Sludge conditioner automatic optimization system based on Ardunio development board control

Abstract

The invention discloses a sludge conditioner automatic optimization system based on Ardunio development board control, and belongs to the technical field of environmental protection. According to the invention, an Arduino Uno development board, Python programming and a serial communication technology are combined, so that the process that a PC end processes data in real time, and the Arduino controller converts a result into a standard current signal and feeds back the standard current signal to a dosing metering pump is realized. Meanwhile, an electrochemical impedance instrument is used for frequency scanning testing, smoothing processing is conducted on the obtained electrochemical parameters ds / fc through a Savitzky-Golay filter, and the change rate of the electrochemical parameters ds / fc is calculated. And judging whether the dosing amount is in an optimal range or not according to the change rate, if not, continuing to execute the dosing instruction until the change rate value is in an interval range of-0.001 to 0.001, and finally establishing a system prototype of sludge dewatering performance change-electric signal transmission-brain-like decision-nursing dosing intelligent control. The invention is expected to solve the existing problems of excessive dosage, unstable conditioning effect and the like.

Description

Technical Field

[0001] The invention relates to an automatic optimization system for sludge conditioner based on Arduino development board control, belonging to the technical field of environmental protection. Background Art

[0002] With the acceleration of my country's urbanization process, the urban sewage treatment rate has increased year by year, and the sludge output of urban sewage treatment plants has also increased sharply. The sludge from urban sewage treatment plants not only has high water content, is easy to rot, and has a strong odor, but also contains a large number of pathogens, parasite eggs, and heavy metals such as chromium and mercury, and dioxins and other toxic, harmful and carcinogenic substances that are difficult to degrade. If the sludge is randomly piled up without treatment, it will easily cause secondary pollution to groundwater, soil, etc. through erosion and leakage of rainwater, which will directly endanger human health.

[0003] The recycling and safe disposal of excess activated sludge is an important part of the sewage treatment system. An effective sludge management process can not only reduce secondary pollution, but also offset the energy consumption of the sewage treatment system and reduce carbon dioxide emissions. In this process, conditioners are usually used to release bound water and improve the dewaterability of sludge, which can minimize the sludge volume and facilitate subsequent transportation. However, due to the unstable nature of sludge, it is impossible to determine the dosage parameters to ensure the best dewatering effect. Exceeding the standard often occurs during sludge conditioning, which not only reduces the sludge dewatering performance, but also causes higher operating costs. At the same time, decentralized sewage sludge treatment is generally used in rural areas, towns, remote areas, scenic spots with high water treatment requirements, nature reserves or ecological red line areas. In sparsely populated rural areas, the use of centralized sewage treatment systems is prone to cause economic burdens.

[0004] Based on the above situation, decentralized sewage treatment system will become a more suitable choice. At present, in the field of decentralized sludge treatment, unattended decentralized treatment devices have appeared. Through the "unattended" sewage station intelligent remote management platform, it can realize automatic detection of sewage pool water level and other parameters, control the rotation of water pumps and timely start the standby pump, reasonably dispatch the operation of water pumps, effectively improve the utilization rate of pumps, extend the service life of motors, reduce accident downtime, improve drainage capacity, and fully realize drainage automation control and pump room remote monitoring. However, because traditional decentralized sewage treatment facilities cannot meet the requirements of refined treatment in terms of sludge conditioning agent addition, this hinders the development of unattended sewage treatment systems. Summary of the invention

[0005] To solve the above problems, the present invention is achieved through the following technical solutions:

[0006] Taking the characteristic value as the indicator, the Python script program is called to process the data in real time. Based on the existing electrochemical database, the result is converted into a standard current signal through the Arduino controller and fed back to the dosing metering pump. Finally, a system prototype of sludge dewatering performance change-electrical signal transmission-brain-like decision-making-adjustment and dosing intelligent control is established.

[0007] In order to realize real-time data processing on the PC side, the Arduino controller converts the result into a standard current signal and feeds it back to the dosing metering pump, a technical route is designed to build a control unit.

[0008] In the hardware control part, in order to realize the automatic control of the peristaltic pump by the PC, the Arduino Uno development board is used to receive the data message from the PC and transmit the data to the peristaltic pump through the serial port communication. The schematic diagram is shown in the attached figure. Figure 3 As shown. Considering that the peristaltic pump external control adopts RS485 communication protocol, a TTL to RS485 module is added between the Arduino Uno development board and the peristaltic pump external control module for connection and communication. In addition, the module is in working state by connecting the TX pin of the development board and the TTL to RS485 module, as well as connecting the power and ground wires of the module and the development board. In this wiring method, the development board can receive data from the PC and transmit the data to the peristaltic pump through the TX pin, thereby triggering the corresponding working process. In the Python programming environment, the serial library is used, the corresponding serial port information is configured, and the appropriate time interval is set. After each data reading, a data message that conforms to the peristaltic pump communication protocol is constructed and sent to the Arduino Uno development board via the USB cable. At the same time, the running time is set to 10s, and then stop running.

[0009] In the software part, the peristaltic pump is controlled to perform the dosing operation by reading the sensor data, that is, the code realizes the precise control of the dosing machine, and can dynamically adjust the dosing action according to the real-time data to ensure the accuracy and efficiency of the dosing. At the same time, through the reasonable waiting and loop mechanism, the stability and sustainability of the dosing operation are guaranteed.

[0010] According to the electrochemical parameters of sludge during sludge conditioning and dosing s / f c The steps to determine whether the dosage is at the optimal dosage in real time are as follows:

[0011] (1) Use an electrochemical impedance spectroscopy instrument connected to a conductivity electrode probe to perform a frequency sweep test on the sludge to extract d s / f cThe value is recorded as X1. After detecting X1, the control system turns on the dosing pump and adds a certain amount of medicine a0. The value of a0 is 1mg / g dry mud-5mg / g dry mud. It is set to end the dosing within 10s and then turn off the dosing pump.

[0012] (2) Repeat the above operation six times to obtain X1, X2, X3, X4, X5, and X6. Use Savitzky-Golay filter to smooth the data and then calculate the electrochemical parameter d s / f c The rate of change value is calculated as:

[0013]

[0014] in, is the smoothing coefficient, which is obtained by fitting the polynomial using the least squares method.

[0015] (3) The slope is calculated as:

[0016]

[0017] Here, h is the interval between adjacent points.

[0018] (4) Calculate the slope Y1' of X6 using the above method and determine whether the value of Y1' is within the range of -0.001-0.001.

[0019] ① If it is in , stop dosing and record the number of dosing at this time as n1. The total dosage is a0n1 mg / g dry mud.

[0020] ② If it is not, continue to add a certain amount of medicine a0, and also end the dosing within 10s, then turn off the dosing pump, and the electrochemical impedance spectroscopy of the conductivity electrode probe conducts the seventh frequency scanning test on the sludge to extract d s / f c The value is recorded as X7, and the data is smoothed using the Savitzky-Golay filter to calculate the electrochemical parameter d s / f c The rate of change value.

[0021] (5) If the slopes obtained in the first n-1 times are not within the range of -0.001-0.001, repeat the above operation until the absolute value of the slope of the electrochemical parameter ds / fc after the nth frequency scan is less than 0.001, and finally the cumulative dosage is a n , and its calculation formula is:

[0022] a n =na0mg / g dry mud

[0023] The reagents include polyaluminium chloride, aluminium chloride and its hydrate, aluminium salt and its hydrate, ferric salt and its hydrate, ferric salt and its hydrate, and polyferric sulfate. The time interval between two consecutive frequency scans performed by the electrochemical impedance spectroscopy is 2.5 minutes. The sinusoidal voltage applied during the frequency scan of the electrochemical impedance spectroscopy is in the range of 1-1000 mV, and the scanning frequency range is 0.1 Hz-10 MHz. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Attached Figure 1 It is the overall schematic diagram of the present invention. In the figure: 1-PC terminal, 2-electrochemical impedance meter, 3-conductivity electrode probe, 4-stirring instrument, 5-dosing metering pump, 6-drug, 7-Ardunio controller.

[0025] Attached Figure 2 It is the relationship between the characteristic value and the dosage. The ordinate is the ratio between the characteristic value and the corresponding dosage, and the abscissa is the dosage.

[0026] Attached Figure 3 This is the Arduino uno development pin diagram.

[0027] Attached Figure 4 It is a flow chart of the present invention. Specific implementation plan

[0028] The present invention is described in detail below in conjunction with embodiments, but the drawings and embodiments involved are only exemplary and do not constitute any limitation to the scope of the present invention. It should be understood by those skilled in the art that the details and forms of the technical solution of the present invention can be modified or replaced without departing from the spirit and scope of the present invention, but these modifications and replacements all fall within the protection scope of the present invention.

[0029] Example 1:

[0030] Residual activated sludge was taken from an anaerobic / aerobic biological reaction process of a recycled water plant in Beijing with a daily water treatment capacity of 1 million tons. The sludge concentration was 12.4 g / L. The sludge was subjected to a frequency scanning test using an electrochemical impedance spectroscopy instrument connected to a conductivity electrode probe. Based on the dynamic changes of ds / fc, it was determined in real time whether the dosage was in the optimal dosage range, thereby controlling the dosing pump to add a certain amount of Fe2(SO4)3 coagulant liquid to the sludge in stages. Through parallel experiments, the average final dosage of the system was obtained to be 65 mg / g, and the optimal dosage indicated by traditional manual conditioning to evaluate the sludge dewatering performance was 80 mg / g, with a difference of 18.75%. At the same time, the average value of the traditional offline indicator CST for feedback on sludge dewatering performance was measured to be 30.2 mm 2 / s, while the average CST value measured by traditional manual conditioning at the optimal dosage was 37.4mm 2 / s, the difference is 19.25%. This shows that the intelligent conditioning system can effectively and quickly condition the sludge to the optimal state of dehydration.

[0031] Example 2:

[0032] Residual activated sludge was taken from an anaerobic / aerobic biological reaction process of a recycled water plant in Beijing with a daily water treatment capacity of 1 million tons. The sludge concentration was 12.4 g / L. The sludge was subjected to a frequency scanning test using an electrochemical impedance spectroscopy instrument connected to a conductivity electrode probe. Based on the dynamic changes of ds / fc, it was determined in real time whether the dosage was in the optimal dosage range, thereby controlling the dosing pump to add a quantitative amount of FeCl3 coagulant liquid to the sludge in stages. Through parallel experiments, the average final dosage of the system was obtained to be 50 mg / g, and the optimal dosage indicated by traditional manual conditioning to evaluate the sludge dewatering performance was 60 mg / g, with a difference of 16.67%. At the same time, the average value of the traditional offline indicator CST for feedback on sludge dewatering performance was measured to be 17.8 mm 2 / s, while the average CST value measured by traditional manual conditioning at the optimal dosage was 21.1mm 2 / s, the difference is 15.64%. This shows that the intelligent conditioning system can effectively and quickly condition the sludge to the optimal state of dehydration.

Claims

1. A sludge conditioner automatic optimization system based on Ardunio development board control, comprising a PC terminal (1), an electrochemical impedance spectroscopy instrument (2), a conductivity electrode probe (3), a stirrer (4), a dosing metering pump (5), a reagent (6), and an Ardunio controller (7), characterized in that: According to the electrochemical parameters of sludge during sludge conditioning and dosing s / f c The system determines in real time whether the dosage is in the optimal dosage range according to the dynamic changes of the dosage. If so, the system stops automatically; otherwise, the system continues to dose until it reaches the optimal dosage range.

2. According to the sludge conditioner automatic optimization system based on Ardunio development board control described in claim 1, it is characterized in that: The Ardunio controller part (7) combines the technical solutions of Arduino Uno development board, Python programming and serial communication. The Arduino Uno development board adopts a design based on ATmega328P microcontroller and has 14 digital input / output pins, 6 of which support PWM output and can be used to accurately control digital devices. At the same time, the development board has a built-in real serial port, which provides direct hardware support and makes data exchange with the PC possible.

3. According to the sludge conditioner automatic optimization system based on Ardunio development board control described in claim 1, it is characterized in that: According to the electrochemical parameters of sludge during sludge conditioning and dosing s / f c The steps to determine whether the dosage is at the optimal dosage in real time are as follows: (1) Use an electrochemical impedance spectroscopy instrument connected to a conductivity electrode probe to perform a frequency sweep test on the sludge to extract d s / f c The value is recorded as X1. After detecting X1, the control system turns on the dosing pump and adds a certain amount of medicine a0. The value of a0 is 1mg / g dry mud-5mg / g dry mud. It is set to end the dosing within 10s and then turn off the dosing pump. (2) Repeat the above operation six times to obtain X1, X2, X3, X4, X5, and X6. Use Savitzky-Golay filter to smooth the data and then calculate the electrochemical parameter d s / f c The rate of change value is calculated as: in, is the smoothing coefficient, obtained by fitting the polynomial using the least squares method; (3) The slope is calculated as: Where h is the interval between adjacent points; (4) Use the above method to calculate the slope Y1' of X6 and determine whether the value of Y1' is within the range of -0.001-0.001: ① If it is, stop dosing, record the number of dosing at this time as n1, and the total dosing amount is a0n1 mg / g dry mud; ② If it is not, continue to add a certain amount of medicine a0, and also end the dosing within 10s, then turn off the dosing pump, and the electrochemical impedance spectroscopy of the conductivity electrode probe conducts the seventh frequency scanning test on the sludge to extract d s / f c The value is recorded as X7, and the data is smoothed using the Savitzky-Golay filter to calculate the electrochemical parameter d s / f c The rate of change of (5) If the slopes obtained in the first n-1 times are not within the range of -0.001-0.001, repeat the above steps until the electrochemical parameter d s / f c The absolute value of the slope of change is less than 0.001, and the cumulative dosage is finally obtained, which is calculated as follows: a n =na0mg / g dry mud Among them, a n is the cumulative dosage.

4. According to the sludge conditioner automatic optimization system based on Ardunio development board control described in claim 1, it is characterized in that: The types of sludge include municipal surplus sludge, digested sludge, industrial wastewater sludge, river and lake bottom mud, pipeline sediment, fecal sludge, organic biogas residue, and pharmaceutical residue. The sludge concentration ranges from 3 to 100 g / L.

5. According to the sludge conditioner automatic optimization system based on Ardunio development board control described in claim 1, it is characterized in that: The medicaments include polyaluminium chloride, aluminium chloride and its hydrate, aluminium salt and its hydrate, ferric salt and its hydrate, ferric salt and its hydrate, ferrous salt and its hydrate, and polyferric sulfate.

6. The sludge conditioner automatic optimization system based on Arduino development board control according to claim 1 or 3, characterized in that: The time interval between two consecutive frequency scans performed by the electrochemical impedance spectroscopy instrument is 2.5 minutes.

7. The sludge conditioner automatic optimization system based on Arduino development board control according to claim 1 or 3, characterized in that: The sinusoidal voltage applied during the frequency scanning process of the electrochemical impedance spectroscopy is in the range of 1-1000 mV, and the scanning frequency is in the range of 0.1 Hz-10 MHz.

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