Intelligent cleaning system for dosing pipeline of sewage treatment plant

Abstract

The application discloses a sewage treatment plant dosing pipeline intelligent cleaning system, which comprises a dosing pipeline, a storage tank is connected to the input end of the dosing pipeline, a sedimentation tank is connected to the output end of the dosing pipeline, a reverse flushing pipeline is connected to one side of the dosing pipeline, a flow rate meter and a water pressure gauge are connected to the dosing pipeline, the input end of the dosing pipeline is connected with the reverse flushing pipeline and the storage tank through a first control valve, and the output end of the dosing pipeline is connected with the reverse flushing pipeline and the sedimentation tank through a second control valve, a reverse flushing pump is connected to the reverse flushing pipeline, and the reverse flushing pipeline is further connected with a flushing storage tank; the system further comprises an intelligent control module; through intelligent monitoring, analysis, control and cleaning effect monitoring and dynamic updating, precise cleaning and dynamic optimization of the PAM dosing pipeline are realized, the PAM dosing system blockage problem is effectively solved, and high cost, low efficiency and safety hazards caused by frequent replacement of the pipeline or manual flushing are avoided.

Description

Technical Field

[0001] This invention relates to the field of wastewater treatment technology, and in particular to a smart cleaning system for chemical dosing pipelines in wastewater treatment plants. Background Technology

[0002] Polyacrylamide (PAM) is widely used as a highly efficient flocculant in wastewater treatment. PAM can effectively promote the coagulation and sedimentation of suspended particles in wastewater, thereby improving wastewater treatment efficiency and water purification effects. However, in practical applications, PAM dosing systems often experience clogging problems, causing numerous challenges to the operation of wastewater treatment plants.

[0003] The primary cause of blockages in PAM dosing systems lies in their inherent properties. PAM is a high-molecular-weight polymer with high viscosity and adsorption capacity. During dosing, the PAM solution easily forms an adhesive layer on the inner wall of the pipe. Over time, these layers gradually accumulate and harden, eventually leading to blockages. Furthermore, factors such as the concentration and temperature of the PAM solution, as well as the material and surface roughness of the pipe, also influence the blockage phenomenon.

[0004] Currently, the common solutions to the clogging problem in PAM dosing systems are as follows:

[0005] When pipes are severely blocked, the problem is usually solved by replacing the blocked pipe. Replacing pipes requires a lot of manpower, resources, and time, especially in large wastewater treatment plants, where frequent pipe replacements can significantly impact normal production.

[0006] Some wastewater treatment plants use manual flushing to resolve pipe blockages. Workers manually flush the clogged pipes using high-pressure water guns or other flushing tools. However, this method is inefficient, and the flushing effect is difficult to guarantee. Summary of the Invention

[0007] In order to solve the above-mentioned technical problems, the present invention provides a smart cleaning system for chemical dosing pipelines in sewage treatment plants.

[0008] The technical solution of this invention is implemented as follows:

[0009] A smart cleaning system for chemical dosing pipelines in a wastewater treatment plant includes a chemical dosing pipeline. The input end of the chemical dosing pipeline is connected to a chemical storage tank, and the output end of the chemical dosing pipeline is connected to a sedimentation tank. A backwashing pipeline is connected to one side of the chemical dosing pipeline. A flow meter and a pressure gauge are connected to the chemical dosing pipeline. The input end of the chemical dosing pipeline is connected to the backwashing pipeline and the chemical storage tank through a first control valve, and its output end is connected to the backwashing pipeline and the sedimentation tank through a second control valve. A backwashing pump is connected to the backwashing pipeline, and the backwashing pipeline is also connected to a flushing liquid storage tank.

[0010] It also includes an intelligent control module, which is communicatively connected to the first control valve, the second control valve, the flow meter, and the pressure gauge. The intelligent control module includes:

[0011] The data acquisition and preprocessing unit is responsible for acquiring signals from the flow rate meter and pressure gauge on the dosing pipeline in real time to obtain raw data on flow rate and pressure.

[0012] The fluid state analysis and feature extraction unit is used to analyze the dynamic changing trend of the fluid state inside the pipeline;

[0013] The control instruction generation and parameter adjustment unit is used to generate a set of instructions for equipment control.

[0014] The cleaning equipment control and execution unit is used to receive the instruction set from the control instruction generation and parameter adjustment unit, and to control the switching of the backwash pump, the first control valve, and the second control valve.

[0015] Furthermore, the data acquisition and preprocessing unit acquires signals from the flow rate meter and pressure gauge on the dosing pipeline in real time, obtains raw data of flow rate and pressure, preprocesses the acquired raw flow rate and pressure signals, including signal amplification, filtering and other operations, removes noise interference, and outputs corrected fluid state analysis data.

[0016] Furthermore, the fluid state analysis and feature extraction unit receives the corrected data output by the data acquisition and preprocessing unit, uses a pre-established fluid dynamics model to extract features from the data, analyzes the dynamic change trend of the fluid state inside the pipeline, including key parameters such as flow velocity, water pressure, Reynolds number, friction loss and local loss, and analyzes the distribution of dirt and fluid resistance in the pipeline in real time based on the dynamic change trend, generating cleaning requirement features that match the current environment.

[0017] Furthermore, the control command generation and parameter adjustment unit calculates the cleaning equipment operating parameters adapted to the current environmental state based on the cleaning demand characteristics generated by the fluid state analysis and feature extraction unit, and generates a set of equipment control commands, including the on / off commands of the first and second control valves, the start / stop commands of the backwash pump, and the adjustment commands of the flushing pressure and flow rate, etc. The set of commands is sent to the cleaning equipment control and execution unit. If the cleaning effect does not meet expectations, the equipment operating parameters are recalculated based on the feedback data to complete the iterative optimization of dynamic parameter adjustment and generate a new set of equipment control commands.

[0018] Furthermore, the cleaning equipment control and execution unit receives the instruction set from the control instruction generation and parameter adjustment unit, controls the opening and closing of the first control valve and the second control valve according to the instruction set, realizes the switching between the dosing pipeline and the backwash pipeline, the storage tank and the sedimentation tank, starts the backwash pump, adjusts the flushing pressure and flow rate, completes the cleaning operation of the dosing pipeline, completes the final execution of equipment power optimization, and generates real-time feedback data of the cleaning process.

[0019] Furthermore, the process of real-time analysis of fouling distribution and fluid resistance within the pipeline includes:

[0020] Receive calibrated flow velocity, water pressure data, and pipeline parameters from the data acquisition and preprocessing unit;

[0021] Calculate the Reynolds number based on the flow velocity and fluid characteristic parameters, and calculate the friction coefficient based on the Reynolds number and pipe roughness.

[0022] Calculate the pressure loss along the friction, and calculate the local pressure loss based on the local drag coefficient and fluid characteristic parameters;

[0023] Analyze the changing trends of pressure loss along the pipeline and local pressure loss to determine whether there is dirt accumulation or blockage in the pipeline.

[0024] By combining the trends of flow velocity and water pressure, the changes in fluid resistance can be further evaluated to determine the location and extent of fouling distribution.

[0025] The analysis results are used as cleaning requirement characteristics and output to the control command generation and parameter adjustment unit.

[0026] Furthermore, the instruction set for controlling the generating device includes:

[0027] Data is received from the fluid state analysis and feature extraction unit, and the friction loss and local loss are compared to see if they exceed a preset threshold.

[0028] The starting power and flow rate of the backwash pump are determined based on the magnitude of friction loss and local loss.

[0029] Based on the changing trends of flow rate and water pressure, adjust the operating parameters of the cleaning equipment, calculate the operating time of the cleaning equipment, and set the cleaning cycle;

[0030] The system generates control commands for the backwash pump, commands for adjusting the flush flow and pressure, and commands for opening and closing the first and second control valves. These commands are then integrated into a command set, which is sent to the cleaning equipment control and execution unit.

[0031] Receive real-time data from the control and execution unit of the cleaning equipment, compare the feedback data with the preset cleaning parameters, determine whether the cleaning process is normal, and if an abnormality is found, adjust the operating parameters of the cleaning equipment or regenerate the instruction set.

[0032] If the cleaning effect does not meet expectations, recalculate the operating parameters of the cleaning equipment;

[0033] A new instruction set is generated based on the new parameters and sent to the control and execution unit of the cleaning equipment for iterative optimization until the cleaning effect reaches the expected level.

[0034] Furthermore, the calculation of the cleaning equipment's operating time includes:

[0035] The fluid state analysis and feature extraction unit receives data and pipeline parameters, and compares whether the friction loss and local loss exceed preset thresholds. If the friction loss or local loss exceeds the preset thresholds, it is determined that there is dirt in the pipeline and it needs to be cleaned.

[0036] Based on the severity of the dirt, i.e. the degree to which the loss value exceeds the threshold, the cleaning time is initially assessed. Specifically, if the loss along the process or the local loss exceeds 20% of the threshold, the dirt is judged as severe; if the loss along the process or the local loss exceeds 10% to 20% of the threshold, the dirt is judged as moderate; if the loss along the process or the local loss exceeds the threshold but does not exceed 10%, the dirt is judged as light.

[0037] Adjustments should be made based on the changing trend of flow velocity and the changes in water pressure.

[0038] The final cleaning time is verified based on the power and flow rate of the cleaning equipment;

[0039] The final determined cleaning time is used as the operating time parameter of the cleaning equipment to set the cleaning cycle.

[0040] Furthermore, the preprocessing of the acquired raw flow velocity and water pressure signals includes:

[0041] The system collects signals from the flow rate meter and pressure gauge on the dosing pipeline in real time to obtain raw data on flow rate and pressure.

[0042] Perform a preliminary check on the collected raw data and remove obviously abnormal data points;

[0043] The flow velocity signal is amplified to ensure that the signal strength reaches the level required for subsequent processing;

[0044] The water pressure signal is amplified to ensure that the signal strength is moderate.

[0045] A low-pass filter is applied to the amplified flow velocity and water pressure signals to remove high-frequency noise interference.

[0046] The filtered flow velocity data is corrected, and the data accuracy is adjusted according to the known characteristics of the flow meter.

[0047] The filtered water pressure data is corrected, and the data accuracy is adjusted according to the known characteristics of the water pressure gauge.

[0048] The corrected flow velocity and water pressure data are integrated into fluid state analysis data and output for use by subsequent units.

[0049] Furthermore, the intelligent control module also includes a cleaning effect monitoring and dynamic update unit, which is used to receive real-time feedback data of the cleaning process generated by the cleaning equipment control and execution unit; continuously monitor the cleaning effect through a data loop analysis module to evaluate whether the cleaning process has achieved the expected goal; determine whether the cleaning parameters need to be further adjusted based on the monitoring results, and provide an adjustment basis for the control command generation and parameter adjustment unit; and complete dynamic updates to adapt to complex environments, ensuring the intelligent operation of the entire system and the continuous optimization of the cleaning effect.

[0050] Compared with the prior art, the present invention has the following advantages:

[0051] 1. This invention installs flow rate and pressure gauges on the dosing pipeline to collect flow rate and pressure data in real time. At the same time, it analyzes the corrected data based on the fluid dynamics model, calculates key parameters, and monitors the dynamic change trend of the fluid state in the pipeline in real time, thereby determining whether there is dirt accumulation or blockage in the pipeline, as well as the distribution location and degree of dirt.

[0052] 2. By calculating the operating parameters of the cleaning equipment that are adapted to the current environmental conditions, a set of equipment control instructions is generated, including control valve opening and closing instructions, backwash pump start / stop instructions, and flushing pressure and flow adjustment instructions. Based on the instruction set, the opening and closing of the first and second control valves are controlled, the pipeline connection is switched, the backwash pump is started, and the flushing pressure and flow are adjusted to complete the cleaning operation of the chemical dosing pipeline.

[0053] 3. This invention achieves precise cleaning and dynamic optimization of PAM dosing pipelines through intelligent monitoring, analysis, control, and cleaning effect monitoring and dynamic updates. It effectively solves the problem of blockage in PAM dosing systems, avoids the high cost, low efficiency and safety hazards caused by frequent pipeline replacement or manual flushing, and improves the efficiency and stability of sewage treatment. Attached Figure Description

[0054] Figure 1 This is a schematic diagram of the chemical dosing pipeline in this invention;

[0055] Figure 2 This is a module framework diagram of the intelligent control module in this invention.

[0056] 1. Dosing pipeline; 2. Storage tank; 3. Sedimentation tank; 4. Backflush pipeline; 5. Flow meter; 6. Pressure gauge; 7. First control valve; 8. Second control valve; 9. Backflush pump; 10. Flushing storage tank. Detailed Implementation

[0057] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention. Example 1

[0058] like Figures 1 to 2 As shown in the figure, this embodiment provides a smart cleaning system for chemical dosing pipelines in a sewage treatment plant, including a chemical dosing pipeline 1. The input end of the chemical dosing pipeline 1 is connected to a chemical storage tank 2, and the output end of the chemical dosing pipeline 1 is connected to a sedimentation tank 3. A backwashing pipeline 4 is connected to one side of the chemical dosing pipeline 1. A flow meter 5 and a pressure gauge 6 are connected to the chemical dosing pipeline 1. The input end of the chemical dosing pipeline 1 is connected to the backwashing pipeline 4 and the chemical storage tank 2 through a first control valve 7, and its output end is connected to the backwashing pipeline 4 and the sedimentation tank 3 through a second control valve 8. A backwashing pump 9 is connected to the backwashing pipeline 4, and the backwashing pipeline 4 is also connected to a flushing storage tank 10.

[0059] It also includes an intelligent control module, which is communicatively connected to the first control valve 7, the second control valve 8, the flow meter 5, and the pressure gauge 6. The intelligent control module includes:

[0060] The data acquisition and preprocessing unit is responsible for acquiring signals from the flow rate meter 5 and the pressure meter 6 on the dosing pipeline 1 in real time, and obtaining the raw data of flow rate and pressure.

[0061] The fluid state analysis and feature extraction unit is used to analyze the dynamic changing trend of the fluid state inside the pipeline;

[0062] The control instruction generation and parameter adjustment unit is used to generate a set of instructions for equipment control.

[0063] The cleaning equipment control and execution unit is used to receive the instruction set of the control instruction generation and parameter adjustment unit, and control the switching of the backwash pump 9, the first control valve 7 and the second control valve 8.

[0064] Furthermore, the data acquisition and preprocessing unit acquires signals from the flow meter 5 and the pressure gauge 6 on the dosing pipeline 1 in real time, obtains raw data of flow velocity and water pressure, preprocesses the acquired raw signals of flow velocity and water pressure to remove noise interference, and outputs corrected fluid state analysis data.

[0065] Furthermore, the fluid state analysis and feature extraction unit receives the corrected data output by the data acquisition and preprocessing unit, uses a pre-established fluid dynamics model to extract features from the data, analyzes the dynamic change trend of the fluid state inside the pipe, and analyzes the dirt distribution and fluid resistance inside the pipe in real time based on the dynamic change trend, generating cleaning requirement features that match the current environment.

[0066] The process of establishing the pre-established fluid dynamics model is as follows:

[0067] Define the model objectives and application scenarios, and establish a fluid dynamics model that can monitor the operating status of the dosing pipeline in real time (whether it is blocked) and provide a basis for cleaning decisions. When applied to the dosing pipeline system, which involves the delivery of the liquid, the fluid flow characteristics, the physical characteristics of the pipeline, and possible blockages need to be considered.

[0068] The following equations are chosen as the basis of the model:

[0069] The continuity equation is used to describe the flow rate conservation of fluid in a pipe:

[0070]

[0071] Darcy-Weisbach's formula is used to calculate pressure loss along the pipe and determine if there is a blockage in the pipe:

[0072]

[0073] The formula for local loss is used to calculate local pressure loss, especially at pipe connections, valves, and other similar locations.

[0074] ;

[0075] Determine the physical properties of the fluid and the pipeline:

[0076] Fluid characteristic parameters:

[0077] Fluid density ρ: Locate or measure the density of the liquid based on its type and temperature;

[0078] Dynamic viscosity of fluid μ: Find or measure the dynamic viscosity of the liquid based on its type and temperature;

[0079] Pipeline characteristic parameters:

[0080] Pipe diameter D: The actual diameter of the dosing pipe;

[0081] Pipe length L: Measure the actual length of the dosing pipeline;

[0082] Pipe roughness ϵ: Locate or measure the roughness of the pipe based on the pipe material and usage.

[0083] Determine the friction coefficient and local drag coefficient

[0084] Darcy's coefficient of friction f:

[0085] For smooth pipes, Blasius's formula can be used:

[0086]

[0087] For rough pipes, the Colebrook formula can be used:

[0088]

[0089] Among them, Reynolds number

[0090] Local resistance coefficient K: Determined by consulting relevant literature or through experiments based on the local structures within the pipeline (such as elbows, valves, etc.);

[0091] Mathematical framework for building the model:

[0092] The above equations and parameters are integrated into a mathematical framework to form a complete fluid dynamics model. The specific steps are as follows:

[0093] Define input parameters:

[0094] Flow velocity v, water pressure p, pipe diameter D, pipe length L, fluid density ρ, fluid dynamic viscosity μ, and pipe roughness ϵ.

[0095] Define output parameters: friction loss Local pressure loss and Reynolds number ;

[0096] Establish a system of equations:

[0097] Continuity equation:

[0098] Darcy-Weisbach's formula:

[0099] Local loss formula:

[0100] Reynolds number calculation:

[0101] Friction coefficient calculation (select Blasius or Colebrook formula based on pipe smoothness);

[0102] Parameter estimation and calibration:

[0103] Theoretical estimate:

[0104] Based on the physical properties of the fluid and the pipeline, the friction coefficient f and the local resistance coefficient K are preliminarily estimated.

[0105] Experimental verification:

[0106] Design experiments to measure pressure loss along the flow path and local pressure loss at different flow velocities.

[0107] Data is collected using high-precision flow meters and pressure sensors.

[0108] Parameter adjustment:

[0109] The experimental data were compared with the model calculation results, and the friction coefficient f and the local resistance coefficient K were adjusted to make the model calculation results consistent with the experimental data.

[0110] Numerical optimization methods (such as least squares) are used to adjust the parameters to ensure the accuracy of the model.

[0111] Model validation:

[0112] Multi-condition verification:

[0113] The applicability of the model was verified under different flow rates, different pipe lengths, and different pipe diameters.

[0114] Ensure that the model can accurately predict changes in fluid state under various operating conditions.

[0115] Long-term stability verification:

[0116] In real-world operating environments, long-term stability tests are conducted on the model to ensure that it maintains accuracy and reliability over extended periods of operation.

[0117] Model documentation:

[0118] Detailed record of the model building process:

[0119] This includes the selected equation, the method for determining the parameters, the experimental verification process, and the method for adjusting the parameters.

[0120] Provide instructions for using the model:

[0121] It explains how to input data, how to interpret the model output, and how to perform routine maintenance.

[0122] Furthermore, the control command generation and parameter adjustment unit calculates the cleaning equipment operating parameters adapted to the current environmental conditions based on the cleaning demand characteristics generated by the fluid state analysis and feature extraction unit, and generates a set of control commands for the equipment.

[0123] The instruction set is sent to the control and execution unit of the cleaning equipment.

[0124] Furthermore, the cleaning equipment control and execution unit receives the instruction set from the control instruction generation and parameter adjustment unit, controls the opening and closing of the first control valve 7 and the second control valve 8 according to the instruction set, realizes the switching between the dosing pipeline 1 and the backwash pipeline 4, the storage tank 2 and the sedimentation tank 3, starts the backwash pump 9, adjusts the flushing pressure and flow rate, completes the cleaning operation of the dosing pipeline 1, completes the final execution of equipment power optimization, and generates real-time feedback data of the cleaning process.

[0125] Furthermore, the process of real-time analysis of fouling distribution and fluid resistance within the pipeline includes:

[0126] Receive calibrated flow velocity, water pressure data, and pipeline parameters from the data acquisition and preprocessing unit;

[0127] Calculate the Reynolds number based on the flow velocity and fluid characteristic parameters, and calculate the friction coefficient based on the Reynolds number and pipe roughness.

[0128] Calculate the pressure loss along the friction, and calculate the local pressure loss based on the local drag coefficient and fluid characteristic parameters;

[0129] Analyze the changing trends of pressure loss along the pipeline and local pressure loss to determine whether there is dirt accumulation or blockage in the pipeline.

[0130] By combining the trends of flow velocity and water pressure, the changes in fluid resistance can be further evaluated to determine the location and extent of fouling distribution.

[0131] The analysis results are used as cleaning requirement characteristics and output to the control command generation and parameter adjustment unit.

[0132] Specifically, real-time data acquisition of flow velocity v and water pressure p is performed. The data acquisition frequency can be set according to actual needs, but it is generally recommended to acquire data once per second to ensure that real-time changes in the fluid state can be captured. For example, to acquire flow velocity v and water pressure p data once per second, input pipe parameters: diameter D = 0.1 m, length L = 10 m, fluid density ρ = 1000 kg / m³, fluid dynamic viscosity μ = 0.001 Pa·s, and pipe roughness ϵ = 0.0001 m.

[0133] The collected flow velocity v and water pressure p data are input into a pre-established fluid dynamics model, along with known pipe parameters (such as pipe diameter D, pipe length L, fluid density ρ, fluid dynamic viscosity μ, and pipe roughness ϵ), to calculate the pressure loss along the pipe.

[0134]

[0135] The friction coefficient f can be calculated using the Reynolds number Re and the pipe roughness ϵ.

[0136]

[0137]

[0138] Calculate local pressure loss:

[0139] The local resistance coefficient K is predetermined based on the local structure within the pipeline (such as elbows, valves, etc.).

[0140] The Reynolds number (Re) is used to determine the flow state (laminar or turbulent) of a fluid:

[0141]

[0142] If Re < 2000, the fluid is laminar;

[0143] If Re > 4000, the fluid is turbulent;

[0144] If 2000≤Re≤4000, the fluid is a transitional flow;

[0145] Extracting key feature parameters: loss along the way Local loss Reynolds number The changing trends of flow velocity v and water pressure p;

[0146] Analyze fouling distribution and fluid resistance, including pressure loss along the flow path. A significant increase in friction loss indicates potential fouling or blockage within the pipeline. By comparing friction loss at different locations, the distribution of fouling can be roughly determined. For example, if the friction loss of a certain section of pipeline is significantly higher than other sections, it suggests that there may be more fouling in that section.

[0147] If local pressure loss A significant increase indicates that local structures within the pipeline (such as elbows and valves) may be clogged with dirt. By analyzing the changing trend of local losses, the degree of blockage in these local structures can be determined.

[0148] Changes in the Reynolds number can indicate whether the flow state of a fluid has changed. If the Reynolds number changes from turbulent to laminar flow, it may indicate a blockage in the pipe.

[0149] Analyze the trends of flow velocity v and water pressure p. If the flow velocity suddenly decreases or the water pressure suddenly increases, it may be a sign of pipe blockage.

[0150] The extracted feature parameters are compared with preset thresholds. If the loss along the process or the local loss exceeds the threshold, an alarm is triggered, indicating that there may be a blockage.

[0151] Based on the analysis results, a cleaning decision is generated. If pipe blockage is confirmed, a backflushing pump is automatically activated for cleaning.

[0152] During the cleaning process, data is continuously collected and analyzed to update the changes in dirt distribution and fluid resistance in real time, ensuring that the cleaning effect meets expectations.

[0153] Furthermore, the instruction set for controlling the generating device includes:

[0154] Data is received from the fluid state analysis and feature extraction unit, and the friction loss and local loss are compared to see if they exceed a preset threshold.

[0155] The starting power and flow rate of the backwash pump 9 are determined based on the magnitude of friction loss and local loss.

[0156] Based on the changing trends of flow rate and water pressure, adjust the operating parameters of the cleaning equipment, calculate the operating time of the cleaning equipment, and set the cleaning cycle;

[0157] The system generates control commands for the backwash pump 9, adjusts the flushing flow rate and pressure, and generates on / off commands for the first control valve 7 and the second control valve 8. These commands are then integrated into a command set, which is sent to the cleaning equipment control and execution unit.

[0158] Receive real-time data from the control and execution unit of the cleaning equipment, compare the feedback data with the preset cleaning parameters, determine whether the cleaning process is normal, and if an abnormality is found, adjust the operating parameters of the cleaning equipment or regenerate the instruction set.

[0159] If the cleaning effect does not meet expectations, recalculate the operating parameters of the cleaning equipment;

[0160] A new instruction set is generated based on the new parameters and sent to the control and execution unit of the cleaning equipment for iterative optimization until the cleaning effect reaches the expected level.

[0161] Furthermore, the calculation of the cleaning equipment's operating time includes:

[0162] The fluid state analysis and feature extraction unit receives data and pipeline parameters, and compares whether the friction loss and local loss exceed preset thresholds. If the friction loss or local loss exceeds the preset thresholds, it is determined that there is dirt in the pipeline and it needs to be cleaned.

[0163] Based on the severity of the dirt, i.e. the degree to which the loss value exceeds the threshold, the cleaning time is initially assessed. Specifically, if the loss along the process or the local loss exceeds 20% of the threshold, the dirt is judged as severe; if the loss along the process or the local loss exceeds 10% to 20% of the threshold, the dirt is judged as moderate; if the loss along the process or the local loss exceeds the threshold but does not exceed 10%, the dirt is judged as light.

[0164] Adjustments should be made based on the changing trend of flow velocity and the changes in water pressure.

[0165] The final cleaning time is verified based on the power and flow rate of the cleaning equipment;

[0166] The final determined cleaning time is used as the operating time parameter of the cleaning equipment to set the cleaning cycle.

[0167] Specifically, the cleaning requirement features are received from the fluid state analysis and feature extraction unit, including friction loss along the flow path. Local losses Reynolds number The changing trends of flow velocity v and water pressure p are used to assess whether these characteristics meet the preset cleaning trigger conditions (such as friction loss or local loss exceeding the threshold).

[0168] Based on the characteristics of the received cleaning request, determine whether the cleaning procedure needs to be initiated and compare the losses along the process. and local loss If the loss along the process or the local loss exceeds the preset threshold, it is determined that the cleaning process needs to be started; if the loss does not exceed the threshold, it returns to the monitoring state and continues to receive new feature data.

[0169] Based on the characteristics of cleaning needs, calculate the operating parameters of the cleaning equipment that are adapted to the current environmental conditions. Based on the magnitude of friction loss and local loss, determine the starting power and flow rate of the backwash pump. Based on the changing trends of flow velocity v and water pressure p, adjust the operating parameters of the cleaning equipment to ensure the cleaning effect. Calculate the operating time of the cleaning equipment and set the cleaning cycle according to the degree of dirt accumulation.

[0170] Based on the determined operating parameters, a set of instructions for equipment control is generated, including control instructions such as start / stop instructions for the backwash pump, and adjustment instructions for flush flow and pressure. Valve control instructions are also generated, including on / off instructions for the first and second control valves, to switch the connection between the pipeline and the backwash pipeline, the chemical storage tank, and the sedimentation tank. The instruction set is then packaged and prepared to be sent to the cleaning equipment control and execution unit.

[0171] Furthermore, the preprocessing of the acquired raw flow velocity and water pressure signals includes:

[0172] The signals from the flow rate meter 5 and the pressure meter 6 on the dosing pipeline 1 are collected in real time to obtain the raw data of flow rate and pressure.

[0173] Perform a preliminary check on the collected raw data and remove obviously abnormal data points;

[0174] The flow velocity signal is amplified to ensure that the signal strength reaches the level required for subsequent processing;

[0175] The water pressure signal is amplified to ensure that the signal strength is moderate.

[0176] A low-pass filter is applied to the amplified flow velocity and water pressure signals to remove high-frequency noise interference.

[0177] The filtered flow velocity data is corrected, and the data accuracy is adjusted according to the known characteristics of the flow meter.

[0178] The filtered water pressure data is corrected, and the data accuracy is adjusted according to the known characteristics of the water pressure gauge 6.

[0179] The corrected flow velocity and water pressure data are integrated into fluid state analysis data and output for use by subsequent units.

[0180] Furthermore, the intelligent control module also includes a cleaning effect monitoring and dynamic update unit, which is used to receive real-time feedback data of the cleaning process generated by the cleaning equipment control and execution unit; continuously monitor the cleaning effect through a data loop analysis module to evaluate whether the cleaning process has achieved the expected goal; determine whether the cleaning parameters need to be further adjusted based on the monitoring results, and provide an adjustment basis for the control command generation and parameter adjustment unit; and complete dynamic updates to adapt to complex environments, ensuring the intelligent operation of the entire system and the continuous optimization of the cleaning effect.

[0181] The specific embodiments of the invention have been described in detail above, but these are merely examples. The invention is not limited to the specific embodiments described above. Those skilled in the art should understand that the embodiments and descriptions in the specification are only illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A sewage treatment plant dosing pipeline intelligent cleaning system, comprising a dosing pipeline (1), characterized in that, The medicament feeding pipeline (1) is connected with a medicament storage tank (2) at the input end, and is connected with a sedimentation tank (3) at the output end, a reverse flushing pipeline (4) is connected to one side of the medicament feeding pipeline (1), a flow rate meter (5) and a water pressure gauge (6) are connected to the medicament feeding pipeline (1), the input end of the medicament feeding pipeline (1) is connected with the reverse flushing pipeline (4) and the medicament storage tank (2) through a first control valve (7), and the output end of the medicament feeding pipeline (1) is connected with the reverse flushing pipeline (4) and the sedimentation tank (3) through a second control valve (8), a reverse flushing pump (9) is connected to the reverse flushing pipeline (4), and a flushing liquid storage tank (10) is further connected to the reverse flushing pipeline (4); Further comprising an intelligent control module, which is in communication connection with the first control valve (7), the second control valve (8), the flow rate meter (5) and the water pressure gauge (6), and the intelligent control module comprises: a data acquisition and preprocessing unit, which is responsible for real-time acquisition of signals of the flow rate meter (5) and the water pressure gauge (6) on the medicament feeding pipeline (1), and acquisition of original data of flow rate and water pressure; a fluid state analysis and feature extraction unit, which is used for analyzing the dynamic change trend of the fluid state in the pipeline; a control instruction generation and parameter adjustment unit, which is used for generating an instruction set for controlling the equipment; a cleaning equipment control and execution unit, which is used for receiving the instruction set of the control instruction generation and parameter adjustment unit, and controlling the opening and closing of the reverse flushing pump (9), the first control valve (7) and the second control valve (8); The data acquisition and preprocessing unit acquires the signals of the flow rate meter (5) and the water pressure gauge (6) on the medicament feeding pipeline (1) in real time, acquires the original data of flow rate and water pressure, pre-processes the collected flow rate and water pressure original signals, removes noise interference, and outputs the corrected fluid state analysis data; The fluid state analysis and feature extraction unit receives the corrected data output by the data acquisition and preprocessing unit, extracts features from the data by using a pre-established fluid dynamics model, analyzes the dynamic change trend of the fluid state in the pipeline, analyzes the dirt distribution and fluid resistance in the pipeline in real time according to the dynamic change trend, and generates cleaning demand characteristics matched with the current environment; The process of analyzing the dirt distribution and fluid resistance in the pipeline in real time comprises: receiving the corrected flow rate, water pressure data and pipeline parameters from the data acquisition and preprocessing unit; calculating the Reynolds number according to the flow rate and fluid characteristic parameters, and calculating the friction coefficient according to the Reynolds number and the pipeline roughness; calculating the along-path pressure loss, and calculating the local pressure loss according to the local resistance coefficient and the fluid characteristic parameters; analyzing the change trend of the along-path pressure loss and the local pressure loss, and judging whether there is dirt accumulation or blockage in the pipeline; further evaluating the change of the fluid resistance in combination with the change trend of the flow rate and the water pressure, and determining the position and degree of the dirt distribution; outputting the analysis result as the cleaning demand characteristics to the control instruction generation and parameter adjustment unit.

2. The intelligent cleaning system for a dosing pipe of a sewage treatment plant according to claim 1, characterized in that: The control instruction generation and parameter adjustment unit calculates the running parameters of the cleaning equipment suitable for the current environmental state according to the cleaning demand characteristics generated by the fluid state analysis and feature extraction unit, generates an instruction set for equipment control, and sends the instruction set to the cleaning equipment control and execution unit.

3. The intelligent cleaning system for a dosing pipe of a sewage treatment plant according to claim 1, characterized in that: The cleaning equipment control and execution unit receives the instruction set from the control instruction generation and parameter adjustment unit, controls the opening and closing of the first control valve (7) and the second control valve (8) according to the instruction set, realizes the switching between the dosing pipeline (1) and the reverse flushing pipeline (4), the storage tank (2) and the sedimentation tank (3), starts the reverse flushing pump (9), adjusts the flushing pressure and flow rate, completes the cleaning operation of the dosing pipeline (1), and generates real-time feedback data of the cleaning process.

4. The intelligent cleaning system for a dosing pipe of a sewage treatment plant according to claim 2, characterized in that, The instruction set for equipment control includes: Receiving data from the fluid state analysis and feature extraction unit, comparing whether the along-the-way loss and the local loss exceed the preset threshold value; According to the size of the along-the-way loss and the local loss, determining the starting power and flow rate of the reverse flushing pump (9); According to the change trend of the flow rate and the water pressure, adjusting the running parameters of the cleaning equipment, calculating the running time of the cleaning equipment, and setting the cleaning period; Generating control instructions for the reverse flushing pump (9), generating adjustment instructions for the flushing flow rate and pressure, generating switch instructions for the first control valve (7) and the second control valve (8), integrating them into an instruction set, and sending the instruction set to the cleaning equipment control and execution unit; Receiving real-time data fed back by the cleaning equipment control and execution unit, comparing the feedback data with the preset cleaning parameters, judging whether the cleaning process is normal, and if an abnormality is found, adjusting the running parameters of the cleaning equipment or regenerating the instruction set; If the cleaning effect does not reach the expectation, the running parameters of the cleaning equipment are recalculated; According to the new parameters, a new instruction set is generated and sent to the cleaning equipment control and execution unit for iterative optimization until the cleaning effect reaches the expectation.

5. The intelligent cleaning system for a dosing pipe of a sewage treatment plant according to claim 4, characterized in that, The calculation of the running time of the cleaning equipment includes: Receiving data and pipeline parameters from the fluid state analysis and feature extraction unit, comparing whether the along-the-way loss and the local loss exceed the preset threshold value; if the along-the-way loss or the local loss exceeds the preset threshold value, it is determined that there is dirt in the pipeline and cleaning is needed; According to the severity of the dirt, i.e. the degree to which the loss value exceeds the threshold value, the cleaning time is preliminarily evaluated. Specifically, if the along-the-way loss or the local loss exceeds more than 20% of the threshold value, it is determined that the dirt is serious; if the along-the-way loss or the local loss exceeds 10%-20% of the threshold value, it is determined that the dirt is moderate; if the along-the-way loss or the local loss exceeds the threshold value but not more than 10%, it is determined that the dirt is relatively light; Adjusting in combination with the change trend of the flow rate and the change situation of the water pressure; According to the power and flow rate of the cleaning equipment, the final cleaning time is verified; The finally determined cleaning time is used as the running time parameter of the cleaning equipment for setting the cleaning period.

6. The intelligent cleaning system for a dosing pipe of a sewage treatment plant according to claim 1, characterized in that, The process of preprocessing the collected flow rate and water pressure original signals includes: Real-time acquisition of the signals of the flow rate meter (5) and the water pressure gauge (6) on the dosing pipeline (1) to obtain the original data of the flow rate and the water pressure; The collected original data is preliminarily checked to remove obviously abnormal data points; Low-pass filters are applied to the flow rate signal and the water pressure signal to remove high-frequency noise interference; The filtered flow rate signal is amplified to ensure that the signal strength reaches the required level for subsequent processing; The filtered water pressure signal is also amplified to ensure that the signal strength is moderate; The filtered flow rate data is corrected to adjust the data accuracy according to the known flow meter characteristics; The filtered water pressure data is corrected to adjust the data accuracy according to the known water pressure gauge (6) characteristics; The corrected flow rate and water pressure data are integrated into fluid state analysis data, which is output for subsequent units.

7. The intelligent cleaning system for a dosing pipe of a sewage treatment plant according to claim 1, characterized in that: The intelligent control module also includes a cleaning effect monitoring and dynamic updating unit, which receives real-time feedback data generated by the cleaning equipment control and execution unit during the cleaning process. Through the data cycle analysis module, the cleaning effect is continuously monitored to evaluate whether the cleaning process has achieved the expected goal. According to the monitoring results, it is determined whether the cleaning parameters need to be further adjusted, and the adjustment basis is provided for the control instruction generation and parameter adjustment unit. Dynamic updating for complex environment adaptation is completed to ensure the intelligent operation of the entire system and the continuous optimization of the cleaning effect.

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