Intelligent membrane treatment assembly control system for sewage treatment system

Abstract

The utility model relates to an intelligent membrane treatment assembly control system for a sewage treatment system, which comprises an MBR system, a water production system, a backwashing system, an aeration system and a dosing cleaning system, the MBR system consists of a sludge reflux pump, an MBR tank, an MBR membrane assembly and an ultrasonic liquid level meter, and the water production system comprises a water production tank, a water production pump, a water outlet valve and a pressure gauge. The backwashing system comprises a backwashing water pump and a backwashing water inlet valve, the aeration system comprises a fan and an aeration valve, the dosing cleaning system consists of two chemical backwashing systems, and the valves, the pumps and the sensor components are all connected with the control system and are controlled by the control system. The control system integrates an intelligent data acquisition system, an intelligent communication system and a deep learning chip, realizes intelligent control of the membrane module, realizes real-time monitoring through a multi-parameter sensor, realizes dynamic sensing of an operation state, tracks performance attenuation data of the membrane module in real time, improves prediction precision, reduces waste of the membrane module, and reduces cost. The scientific replacement of the membrane component is realized.

Description

A smart membrane treatment component control system for wastewater treatment systems Technical Field

[0001] This utility model belongs to the technical field of sewage treatment systems, and specifically relates to a control system for an intelligent membrane treatment component in a sewage treatment system. Background Technology

[0002] With the widespread application of membrane separation technology in environmental protection, water treatment, and other fields, the demand for intelligent control of membrane treatment components is becoming increasingly urgent. Traditional membrane treatment component control systems mostly use PLCs or microcontrollers to achieve simple logic control and data acquisition, which suffers from low intelligence, poor control accuracy, and inability to achieve adaptive optimization. These issues make it difficult to meet the requirements for efficient and stable operation under complex working conditions, mainly in the following aspects:

[0003] (1) Low level of intelligence: The control algorithm is simple and mainly relies on the preset logic control program. It lacks intelligent optimization algorithm and cannot automatically adjust the operating parameters according to the real-time working conditions, making it difficult to achieve efficient and stable operation.

[0004] (2) Limited data processing capability: The data processing capability of the sensor is limited, making it difficult to perform complex data analysis and model prediction, and thus unable to achieve online monitoring and prediction of membrane fouling status;

[0005] (3) Reliance on human experience: The operation and maintenance of the system rely heavily on human experience and lack intelligent operation and maintenance methods, resulting in high operation and maintenance costs and low efficiency;

[0006] (4) Difficulty in fault diagnosis: The lack of an effective fault diagnosis and early warning mechanism makes it difficult to quickly locate and eliminate faults, affecting the normal operation of the system;

[0007] (5) Unable to adapt to complex working conditions: It is difficult to cope with complex working conditions such as water quality fluctuations and membrane fouling, and it is difficult to meet the high requirements of membrane treatment systems in emerging application fields. Summary of the Invention

[0008] The purpose of this utility model is to solve the above-mentioned problems. This application proposes an intelligent membrane treatment component control system for sewage treatment systems. The control system adopts artificial intelligence + Internet of Things + big data to collect, analyze and calculate the operating status data of the membrane component, realize the automation and intelligence of the membrane separation process, reduce operating costs, and improve production efficiency and product quality.

[0009] To achieve the above objectives, this utility model provides the following technical solution: an intelligent membrane treatment component control system for a wastewater treatment system, including an MBR system, a product water system, a backwashing system, an aeration system, and a chemical dosing and cleaning system. The MBR system includes a sludge return pump and an MBR tank. An MBR membrane module is installed in the MBR tank, and an ultrasonic level gauge is installed on the wall of the MBR tank. The sludge in the MBR tank is returned to the front end of the biological treatment tank through the sludge return pump.

[0010] The water production system includes a water production tank, which is connected to the MBR membrane module through a water production pipeline and a main pipeline. The water production pipeline is equipped with a water production pump and an outlet valve, and a pressure gauge is installed on the main pipeline near the MBR tank.

[0011] The backwashing system includes a backwash water pump and a backwash inlet valve. The backwash water pump and the backwash inlet valve are connected to the main pipeline and the MBR membrane module via a backwash pipeline.

[0012] The dosing and cleaning system includes a chemical backwashing system one and a chemical backwashing system two. Both chemical backwashing system one and chemical backwashing system two are connected to the main pipeline and the MBR membrane module through dosing pipelines.

[0013] The aeration system includes a blower and an aeration valve, which are connected to the MBR membrane module in the MBR tank via aeration pipes;

[0014] The control system is based on PLC control, and the PLC integrates an intelligent data acquisition system, an intelligent communication system, and a deep learning chip.

[0015] Furthermore, the sludge return pump, ultrasonic level gauge, product water pump, outlet valve, pressure gauge, backwash water pump, backwash inlet valve, blower, and aeration valve are all connected to the control system and controlled by the control system.

[0016] Furthermore, the ultrasonic level gauge is equipped with high and low liquid levels. At a low liquid level, the outlet valve and the water pump are shut off, and at a high liquid level, they are turned on.

[0017] Furthermore, the water production tank stores the produced water, providing a water source for the backwashing system.

[0018] Furthermore: the water pump is a self-priming pump, and the outlet valve and backwash inlet valve are both electric valves.

[0019] Compared with the prior art, the beneficial effects of this utility model are as follows:

[0020] This utility model's control system integrates PLC-based control, an intelligent data acquisition system, an intelligent communication system, and a deep learning chip. It is equipped with sensors such as pressure gauges and ultrasonic level gauges to achieve real-time monitoring of the operating status. The monitoring frequency reaches the millisecond level, and the data acquisition accuracy is improved by more than 90% compared to traditional equipment.

[0021] This invention uses multi-parameter sensors for real-time monitoring to achieve dynamic perception of operating status; based on the deep learning chip in the PLC control system, it automatically optimizes parameters such as transmembrane pressure difference (TMP) and backwashing frequency, reducing manual intervention by more than 90%, and providing early warning of membrane module performance degradation, thus reducing unplanned downtime by 75%; it also reduces the high maintenance costs caused by severe membrane fouling; and it solves the problems of short equipment lifespan and blind replacement decisions. The lifespan of traditional membrane modules relies on experience judgment, and premature replacement wastes costs, while delayed replacement causes system failure.

[0022] The dosing control system employs PID closed-loop control and fuzzy logic algorithms, combined with online and enzymatic catalytic cleaning, improving cleaning efficiency by 50% and reducing reagent consumption by 40-60%. This novel intelligent membrane module control system tracks membrane module performance degradation data in real time, achieving a prediction accuracy of 95% based on historical data and deep learning, guiding scientific replacement. The modular design of this invention allows for flexible expansion of processing capacity, with adjustable single-module processing capacity, reducing the construction period by 40%. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of this utility model, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only for more clearly illustrating the technical solutions in the embodiments of this utility model or the prior art. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 is a schematic diagram of the overall structure of this utility model;

[0025] In the diagram: 1-MBR tank, 2-MBR membrane module, 3-ultrasonic level gauge, 4-permeate tank, 5-permeate pump, 6-outlet valve, 7-pressure gauge, 8-backwash pump, 9-backwash inlet valve, 10-blower, 11-aeration valve.

[0026] 12- Backwashing system one, 13- Backwashing system two, 14- Control system, 15- Sludge return pump. Detailed Implementation

[0027] To enable those skilled in the art to better understand and implement the technical solution of this utility model, the present utility model will be further described below with reference to specific embodiments. However, the embodiments described are only for illustration and are not intended to limit the present utility model.

[0028] Figure 1 shows a smart membrane treatment component control system for a wastewater treatment system, which includes an MBR system, a permeate system, a backwashing system, an aeration system, and a chemical dosing and cleaning system.

[0029] The MBR system includes a sludge return pump 15 and an MBR tank 1. The MBR tank 1 is equipped with an MBR membrane module 2. An ultrasonic level gauge 3 is installed on the wall of the MBR tank 1. The sludge in the MBR tank 1 is returned to the front end of the biological treatment tank through the sludge return pump 15. The ultrasonic level gauge 3 is set with high and low levels. The ultrasonic level gauge 3 is connected to the control system and transmits data to it.

[0030] The water production system includes a water production tank 4 for storing water produced by the MBR tank. The water production tank 4 is connected to the MBR membrane module 2 via a water production pipeline and a main pipeline. The water production pipeline is equipped with a water production pump 5 and an outlet valve 6. A pressure gauge 7 is installed on the main pipeline near the MBR tank 1. The water production pump 5 is a self-priming pump, and the outlet valve 6 is an electric valve. The water production pump 5, the outlet valve 6, and the pressure gauge 7 are all connected to the control system 14, which receives data, analyzes and processes it, and performs further control.

[0031] MBR tank 1 uses real-time data collected by pressure gauge 7 and ultrasonic level gauge 3 to achieve online monitoring and prediction of membrane fouling status; the outlet valve 6 and product water pump 5 are closed when the liquid level is low, and opened when the liquid level is high; the liquid level and the opening of outlet valve 6 are controlled by control system 14.

[0032] The backwashing system includes a backwash water pump 8 and a backwash water inlet valve 9. The backwash water pump 8 and the backwash water inlet valve 9 are connected to the main pipeline and the MBR membrane module 2 through a backwash pipeline. Both the backwash water pump 8 and the backwash water inlet valve are connected to the control system 14.

[0033] The chemical dosing and cleaning system includes a chemical backwashing system 12 and a chemical backwashing system 23. When the cleaning effect of the backwashing system is insufficient to remove contaminants from the membrane surface, the chemical dosing and cleaning system is activated to clean the MBR membrane module 2. Both the chemical backwashing system 12 and the chemical backwashing system 23 are connected to the main pipeline and the MBR membrane module 2 through chemical dosing pipelines. The chemical dosing and cleaning system uses PID closed-loop control and fuzzy logic algorithm for precise chemical dosing.

[0034] The aeration system includes a blower 10 and an aeration valve 11. The blower 10 and the aeration valve 11 are connected to the MBR membrane module 2 in the MBR tank 1 through aeration pipes to provide oxygen to the aerobic microorganisms in the MBR tank and to clean the sludge attached to the surface of the MBR membrane module 2 by flushing it. The aeration valve 11 is linked to the blower 10 to ensure the normal operation of the entire system.

[0035] The control system 14 is based on PLC control and integrates an intelligent data acquisition system, an intelligent communication system and a deep learning chip within the PLC. The intelligent data acquisition system realizes real-time acquisition of data from sensors (pressure gauge, ultrasonic level gauge), and then analyzes and processes the data according to the deep learning chip to realize online monitoring and prediction of membrane fouling status.

[0036] The intelligent communication system enables remote monitoring, fault diagnosis and early warning, interacts with the cloud platform to achieve remote monitoring, supports remote intervention of expert systems, and improves system operation and maintenance efficiency and reliability.

[0037] Deep learning chips, based on advanced algorithms such as fuzzy control, neural networks, and reinforcement learning, construct multi-objective optimization models for membrane flux, energy consumption, and recovery rate, enabling real-time optimization and control of membrane module operating parameters and improving system operating efficiency.

[0038] The control logic of this utility model is as follows:

[0039] (a) Operating cycle

[0040] Close the backwash water pump 8 and the backwash inlet valve 9, and open the product water pump 5 and the outlet valve 6.

[0041] The water pump 5 ran continuously for 13 minutes;

[0042] Turn off the permeate pump 5 and the outlet valve 6 to stop water production and allow the performance of the MBR membrane module 2 to recover for 2 minutes.

[0043] The water pump 5 and the outlet valve 6 are turned on and run for 13 minutes.

[0044] Turn off the permeate pump 5 and the outlet valve 6 to stop water production and allow the performance of the MBR membrane module 2 to recover for 0.5 minutes.

[0045] Turn on the backwash water pump 8 and the backwash inlet valve 9 to perform hydraulic backwashing for 1 minute;

[0046] Turn off the backwash water pump 8 and the backwash inlet valve 9 to stop backwashing, pause water production, and run for 0.5 minutes;

[0047] Start water intake and filtration to begin the next water production cycle. (II) Backwashing Cycle

[0048] After MBR pool 1 has accumulated one cycle of operation;

[0049] Stop the water pump 5 and the outlet valve 6, allow the mixed liquor to enter, and keep the blower 10 aerating for 0.5 minutes;

[0050] The clean water in the permeate tank 4 is sent through the permeate pipeline to the main pipeline and then discharged into the MBR tank 1 to be fed back into the membrane fibers in reverse. The process is run for 1 minute.

[0051] Turn off backwash water pump 8 and backwash inlet valve 9 to stop backwashing; suspend water production and run for 0.5 minutes.

[0052] Start water intake and filtration to begin the next water production cycle. (III) Liquid level linkage control

[0053] When the liquid level in MBR tank 1 is higher than the minimum liquid level and the blower 10 is turned on, the backwash inlet valve 9 is closed and the outlet valve 6 and the permeate pump 5 are turned on. MBR tank 1 produces water normally and the permeate is stored in the permeate tank 4. The permeate pump 5 runs for 13 minutes. When it stops, the permeate pump 5 is stopped first, and then the outlet valve 6 is stopped. The system stops for 2 minutes to allow the membrane fibers to aerate and shake, so that the membrane performance can be restored.

[0054] Open the outlet valve 6 and the product water pump 5 and run for another 13 minutes. Then, stop the product water pump 5 and the outlet valve 6 in sequence, and let the system rest for 30 seconds.

[0055] Open the backwash inlet valve 9 and backwash pump 8 in sequence, backwash continuously for 1 minute, then stop the backwash pump 8 and backwash inlet valve 9 in sequence. The system will pause for 30 seconds before starting the next cycle. (IV) Cleaning Process

[0056] 1) Stop filtration: outlet valve 6 and product water pump 5, air scrubbing: aeration and mixed liquor inlet;

[0057] 2) Pump sodium hypochlorite into the backwashing pipeline through the backwashing system 12, and then input it into the membrane fiber in reverse through the main pipeline for 2 minutes;

[0058] 3) Pause for 4 minutes to allow the drug and the membrane surface to have sufficient contact time;

[0059] 4) Repeat steps 2-3 4 times, running for 24 minutes;

[0060] 5) Turn on the backwash water pump 8, and use the clean water in the product water tank 4 to backwash the membrane fibers and the chemical agents inside the main pipeline for 10 minutes.

[0061] 6) Pump citric acid into the backwashing pipeline through the backwashing system 213, and then input it into the membrane fiber in reverse through the main pipeline for 2 minutes;

[0062] 7) Pause for 4 minutes to ensure sufficient contact time between the agent and the membrane fiber surface;

[0063] 8) Repeat steps 6 and 7 four times, running for 24 minutes;

[0064] 9) Turn on the backwash water pump 8. The clean water in the product water tank 4 flows through the product water pipeline to the main pipeline to backwash the membrane fibers and the chemical agents inside the main pipeline. Run for 10 minutes.

[0065] 10) Re-aerate and filter MBR membrane module 2.

[0066] Valve control

[0067] Under any circumstances, if the outlet valve 6 is closed or not fully opened, the product water pump 5 will stop working (or not start).

[0068] The outlet valve 6 is linked to the product water pump 5 and interlocked with the backwash inlet valve 9.

[0069] Precautions during aspiration:

[0070] (1) If any abnormality occurs during system operation, stop the pumping function of the permeate pump 5; (2) If the blower 10 supplying air for membrane cleaning stops due to any fault, stop the pumping filtration of the permeate pump 5. If it continues to operate in this state, a large amount of sludge aggregates and microparticles will accumulate on the membrane surface, and the transmembrane pressure difference will rise rapidly. At this time, offline chemical cleaning is required to restore it. (3) Operating pressure: The pressure gauge is less than -0.05MPa (the alarm value of the permeate pump is set to -0.05MPa); stop the permeate pump 5, and then stop the outlet valve 6; (4) When the MBR tank is running, the liquid level above the top of the MBR membrane module must be greater than 0.5m. (5) Set the ultrasonic level gauge to control the minimum liquid level. When the liquid level is lower than the minimum liquid level, the permeate pump 5 stops working.

[0071] This invention solves the problems of high reliance on manual labor and low automation in traditional membrane treatment. It addresses the issues of reliance on manual experience to adjust operating parameters (such as pressure, flow rate, and cleaning cycle), resulting in delayed response and large operational errors. A novel intelligent membrane module control system is adopted, using multi-parameter sensors (pressure, dissolved oxygen, conductivity, etc.) for real-time monitoring, enabling dynamic perception of the operating status. Based on machine learning algorithms, the intelligent control system automatically optimizes parameters such as transmembrane pressure differential (TMP) and backwashing frequency, reducing manual intervention by over 90%. This lowers the high maintenance costs caused by severe membrane fouling. Combined with online and enzyme-catalyzed cleaning, cleaning efficiency is increased by 50%, and reagent usage is reduced by 40-60%. Furthermore, it solves the problems of short equipment lifespan and blind replacement decisions. Traditional membrane module lifespan relies on experience-based judgment; premature replacement wastes costs, while delayed replacement leads to system failure.

[0072] This utility model's control system combines artificial intelligence, the Internet of Things, and big data. It not only solves the inherent defects of traditional processes but also promotes the transformation of the water treatment industry towards digitalization and low carbonization, providing efficient and reliable technical support for industrial water conservation, zero emissions, and other scenarios.

[0073] All content not described in detail in this utility model is prior art.

[0074] It should be noted that the above description describes the configuration of a single aerator. Without departing from the technical principles of this application, those skilled in the art can combine the technical solutions in the above embodiments, or make equivalent changes or substitutions to the relevant technical features. Any changes or equivalent substitutions made within the technical concept and / or technical principles of this application will fall within the protection scope of this application.

Claims

1. A smart membrane treatment component control system for a wastewater treatment system, comprising an MBR system, a permeate system, a backwashing system, an aeration system, a chemical dosing and cleaning system, and a control system (14), characterized in that: The MBR system includes a sludge return pump (15) and an MBR tank (1). The MBR tank (1) is equipped with an MBR membrane module (2). An ultrasonic level gauge (3) is installed on the wall of the MBR tank (1). The sludge in the MBR tank (1) is returned to the front end of the biological treatment tank through the sludge return pump (15). The water production system includes a water production tank (4). The water production tank (4) is connected to the MBR membrane module (2) through a water production pipeline and a main pipeline. A water production pump (5) and an outlet valve (6) are installed on the water production pipeline. A pressure gauge (7) is installed on the main pipeline near the MBR tank (1). The backwashing system includes a backwash water pump (8) and a backwash inlet valve (9). The backwash water pump (8) The backwash inlet valve (9) is connected to the main pipeline and the MBR membrane module (2) through the backwash pipeline; the chemical cleaning system includes chemical backwash system one (12) and chemical backwash system two (13), both of which are connected to the main pipeline and the MBR membrane module (2) through the chemical dosing pipeline; the aeration system includes a blower (10) and an aeration valve (11), which are connected to the MBR membrane module (2) in the MBR tank (1) through the aeration pipeline; the control system (14) is based on PLC control, and the PLC integrates an intelligent data acquisition system, an intelligent communication system and a deep learning chip.

2. The intelligent membrane treatment component control system for a wastewater treatment system according to claim 1, characterized in that: The sludge return pump (15), ultrasonic level gauge (3), product water pump (5), outlet valve (6), pressure gauge (7), backwash water pump (8), backwash inlet valve (9), blower (10), and aeration valve (11) are all connected to the control system (14) and are controlled by the control system (14).

3. The intelligent membrane treatment component control system for a wastewater treatment system according to claim 1, characterized in that: The ultrasonic level gauge (3) is set with high and low liquid levels. When the liquid level is low, the outlet valve (6) and the water pump (5) are closed, and when the liquid level is high, they are turned on.

4. The intelligent membrane treatment component control system for a wastewater treatment system according to claim 1, characterized in that: The water production tank (4) stores the water production and provides a water source for the backwashing system.

5. The intelligent membrane treatment component control system for a wastewater treatment system according to claim 1, characterized in that: The water pump (5) is a self-priming pump, and the outlet valve and backwash inlet valve are both electric valves.

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