculated based on the apparent viscosity and the shear rate. Further, the uniformity index model is constructed, which sets that the torque and the rotation frequency have a positive effect on the uniformity, while the flow rate has a negative correlation, thereby establishing an index model reflecting the uniformity of mixing. On this basis, in combination with the residence time and the mixing intensity G, the mixing process uniformity MI value is derived by using the index model. Finally, through the feedback of the G value and the MI value, the operating parameters of the mixer are automatically adjusted to realize the dynamic optimization control of the mixing process.

[0043] The method of the embodiment can realize real-time monitoring and control of the mixing process inside the pipeline online dynamic mixer 1, and improve the intelligent level of flocculation mixing. The method introduces the fluid shear rate and apparent viscosity parameters, comprehensively considers factors such as particle settling velocity and residence time, and effectively overcomes the error problem caused by the traditional dependence on experience setting parameters. At the same time, the proposed mixing intensity G and uniformity index MI two core indexes can reflect the mixing efficiency and flocculation state, realize the quantitative evaluation and optimization adjustment of the mixing state. In addition, the method uses a non-invasive measurement method, which only relies on the measurement of conventional external parameters, avoids the corrosion, pollution and measurement distortion problems caused by the sensor placed in the pipe, and improves the reliability and maintainability of the system. The scheme can also timely discover the operation abnormalities caused by equipment blockage, paddle 3 scaling or uneven distribution of flocculation groups, and enhance the safety and stability of the system.

[0044] In step 1, the inlet flow rate Q1 and the outlet flow rate Q2 of the pipeline online dynamic mixer 1 are collected by flow meters arranged at the inlet and outlet ends respectively, and the output torque T and the spindle rotation frequency f of the driving motor of the pipeline online dynamic mixer 1 are parameters of the device operation, which can be regulated and controlled.

[0045] In step 2, the shear rate of the paddle 3 of the pipeline online dynamic mixer 1 is calculated as the product of the shear coefficient and the spindle rotation frequency f, and the calculation formula is as follows:

[0046] ;

[0047] wherein, γ represents the shear coefficient, and is a set value, which is preferably set to 0.1 in the embodiment. ;

[0048] Further, the apparent viscosity of the fluid is calculated according to the shear rate γ and the fluid yield stress σ, and the calculation formula is as follows:

[0049] ;

[0050] wherein, σ represents the fluid yield stress, n is the flow index, and K is the coefficient.

[0051] Optionally, in the embodiment, γ = 0.1, n = 1, and K = 1. ;

[0052] The embodiment further refines the calculation method of the shear rate and the apparent viscosity. Specifically, the linear model is adopted to obtain the shear rate, that is, a shear coefficient related to the structure of the mixer and the geometric parameters of the paddle 3 is set, and the shear rate is obtained by multiplying the shear coefficient and the spindle rotation frequency. This method considers the influence of the mixer design on the shear field, and simplifies the derivation process of the shear rate. Then, the apparent viscosity of the fluid is calculated in combination with the measured or preset fluid yield stress. This method makes the calculation of the apparent viscosity more consistent with the actual flow state, which helps to improve the accuracy of the calculation of the mixing intensity G, and further improves the overall monitoring accuracy.

[0053] Further, the fluid mixing intensity G is calculated according to the fluid apparent viscosity η, and the calculation formula is as follows:

[0054] ;

[0055] ​​​​​​​​​Wherein, D represents the diameter of the pipeline 4, L represents the length of the fluid passing through the pipeline in the online dynamic mixer 1; T represents the output torque of the driving motor; represents the spindle rotation frequency;

[0056] The embodiment establishes a mixing intensity calculation model derived based on the fluid dynamics characteristics, and quantitatively evaluates the mixing capacity of the online dynamic mixer 1 through the combination of structural parameters and operating parameters. By introducing the structured mixing intensity formula, the evaluation of mixing efficiency is more quantitative and accurate, facilitating systematic management and automatic control. By including the output torque of the driving motor and the spindle frequency in the calculation, dynamic feedback of actual energy consumption and mechanical input is realized, improving the monitoring sensitivity; and the introduction of apparent viscosity fully considers the influence of fluid rheological properties on mixing intensity, making the calculation result more close to the actual operating state. This formula does not need to rely on complex three-dimensional simulation or flow field sensing devices, and can be applied to online mixing systems of different types and scales, improving the practicality and universality of the scheme.

[0057] The residence time of the fluid in the online dynamic mixer 1 in step 4 The calculation formula is:

[0058] ;

[0059] ;

[0060] Wherein, is the flow rate, calculated by the average value of the flow rate through the inlet and outlet;

[0061] In step 3, the driving motor output torque and the spindle rotation frequency are increasing functions, and the flow rate is a decreasing function, and the fluid mixing process uniformity index calculation model is constructed as follows:

[0062] ;

[0063] Wherein, G is the fluid mixing intensity, is an empirical parameter, is the residence time of the fluid in the online dynamic mixer 1;

[0064] Optionally, ;

[0065] In step 5, according to the obtained fluid mixing intensity G and the mixing process uniformity index MI, the control parameters of the online dynamic mixer 1 are determined to control the operation of the online dynamic mixer;

[0066] The control parameters of the pipeline online dynamic mixer 1 include the inlet flow rate of the pipeline online dynamic mixer 1 , the outlet flow rate , the output torque of the driving motor , and the rotation frequency of the main shaft .

[0067] Further, according to the obtained fluid mixing intensity G and the mixing process uniformity index MI, the control parameters of the pipeline online dynamic mixer 1 are determined, including the following steps:

[0068] Step 51, control the pipeline flow rate so that the inlet flow rate v of the pipeline online dynamic mixer 1 , the linear velocity v of the paddle 3 of the pipeline online dynamic mixer 1 is greater than the settling velocity of the particles in the fluid, that is, , as condition one is met;

[0069] Specifically, the pipeline flow rate is adjusted so that the inlet flow rate v of the pipeline online dynamic mixer 1 satisfies:

[0070] ;

[0071] Wherein μ represents the dynamic viscosity of the fluid, ρ represents the density of the fluid, D represents the diameter of the pipeline 4, and Re represents the Reynolds number of the fluid. In this embodiment, Re = 3000.

[0072] Specifically, the settling velocity of the particles in the fluid is , and the calculation formula is:

[0073] ;

[0074] The linear velocity v of the paddle 3 of the pipeline online dynamic mixer 1 is , and the calculation formula is:

[0075] ;

[0076] Wherein g represents the acceleration of gravity, d represents the diameter of the particles, represents the density of the particles, r represents the radius of the paddle 3 of the pipeline online dynamic mixer 1, and C d represents the drag coefficient, which can be set as C d = 0.44.

[0077] Step 52, perform fast Fourier transform on the output torque T of the driving motor to obtain a frequency spectrum, and according to the spectral line energy of the paddle passing frequency in the frequency spectrum, determine the range A of the driving motor output torque and the rotation frequency of the main shaft that make the floc distribution uniform;

[0078] The blade passes through the frequency, that is, the characteristic interference frequency generated due to the rotation of the plurality of blades 3 driven by the rotation of the main shaft, is the main shaft rotation frequency times the number of blades , that is ;

[0079] Specifically, the fast Fourier transform is performed on the driving motor output torque T to obtain a frequency spectrum, if the spectral line energy at the frequency of in the frequency spectrum is the largest, it indicates that the flocculation group is unevenly distributed, at this time, the driving motor output torque and the main shaft rotation frequency need to be reduced, so as to reduce the flocculant concentration and make the flocculation group evenly distributed;

[0080] Step 53, under the condition one and in the numerical range A calculated, set the range of fluid mixing intensity G and mixing process uniformity index MI, under the operating constraints of the pipeline online dynamic mixer 1, the inlet flow rate , the outlet flow rate , the driving motor output torque and the main shaft rotation frequency are calculated by using the NSGA-II multi-objective optimization algorithm;

[0081] Specifically, the operating constraints of the pipeline online dynamic mixer 1 include:

[0082] 1) The driving motor output torque does not exceed the rated torque of the driving motor;

[0083] 2) The driving motor output power P does not exceed the rated power of the driving motor;

[0084] MI=1 represents complete mixing, if MI=0, it represents no mixing, and the larger the value of MI represents the better the mixing effect; assuming that the size parameters of the pipeline online dynamic mixer 1 and the pipeline 4 are unchanged, the fluid mixing intensity G is an increasing function of the driving motor output torque and the main shaft rotation frequency , the larger the value of the fluid mixing intensity G, the more intense the mixing process, typically, 50s -1 ≤G≤100s -1 represents slow mixing, 300s -1 ≤G≤600s -1 represents fast mixing.

[0085] Further, the range of fluid mixing intensity G and mixing process uniformity index MI is set to: 100s -1 <G<300s -1 , and MI≥0.9;

[0086] Specifically, assuming that the pipeline online dynamic mixer 1 and the pipeline size parameters are unchanged, by increasing the driving motor output torque and the main shaft rotation frequency , reducing the flow Q flowing through, satisfying condition two: 100s -1 <G<300s -1 , MI≥0.9, in order to obtain the expected inlet flow , outlet flow , driving motor output torque and the main shaft rotation frequency ;

[0087] A multi-objective optimization process is performed, and the objective function constructed is as follows:

[0088] ;

[0089] , ;

[0090] , ;

[0091] , ;

[0092] The NSGA-II multi-objective optimization algorithm is used for optimization and solution, the population size popSize=100, the maximum iteration number maxGen=500, the number of decision variables numVar=4, the crossover probability pc=0.9, the mutation probability pm=0.25, the SBX crossover distribution index is 20, and the polynomial mutation distribution index is 20;

[0093] The above embodiment organically integrates particle dynamics, mechanical vibration analysis and mixing efficiency evaluation, realizes high-precision modeling and dynamic optimization of the online mixer control strategy. Through quantitative constraint of the particle settling velocity, the problems of insufficient flocculation and pipeline deposition are effectively prevented; the frequency spectrum identification method ensures that the selected operating frequency and torque interval have physical basis, and improves the mixing uniformity; by taking G and MI as double objective optimization parameters, and inversely deducing the system operating parameters under reasonable constraints, the intelligent and adaptive control ability of the system is significantly improved. Overall, this method not only ensures the mixing efficiency, but also guarantees the system operation safety and energy consumption control, and is suitable for industrial online deployment and closed-loop control.

[0094] Steps 1 to 5 above are the control process performed under the condition that the online dynamic mixer 1 is fault-free. Furthermore, before step 2, it also includes judging whether the online dynamic mixer 1 is faulty. If the online dynamic mixer 1 is faulty, the fault is eliminated first and then control is performed. The fault judgment and elimination methods include the following:

[0095] Step 101: Calculate the flow rate change rate. If the flow rate change rate is greater than the set flow rate change threshold, and the output torque T of the drive motor increases at a rate exceeding the set change rate threshold, it is determined that the pipeline online dynamic mixer 1 is blocked. Control the pipeline online dynamic mixer 1 to stop and clear the pipeline 4.

[0096] The formula for calculating the rate of change in flow rate is as follows:

[0097] ;

[0098] Optionally, the flow rate change threshold can be set to no less than 0.05;

[0099] Among them, if the rate of change of the output torque T of the drive motor exceeds the set rate of change threshold, the output torque T of the drive motor is determined to be a sharp increase.

[0100] Step 102: Perform a fast Fourier transform on the output torque T of the drive motor to obtain the spectrum. If the frequency in the spectrum is the main shaft rotation frequency... If the spectral energy is the largest, it is determined that the blade 3 connected to the main shaft 2 of the drive motor has scale or corrosion damage, and the machine is stopped to repair the blade 3.

[0101] This embodiment provides a mixer fault intelligent identification method based on real-time operating data, which can effectively detect and locate typical faults such as blockage, scaling, or mechanical damage. Compared with traditional methods relying on manual experience for inspection, this solution has higher real-time performance, accuracy, and automation. By setting a dual threshold mechanism for flow rate and torque, the reliability of blockage identification is improved, avoiding false alarms or missed alarms. The introduction of frequency domain analysis allows for in-depth analysis of the motor's operating status, enabling early identification of mechanical anomalies and reducing the risk of equipment damage and maintenance costs. This monitoring mechanism can be linked with the control system to achieve closed-loop fault handling, ensuring stable and continuous system operation.

[0102] To illustrate the effectiveness of the method in this embodiment, actual deployment and experimental verification were conducted.

[0103] like Figure 4 The pipeline online dynamic mixer 1 shown has two blades 3, and the drive motor spindle 2 rotates at a frequency of 40Hz. After running for a period of time, a fast Fourier transform is performed on the output torque of the drive motor to obtain the following result: Figure 5the spectrum diagram shown in (a) and (b) in FIG. 1, as Figure 5 As shown in (a) in FIG. 1, the spectral line with the maximum energy on the output torque spectrum diagram of the driving motor corresponds to the rotation frequency of the driving motor main shaft 2, indicating that the paddle 3 connected to the driving motor main shaft 2 has a fouling or corrosion phenomenon, as Figure 5 As shown in (b) in FIG. 1, the spectral line with the maximum energy on the output torque spectrum diagram of the driving motor corresponds to the passing frequency of the paddle 3 connected to the driving motor main shaft 2, indicating that the flocculation clusters in the pipeline online dynamic mixer are unevenly distributed.

[0104] Through experiments, compared with existing monitoring methods, the method of the embodiment can prolong the service life of the sensor by more than 1 year, reduce the failure downtime and maintenance time of the pipeline online dynamic mixer by more than 70%, improve the sludge treatment efficiency by more than 20%, increase the solid content of the sludge by more than 5%, reduce the filter cake by more than 5%, and save energy by more than 10%.

[0105] Embodiment 2

[0106] Based on embodiment 1, a pipeline online dynamic mixer monitoring method is provided in the embodiment, comprising:

[0107] The acquisition module is configured to acquire the inlet flow, outlet flow, driving motor output torque, and main shaft rotation frequency of the pipeline online dynamic mixer;

[0108] The mixing intensity calculation module is configured to calculate the shear rate of the paddle of the pipeline online dynamic mixer according to the main shaft rotation frequency, and then calculate the apparent viscosity of the fluid, and calculate the fluid mixing intensity according to the apparent viscosity of the fluid;

[0109] The construction module is configured to construct a fluid mixing process uniformity index calculation model with respect to the driving motor output torque and the main shaft rotation frequency being an increasing function, and with respect to the flow passing through being a decreasing function;

[0110] The mixing process uniformity index calculation module is configured to calculate the residence time of the fluid in the pipeline online dynamic mixer according to the inlet flow and the outlet flow, and calculate the mixing process uniformity index through the fluid mixing process uniformity index calculation model according to the residence time and the fluid mixing intensity;

[0111] The regulation and control module is configured to determine the regulation and control parameters of the pipeline online dynamic mixer according to the obtained fluid mixing intensity and mixing process uniformity index, so as to control the operation of the online dynamic mixer.

[0112] Further, the fluid mixing intensity G is calculated according to the apparent viscosity The calculation formula is:

[0113] ;

[0114] Wherein, D represents the pipe diameter, L represents the length of the fluid through the pipe in the online dynamic mixer; T represents the output torque of the driving motor of the pipe online dynamic mixer, represents the spindle rotation frequency of the driving motor of the pipe online dynamic mixer.

[0115] Further, the fluid mixing process uniformity index calculation model is constructed as follows:

[0116] ;

[0117] Wherein, represents the residence time of the fluid in the pipe online dynamic mixer; G is the fluid mixing intensity; is an empirical parameter.

[0118] It should be noted that the various modules in the embodiment correspond one by one to the various steps in Embodiment 1, and the specific implementation process is the same, which will not be repeated here.

[0119] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0120] The above describes the specific embodiments of the present application in conjunction with the accompanying drawings, but is not a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications or changes made on the basis of the technical solutions of the present application without creative labor are still within the protection scope of the present application.

Claims

1. A method of monitoring an in-line dynamic mixer of a pipeline, the method comprising: The method comprises the following steps: acquiring the inlet flow, outlet flow, driving motor output torque and main shaft rotation frequency of the pipeline online dynamic mixer; calculating the shear rate of the pipeline online dynamic mixer blade according to the main shaft rotation frequency, and then calculating the fluid apparent viscosity, and calculating the fluid mixing intensity according to the fluid apparent viscosity; constructing a fluid mixing process uniformity index calculation model with respect to the driving motor output torque and the main shaft rotation frequency being increasing functions, and with respect to the flow being a decreasing function; calculating the residence time of the fluid in the pipeline online dynamic mixer according to the inlet flow and the outlet flow, and calculating the mixing process uniformity index through the fluid mixing process uniformity index calculation model according to the residence time and the fluid mixing intensity; determining the regulation and control parameters of the pipeline online dynamic mixer according to the obtained fluid mixing intensity and the mixing process uniformity index, so as to control the operation of the pipeline online dynamic mixer.

2. A method of monitoring an inline dynamic mixer of a pipe as claimed in claim 1, characterized in that: The shear rate of the pipeline online dynamic mixer blade is the product of the shear coefficient and the main shaft rotation frequency f; The fluid apparent viscosity is calculated according to the shear rate and the fluid yield stress.

3. The pipeline online dynamic mixer monitoring method of claim 1, wherein: According to the apparent viscosity of the fluid The fluid mixing intensity G is calculated according to the following formula: ； Wherein, D represents the pipe diameter, L represents the length of the fluid passing through the pipe online dynamic mixer; T represents the output torque of the driving motor of the pipe online dynamic mixer, represents the main shaft rotation frequency of the driving motor of the pipe online dynamic mixer.

4. The pipeline online dynamic mixer monitoring method of claim 1, wherein: The constructed fluid mixing process uniformity index calculation model is as follows: ； wherein, G is the fluid mixing intensity; and is an empirical parameter.

5. The pipeline online dynamic mixer monitoring method of claim 1, wherein: The regulation and control parameters of the pipeline online dynamic mixer include the inlet flow, outlet flow, driving motor output torque and main shaft rotation frequency of the pipeline online dynamic mixer.

6. The pipeline online dynamic mixer monitoring method of claim 1, wherein: determining the regulation and control parameters of the pipeline online dynamic mixer according to the obtained fluid mixing intensity G and the mixing process uniformity index MI, comprises the following steps: Controlling the pipe flow such that the pipe on-line dynamic mixer inlet flow rate is greater than the particle settling velocity in the fluid as condition one performing fast Fourier transform on the driving motor output torque to obtain a frequency spectrum diagram, and judging the range A of the driving motor output torque and the main shaft rotation frequency that makes the floc distribution uniform according to the spectral line energy of the blade passing frequency fp in the frequency spectrum diagram; under the condition that the first condition is met and within the calculated numerical range A, setting the range of the fluid mixing intensity G and the mixing process uniformity index MI, and calculating the inlet flow, outlet flow, driving motor output torque and main shaft rotation frequency under the operation constraints of the pipeline online dynamic mixer.

7. The pipeline online dynamic mixer monitoring method of claim 1, wherein: further comprising judging whether the pipeline online dynamic mixer has a fault, and when the pipeline online dynamic mixer has a fault, first removing the fault and then performing regulation and control, and the fault judgment and removal method comprises the following steps: calculating the flow rate of change, if the flow rate of change is greater than the set flow rate of change threshold, and the driving motor output torque T increases at a rate of change that exceeds the set rate of change threshold, it is determined that the pipeline online dynamic mixer is blocked, the pipeline online dynamic mixer is controlled to stop and dredge the pipeline; Performing a Fast Fourier Transform on the output torque T of the drive motor yields a frequency spectrum. If the frequencies in the frequency spectrum are the main shaft rotation frequency... If the spectral line has the highest energy, it is determined that there is scaling or corrosion damage on the propeller blades of the drive motor, and the machine should be stopped to repair the propeller blades.

8. An in-line dynamic mixer monitoring system for a pipeline, characterized by, comprises: an acquisition module configured to acquire the inlet flow, outlet flow, driving motor output torque and main shaft rotation frequency of the pipeline online dynamic mixer; The mixing intensity calculation module is configured to calculate a shear rate of a blade of the pipe online dynamic mixer according to the spindle rotation frequency, to further calculate a fluid apparent viscosity, and to calculate a fluid mixing intensity according to the fluid apparent viscosity; The construction module is configured to construct a fluid mixing process uniformity index calculation model in a manner that the output torque of the driving motor and the spindle rotation frequency are increasing functions, and the flow rate is a decreasing function; The mixing process uniformity index calculation module is configured to calculate a residence time of the fluid in the pipe online dynamic mixer according to the inlet flow rate and the outlet flow rate, and to calculate a mixing process uniformity index according to the residence time and the fluid mixing intensity through the fluid mixing process uniformity index calculation model; The regulation module is configured to determine a regulation parameter of the pipe online dynamic mixer according to the obtained fluid mixing intensity and the mixing process uniformity index, so as to control the operation of the online dynamic mixer.

9. The pipe online dynamic mixer monitoring system according to claim 8, wherein: According to the apparent viscosity of the fluid The fluid mixing intensity G is calculated according to the following formula: ； Wherein, D represents the pipe diameter, L represents the length of the fluid passing through the pipe online dynamic mixer; T represents the output torque of the driving motor of the pipe online dynamic mixer, represents the main shaft rotation frequency of the driving motor of the pipe online dynamic mixer.

10. The pipe online dynamic mixer monitoring system according to claim 8, wherein: The constructed fluid mixing process uniformity index calculation model is as follows: ； wherein, G is the fluid mixing intensity; and is an empirical parameter.

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