CN116332427A - Water treatment device based on electrochemical method and application thereof - Google Patents
Water treatment device based on electrochemical method and application thereof Download PDFInfo
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- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F9/00—Multistage treatment of water, waste water or sewage
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- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F1/00—Treatment of water, waste water, or sewage
- C02F1/30—Treatment of water, waste water, or sewage by irradiation
- C02F1/32—Treatment of water, waste water, or sewage by irradiation with ultraviolet light
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- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F1/00—Treatment of water, waste water, or sewage
- C02F1/46—Treatment of water, waste water, or sewage by electrochemical methods
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- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F1/00—Treatment of water, waste water, or sewage
- C02F1/52—Treatment of water, waste water, or sewage by flocculation or precipitation of suspended impurities
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- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F2209/00—Controlling or monitoring parameters in water treatment
- C02F2209/06—Controlling or monitoring parameters in water treatment pH
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- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F2209/00—Controlling or monitoring parameters in water treatment
- C02F2209/08—Chemical Oxygen Demand [COD]; Biological Oxygen Demand [BOD]
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- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F2209/00—Controlling or monitoring parameters in water treatment
- C02F2209/20—Total organic carbon [TOC]
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02W—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO WASTEWATER TREATMENT OR WASTE MANAGEMENT
- Y02W10/00—Technologies for wastewater treatment
- Y02W10/10—Biological treatment of water, waste water, or sewage
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Abstract
The invention provides a water treatment device based on an electrochemical method and application thereof, and relates to the technical field of chemical raw water treatment equipment.
Description
Technical Field
The invention relates to the technical field of chemical raw water treatment equipment, in particular to a water treatment device based on an electrochemical method and application thereof.
Background
The electrochemical water treatment technology utilizes electrochemical reaction to purify water, and is an environment-friendly, efficient and energy-saving method for treating water. The basic principle is that under the action of a certain electric field, the impurities in the water are subjected to chemical changes such as oxidation, reduction and the like through electrochemical reaction, so that the aim of purifying the water quality is fulfilled. In household tap water, the quality of water can be improved by removing impurities such as iron, manganese, odor and the like; in the industrial wastewater treatment, pollutants such as heavy metals, organic matters and the like can be removed; in sea water desalination, the reverse osmosis membrane and electrochemical treatment can be combined to reduce the treatment cost and improve the water quality.
The existing electrochemical water treatment has the following problems:
problem one: in the existing electrochemical water treatment, many parts need to be manually operated, and the whole intelligent level is low.
And a second problem: all electrical elements of the existing automatic electrochemical water treatment are operated according to a specified program, the whole system cannot realize intelligent control, and the efficiency control of the whole water treatment stage is poor.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a water treatment device based on an electrochemical method and application thereof, which solve the problems that the prior electrochemical water treatment needs manual operation in many parts, the whole intelligent level is low, the intelligent control cannot be realized by the system, and the efficiency control in the whole water treatment stage is poor.
Technical proposal
In order to achieve the above purpose, the invention is realized by the following technical scheme: the water treatment device based on the electrochemical method comprises a raw water tank body, wherein a raw water pipeline is externally connected to the raw water tank body, a liquid level sensor is arranged in the raw water tank body, an open aeration tank, a secondary treatment tank, a tail treatment tank, an auxiliary tank body and a primary reaction tank are arranged outside the raw water tank body, a communication pipeline is arranged among the raw water tank body, the open aeration tank, the secondary treatment tank, the tail treatment tank, the auxiliary tank body and the primary reaction tank, and a control host is arranged at a position close to the raw water tank body.
Preferably, the secondary treatment tank, the tail treatment tank and the primary reaction tank are symmetrically arranged in a double-tank manner, the two groups of the secondary treatment tank, the tail treatment tank and the primary reaction tank which are arranged in a opposite supporting manner are all connected through communication pipelines, an auxiliary tank body is arranged between the primary reaction tanks, and the auxiliary tank body is a middle water storage tank.
Preferably, an intermediate control pump is arranged between the intermediate reaction tank body and the primary reaction tank, a tail control pump is connected to the tail end pipeline of the tail treatment tank, and the tail control pump and the intermediate control pump are electrically connected with a control host.
Preferably, the raw water tank body, the open aeration tank, the secondary treatment tank, the tail treatment tank, the auxiliary tank body and the primary reaction tank are respectively provided with a liquid level sensor, a flow rate sensor, a temperature sensor, a ph meter, a dissolved oxygen sensor and a conductivity measuring instrument, and are electrically connected with the control host. ORP, ammonia nitrogen, residual chlorine, COD, BOD, heavy metals, nitrous acid-nitrate index, total phosphorus and chlorophyll
Preferably, an application process of a water treatment device based on an electrochemical method, the application process comprises the following modules:
and (3) a water inlet system: introducing water to be treated into the treatment device, and communicating the water with the treatment device through a pipeline and/or a pump;
and (3) a water outlet system: discharging the treated water from the treatment device, and communicating the treated water through a pipeline and/or a pump;
solution circulation system: circulating electrolyte from the processing device to the reservoir;
an automatic control system: realizing automatic control and monitoring of parameters including temperature, PH value, current, voltage and the like in the processing device;
a data processing system: the method comprises the steps of monitoring various parameters in a processing device in real time and collecting data, and realizing evaluation and optimization of water treatment effect through data processing and analysis;
and (3) optimizing a control system: and (3) learning and optimizing the processed data, and adjusting specific physicochemical parameters in each processing flow.
Preferably, the detailed flow of the water inlet system is as follows:
sp1, the water inlet receives water flow from a tap water pipeline and filters out larger impurities through a filter;
sp2, the magnetic valve controls the inflow, and monitor the inflow in real time through the water flow meter;
sp3, the PH value of the inlet water is monitored in real time by a PH sensor;
sp4, a water quality sensor for inflow water monitors indexes such as total dissolved solids, COD, BOD, TOC, ammonia nitrogen and the like of inflow water in real time;
sp5, the inlet water is sent into an electrochemical reaction tank through a pressure pump.
Preferably, the detailed flow of the water outlet system is as follows:
sp1, effluent flows out from the electrochemical reaction tank and is precipitated by the precipitation tank;
sp2, sediment in the sedimentation tank is discharged through a sludge pump;
sp3, regulating the flow of the effluent through a magnetic valve, and monitoring the water yield in real time through a water flowmeter;
sp4 and a water quality sensor for outlet water monitor indexes such as total dissolved solids, COD, BOD, TOC and ammonia nitrogen of the outlet water in real time;
sp5, the effluent is disinfected by a UV lamp and finally discharged through a water outlet.
Preferably, the detailed flow of the solution circulation system is as follows:
sp1, the solution flows out of the electrochemical reaction tank, and larger impurities are filtered by a filter;
sp2, controlling the flow of the solution by a magnetic valve, and monitoring the flow of the solution in real time by a water flow meter;
sp3, the PH value of the solution is monitored in real time by a PH sensor of the solution;
sp4, the water quality sensor of the solution monitors indexes such as total dissolved solids, COD, BOD, TOC, ammonia nitrogen and the like of the solution in real time;
sp5, the solution is heated by a heater and finally flows into the electrochemical reaction tank again.
Preferably, the detailed flow of the automatic control system is as follows:
sp1, the system acquires real-time data of water inflow, water outflow and solution through a sensor;
sp2, an automatic control system regulates the flow of water and solution through a PID algorithm so as to ensure the stability of the solution in the electrochemical reaction tank;
sp3, the automatic control system regulates the heater through a PID algorithm to ensure the temperature stability of the solution;
sp4, an automatic control system adjusts the potential in the electrochemical reaction tank through a PID algorithm so as to control the reaction rate and effect;
sp5, the automatic control system can realize the monitoring and control of the system through remote control.
Preferably, the specific flow of the data processing system is as follows:
sp1, acquisition data: various parameters in the systems such as water inlet, water outlet, solution circulation and the like are monitored in real time through sensors and monitoring instruments, such as PH value, dissolved oxygen, conductivity and the like;
sp2, stored data: storing the acquired real-time data in a database for subsequent processing and analysis;
sp3, data cleaning: cleaning and processing the acquired data, removing abnormal values and noise interference, and ensuring the accuracy and reliability of the data;
sp4, data analysis: analyzing and excavating historical data through a data excavation and analysis technology, finding problems and abnormal conditions existing in the water treatment process, and providing references for subsequent optimization;
sp5, predictive modeling: modeling and predicting the water treatment process by utilizing technologies such as machine learning, neural network and the like, predicting possible abnormal conditions in the future, and timely taking measures to avoid accidents;
sp6, real-time monitoring: and according to analysis and prediction results, the water treatment process is monitored and controlled in real time, so that the stability and safety of the water treatment process are ensured.
Advantageous effects
The invention provides a water treatment device based on an electrochemical method and application thereof. The beneficial effects are as follows:
1. the invention adopts automatic control, can realize real-time monitoring and control of the water treatment process, avoids errors and risks caused by manual operation, and improves the water treatment efficiency and safety; under the monitoring of an automatic control system, the water treatment device can timely find and treat abnormal conditions, and the water quality is prevented from being polluted or equipment is prevented from being damaged; meanwhile, the automatic control system can conduct prediction and optimization treatment through data analysis, and achieves intellectualization of water treatment.
2. The invention adopts data processing, can monitor and analyze various data in real time, including water quality, water quantity, voltage, current, temperature and the like, can find abnormal conditions in the water treatment process, realizes real-time monitoring and adjustment of the water treatment process, and ensures the quality and efficiency of water treatment; meanwhile, the data processing system can also carry out quality analysis on the processed water, provide related reports, help the supervision department and users to know the quality condition of the water, and realize long-term monitoring and control of the water quality.
3. The water treatment device adopting the modularized electrochemical method can be combined according to actual needs, and different modules can be selected and matched according to different water treatment demands, so that a more flexible and efficient water treatment mode is realized; meanwhile, the reliability and maintainability of the device can be improved through the modularized design, and the device is convenient for a user to repair and replace.
Drawings
FIG. 1 is a schematic diagram of a main layout of the present invention;
FIG. 2 is a schematic side layout of the present invention;
fig. 3 is a main flow chart of the present invention.
Wherein: 1. an intermediate reaction tank; 2. opening an aeration tank; 3. a secondary treatment tank; 4. a tail treatment tank; 5. the tail part controls the pump; 6. a raw water tank body; 7. a communication pipe; 8. a control host; 9. an auxiliary tank; 10. a primary reaction tank; 11. the pump is controlled intermediately.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
First embodiment:
as shown in the figure, the water treatment device based on the electrochemical method comprises a raw water tank body 6, a raw water pipeline is externally connected to the raw water tank body 6, a liquid level sensor is arranged in the raw water tank body 6, an open aeration tank 2, a secondary treatment tank 3, a tail treatment tank 4, an auxiliary tank 9 and a primary reaction tank 10 are arranged outside the raw water tank body 6, a communication pipeline 7 is arranged between the raw water tank body 6, the open aeration tank 2, the secondary treatment tank 3, the tail treatment tank 4, the auxiliary tank 9 and the primary reaction tank 10, an intermediate control pump 11 is arranged between the intermediate reaction tank 1 and the primary reaction tank 10, the tail control pump 5 is connected to the tail end pipeline of the tail treatment tank 4, the tail control pump 5 and the intermediate control pump 11 are electrically connected with a control host 8, the position close to the raw water tank body 6 is provided with the control host 8, and the liquid level sensor, the flow rate sensor, the temperature sensor, the ph meter, the dissolved oxygen sensor and the conductivity meter are electrically connected with the control host 8. The two-stage treatment tank 3, the tail treatment tank 4 and the primary reaction tank 10 are symmetrically arranged by double tank bodies, two groups of two-stage treatment tank 3, the tail treatment tank 4 and the primary reaction tank 10 which are arranged in a opposite supporting way are all connected by adopting a communication pipeline 7, wherein an auxiliary tank body 9 is arranged between the primary reaction tanks 10, the auxiliary tank body 9 is an intermediate water storage tank, the raw water tank body 6, the open aeration tank 2, the two-stage treatment tank 3, the tail treatment tank 4, the auxiliary tank body 9 and the primary reaction tank 10 can be in a proper combination mode according to actual conditions, and different tank bodies are matched at will according to actual water treatment conditions, so that the treated water meets the requirements, and the raw water tank bodies 6, the open aeration tank 2, the two-stage treatment tank 3, the tail treatment tank 4, the auxiliary tank body 9 and the primary reaction tank 10 are all independent modules and are connected by adopting pipelines.
Specific embodiment II:
as shown in the figure, an application flow of the water treatment device based on the electrochemical method comprises the following modules:
and (3) a water inlet system: introducing water to be treated into the treatment device, and communicating the water with the treatment device through a pipeline and/or a pump;
and (3) a water outlet system: discharging the treated water from the treatment device, and communicating the treated water through a pipeline and/or a pump;
solution circulation system: circulating electrolyte from the processing device to the reservoir;
an automatic control system: realizing automatic control and monitoring of parameters including temperature, PH value, current, voltage and the like in the processing device;
a data processing system: the method comprises the steps of monitoring various parameters in a processing device in real time and collecting data, and realizing evaluation and optimization of water treatment effect through data processing and analysis;
and (3) optimizing a control system: and (3) learning and optimizing the processed data, and adjusting specific physicochemical parameters in each processing flow.
The detailed flow of the water inlet system is as follows:
sp1, the water inlet receives water flow from a tap water pipeline and filters out larger impurities through a filter;
sp2, the magnetic valve controls the inflow, and monitor the inflow in real time through the water flow meter;
sp3, the PH value of the inlet water is monitored in real time by a PH sensor;
sp4, a water quality sensor for inflow water monitors indexes such as total dissolved solids, COD, BOD, TOC, ammonia nitrogen and the like of inflow water in real time;
sp5, the inlet water is sent into an electrochemical reaction tank through a pressure pump.
Preferably, the detailed flow of the water outlet system is as follows:
sp1, effluent flows out from the electrochemical reaction tank and is precipitated by the precipitation tank;
sp2, sediment in the sedimentation tank is discharged through a sludge pump;
sp3, regulating the flow of the effluent through a magnetic valve, and monitoring the water yield in real time through a water flowmeter;
sp4 and a water quality sensor for outlet water monitor indexes such as total dissolved solids, COD, BOD, TOC and ammonia nitrogen of the outlet water in real time;
sp5, the effluent is disinfected by a UV lamp and finally discharged through a water outlet.
Preferably, the detailed flow of the solution circulation system:
sp1, the solution flows out of the electrochemical reaction tank, and larger impurities are filtered by a filter;
sp2, controlling the flow of the solution by a magnetic valve, and monitoring the flow of the solution in real time by a water flow meter;
sp3, the PH value of the solution is monitored in real time by a PH sensor of the solution;
sp4, the water quality sensor of the solution monitors indexes such as total dissolved solids, COD, BOD, TOC, ammonia nitrogen and the like of the solution in real time;
sp5, the solution is heated by a heater and finally flows into the electrochemical reaction tank again.
Preferably, the detailed flow of the automatic control system is as follows:
sp1, the system acquires real-time data of water inflow, water outflow and solution through a sensor;
sp2, an automatic control system regulates the flow of water and solution through a PID algorithm so as to ensure the stability of the solution in the electrochemical reaction tank;
sp3, the automatic control system regulates the heater through a PID algorithm to ensure the temperature stability of the solution;
sp4, an automatic control system adjusts the potential in the electrochemical reaction tank through a PID algorithm so as to control the reaction rate and effect;
sp5, the automatic control system can realize the monitoring and control of the system through remote control.
Preferably, the specific flow of the data processing system is as follows:
sp1, acquisition data: various parameters in the systems such as water inlet, water outlet, solution circulation and the like are monitored in real time through sensors and monitoring instruments, such as PH value, dissolved oxygen, conductivity and the like;
sp2, stored data: storing the acquired real-time data in a database for subsequent processing and analysis;
sp3, data cleaning: cleaning and processing the acquired data, removing abnormal values and noise interference, and ensuring the accuracy and reliability of the data;
sp4, data analysis: analyzing and excavating historical data through a data excavation and analysis technology, finding problems and abnormal conditions existing in the water treatment process, and providing references for subsequent optimization;
sp5, predictive modeling: modeling and predicting the water treatment process by utilizing technologies such as machine learning, neural network and the like, predicting possible abnormal conditions in the future, and timely taking measures to avoid accidents;
sp6, real-time monitoring: and according to analysis and prediction results, the water treatment process is monitored and controlled in real time, so that the stability and safety of the water treatment process are ensured.
Third embodiment:
in this embodiment, in combination with the structural content of the above specific embodiment, a control system is further given:
1. and (3) data acquisition:
the data acquisition stage is the basis of an intelligent control scheme, and related parameters in the water treatment process, such as temperature, pH value, conductivity and the like, are monitored and acquired in real time through equipment such as a sensor and the like. The collected data can help model and predict trends and results of the water treatment process. The data acquisition can be realized by the following steps:
parameters and sensor types to be monitored are determined.
And installing a sensor and monitoring equipment, and calibrating and testing the equipment.
And connecting the sensor to the data acquisition equipment, and connecting the data acquisition equipment with the water treatment system.
Parameters of the acquisition equipment, such as sampling frequency, data format, transmission mode and the like, are configured.
And collecting data, and processing and storing the data.
2. And (3) data transmission:
the data transmission is a process of transmitting the collected data to a cloud end or a server for processing and storage. The data transmission can be realized by the following steps:
the destination and manner of data transfer is determined, such as using a cloud service, a local server, or an external data storage service.
Data transfer protocols and modes are configured, such as using MQTT, HTTP or TCP/IP protocols for transfer.
The frequency and manner of data transmission is set, such as real-time transmission or periodic transmission.
And encrypting and verifying the security of the transmission data.
3. And (3) data processing:
data processing is the process of analyzing and processing the acquired data. The data processing can be realized by the following steps:
and cleaning and preprocessing the acquired data, such as removing abnormal data, filling missing values, smoothing and normalizing.
Appropriate data processing techniques are selected, such as rule-based processing, machine learning, deep learning, and the like.
And (5) establishing a model and predicting the trend and result of the water treatment process.
And optimizing the model, and improving the prediction precision and effect of the model.
4. Intelligent decision:
the intelligent decision is a process for making an intelligent decision on the water treatment process according to the analysis result. The intelligent decision can be realized by the following steps:
decision rules and logic are determined, such as threshold-based, rule-based, machine learning-based, and the like.
An appropriate decision method is selected, such as rule-based decisions, optimization algorithm-based decisions, machine learning-based decisions, and the like.
And establishing a decision model to realize intelligent decision.
5. Control instruction transfer:
the control command transmission is to transmit the result of intelligent decision to the control system, such as control commands for adjusting power supply output, switching valve, etc. The control instruction transmission can be realized by the following steps:
and determining equipment and control modes needing to be controlled, such as controlling a water pump, adjusting a valve, adjusting the dosage and the like.
And generating a control instruction according to the result of the intelligent decision.
The control instructions are transmitted to a control system, such as a PLC, SCADA, etc.
And monitoring the execution condition of the control instruction, if so, whether the expected effect is achieved.
6. The control system responds to:
control system response is the process by which the control system responds to control instructions. The control system response may be achieved by:
and receiving a control instruction.
And adjusting the running state of related equipment according to the control instruction, such as adjusting the rotating speed of the water pump, opening/closing the valve, adjusting the dosage and the like.
The operating state of the device, such as the current, voltage, flow, etc. parameters of the device are monitored.
7. And (3) equipment operation state feedback:
the equipment operation state feedback is a process of monitoring and feeding back the equipment operation state. The feedback of the running state of the equipment can be realized by the following steps:
the operating state of the device, such as the current, voltage, flow, etc. parameters of the device are monitored.
And transmitting the monitored data to a data acquisition system.
The collected data is processed and analyzed, such as statistics and trend analysis.
And evaluating and optimizing the running state of the equipment according to the data analysis result.
8. And (3) system optimization:
the system optimization is a process of optimizing and improving the system according to the feedback information. The system optimization can be realized by the following steps:
and (5) improving and optimizing the model and the algorithm according to the feedback information.
Adjustments and improvements to the system are made, such as updating control strategies, changing equipment, etc.
Re-evaluate and optimize system performance, such as improving water treatment efficiency, reducing water treatment costs, etc.
The above are 8 basic flows of the intelligent control solution,
for hardware equipment, it is necessary to select appropriate equipment and sensors according to specific water treatment requirements. On this basis, it is necessary to connect the sensors and the control devices to the control system using field bus technology, such as Modbus, profibus.
For software system aspects, a suitable operating system and software platform need to be employed. Generally, control systems based on Windows platforms are more common, and Visual Studio can be used to develop control programs. In addition, suitable databases and data processing techniques, such as MySQL, MATLAB, etc., are required for data storage and analysis.
In terms of control strategies, corresponding control algorithms, such as PID control, fuzzy control and the like, are adopted according to specific requirements of water treatment, so that control and optimization of water treatment equipment are realized.
Connection of hardware equipment and a sensor: the hardware devices and sensors are connected to a computer or control system through appropriate interfaces and ensure that the data collected by the sensors can be read and processed.
And (3) data acquisition: a Python program was written and sensor readings were taken and stored in a local database, such as MySQL, mongoDB.
Data preprocessing: and writing a data preprocessing program through Python, wherein the data preprocessing program comprises the steps of data cleaning, data normalization, feature extraction and the like, so as to ensure the data quality and effectiveness.
Building a neural network model: a suitable neural network model, such as the multi-layer perceptron Multilayer Perceptron, convolutional neural network Convolutional Neural Network, etc., is selected and a corresponding code is written using Python to build the neural network model.
Training data: training the neural network by using the preprocessed data, and adjusting model parameters to improve the accuracy and the prediction capability of the model.
Model verification: and verifying the trained model by using the test data, and evaluating the accuracy and generalization capability of the model.
Deployment model: and deploying the trained neural network model into a control system so as to realize intelligent control.
And (3) real-time monitoring: and (3) writing a control program through Python to realize real-time monitoring and control of the water treatment equipment. Meanwhile, the data monitored in real time are fed back to the neural network model to realize self-adaptive control and optimize the water treatment process.
It should be noted that the above flow is only a general framework, and the specific implementation needs to be implemented by combining specific hardware devices, software systems and control strategies to implement corresponding programming.
And (3) data acquisition:
import serial
serial ('/dev/ttyACM 0', 9600) # serial devices and baud rates of ser=serial/serial ('/dev/ttyACM 0', 9600) need to be adjusted according to specific situations
while True:
data = ser.readline()
# processing data, e.g. decoding, conversion to floating point numbers, etc
# store data to database etc
Data preprocessing:
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
data = pd.read_csv('data.csv')
# data cleansing, missing value, outlier processing, etc
Extracting characteristics, selecting proper characteristics, and performing pretreatment operations such as normalization
scaler = MinMaxScaler()
data = scaler.fit_transform(data)
Building a neural network model:
import torch.nn as nn
import torch.optim as optim
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
# definition neural network structure
self.fc1 = nn.Linear(10, 20)
self.fc2 = nn.Linear(20, 10)
self.fc3 = nn.Linear(10, 1)
def forward(self, x):
Forward propagation of #
x = self.fc1(x)
x = nn.functional.relu(x)
x = self.fc2(x)
x = nn.functional.relu(x)
x = self.fc3(x)
return x
net = Net()
criterion = nn.MSELoss()
optimizer = optim.SGD(net.parameters(), lr=0.01)
Training data:
import numpy as np
# load data
inputs = np.load('inputs.npy')
labels = np.load('labels.npy')
for epoch in range(1000):
running_loss = 0.0
for i in range(len(inputs)):
# input data into neural network model
optimizer.zero_grad()
outputs = net(inputs[i])
loss = criterion(outputs, labels[i])
loss.backward()
optimizer.step()
running_loss += loss.item()
print('[%d] loss: %.3f' % (epoch + 1, running_loss / len(inputs)))
Model verification:
import numpy as np
inputs = np.load('test_inputs.npy')
labels = np.load('test_labels.npy')
with torch.no_grad():
outputs = net(inputs)
loss = criterion(outputs, labels)
print ('test set MSE:%.3 f'% loss. Item ())
Deployment model:
import serial
serial ('/dev/ttyACM 0', 9600) # serial devices and baud rates of ser=serial/serial ('/dev/ttyACM 0', 9600) need to be adjusted according to specific situations
while True:
data = ser.readline()
# processing data, e.g. decoding, conversion to floating point numbers, etc
Inputting the data into the neural network model to obtain a control instruction
# sends control instructions to a control system to realize intelligent control
And (3) real-time monitoring:
import serial
serial ('/dev/ttyoacm 0', 9600) # serial devices and baud rates require data source setup
while True:
data = ser.readline()
# processing data, e.g. decoding, conversion to floating point numbers, etc
# output monitoring information, such as real-time water quality parameters, equipment status, etc
Data visualization:
import matplotlib.pyplot as plt
import pandas as pd
data = pd.read_csv('data.csv')
processing data, e.g. screening out data columns to be displayed, sorting in time, etc
x=data [' time ]
y1=data [ 'parameter 1' ]
y2=data [' parameter 2 ]
plt.plot (x, y1, label= 'parameter 1')
ply (x, y2, label= 'parameter 2')
plt.xlabel ('time')
plt.ylabel ('parameter value')
plt.title ('Water quality monitoring')
plt.legend()
plt.show()
The following is a water treatment intelligent control system code based on Python and neural network algorithm:
import time
import serial
import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense, Activation
from sklearn.preprocessing import MinMaxScaler
# 1 initializing serial communications
ser = serial.Serial('COM3', 9600, timeout=1)
# 2 setting model parameters
input_size=3# input layer node number
hidden_size=5# hidden layer node number
output_size=1# output layer node number
learning_rate=0.01# learning rate
Number of epochs=100# training cycles
# 3 construction of neural network model
model = Sequential()
model.add(Dense(hidden_size, input_dim=input_size))
model.add(Activation('relu'))
model.add(Dense(output_size))
model.add(Activation('linear'))
model.compile(loss='mse', optimizer='adam')
# 4 read historical data and normalize
data = pd.read_csv('data.csv')
scaler = MinMaxScaler()
data_scaled = scaler.fit_transform(data)
# 5 training neural network model
X_train = data_scaled[:-1, :-1]
y_train = data_scaled[1:, -1:]
model.fit(X_train, y_train, epochs=epochs, verbose=0)
# 6 real-time control of Water treatment facilities
while True:
# 6.1 monitoring Water quality parameters
data = ser.readline()
# processing data, e.g. decoding, conversion to floating point numbers, etc
# output monitoring information, such as real-time water quality parameters, equipment status, etc
# 6.2 predicting the Water quality parameter at the next time according to the monitoring result
X_predict = scaler.transform(np.array([[param1, param2, param3]]))
y_predict = model.predict(X_predict)
Processing y_prediction, e.g. inverse normalization, conversion to actual parameter values, etc
# 6.3 Regulation of Water treatment plant according to the prediction results
# control of water treatment apparatus, e.g. switching on or off a certain electrode, regulating current, etc
# 6.4 recording control results
timestamp = time.time()
# write timestamp, param, param2, param3, y_Prect etc. information into database or file
# 6.5 control cycle
time.sleep(10)
# data visualization can be performed using code in the seventh flow
Data visualization
import matplotlib.pyplot as plt
Reading historical data and denormalizing
data = pd.read_csv('data.csv')
scaler = MinMaxScaler()
data_scaled = scaler.fit_transform(data)
data_inverse = scaler.inverse_transform(data_scaled)
Drawing history data
plt.plot(data_inverse[:, 0], label='Param1')
plt.plot(data_inverse[:, 1], label='Param2')
plt.plot(data_inverse[:, 2], label='Param3')
plt.plot(data_inverse[:, 3], label='Predicted')
plt.legend()
plt.show()
Optimization algorithm
The algorithm may be optimized using the code in the eighth flow, such as using a more complex neural network structure, adjusting the learning rate, etc. After optimization, training and testing are needed again.
An optimization algorithm based on the structure of the neural network,
import tensorflow as tf
from tensorflow.keras import layers, models
# definition neural network structure
model = models.Sequential()
model.add(layers.Dense(64, input_shape=(X_train.shape[1],), activation='relu'))
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(1))
# definition loss function and optimizer
model.compile(loss='mse', optimizer='adam')
Training model #
model.fit(X_train, y_train, epochs=50, batch_size=32, validation_split=0.2)
Test on test set #
y_pred = model.predict(X_test)
# visual prediction result
plt.plot(y_test, label='Actual')
plt.plot(y_pred, label='Predicted')
plt.legend()
plt.show()
In the above code we used the kensat API of the TensorFlow to define a neural network with two hidden layers and used Mean Squared ErrorMSE as the loss function and Adam optimizer as the optimizer. We split the training set into a training set and a validation set and use both sets for training of the model during the training process. After training, we input the test set into the model to obtain the prediction result of the model, and compare it with the true value to evaluate the performance of the model. Finally, the prediction result is visualized for further analysis and fed back to the host computer, so that the control of the whole equipment is realized.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a reference structure" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. The utility model provides a water treatment facilities based on electrochemical method, includes former water pitcher body (6), former water pitcher body (6) external raw water pipeline, and former water pitcher body (6) embeds there is level sensor, its characterized in that: the external part of former water pitcher body (6) is equipped with intermediate reaction jar body (1), open aeration tank (2), secondary treatment jar (3), tail treatment jar (4), supplementary jar body (9) and primary retort (10), be equipped with between former water pitcher body (6), open aeration tank (2), secondary treatment jar (3), tail treatment jar (4), supplementary jar body (9) and primary retort (10) and communicate pipeline (7), be close to the position of former water pitcher body (6) is equipped with control host computer (8).
2. An electrochemical-based water treatment device according to claim 1, wherein: the secondary treatment tank (3), the tail treatment tank (4) and the primary reaction tank (10) are symmetrically arranged in a double-tank mode, two groups of the secondary treatment tank (3), the tail treatment tank (4) and the primary reaction tank (10) are arranged in a propping mode, the secondary treatment tank, the tail treatment tank and the primary reaction tank (10) are all connected through a communication pipeline (7), an auxiliary tank body (9) is arranged between the primary reaction tank (10), and the auxiliary tank body (9) is a middle water storage tank.
3. An electrochemical-based water treatment device according to claim 1, wherein: an intermediate control pump (11) is arranged between the intermediate reaction tank body (1) and the primary reaction tank (10), a tail control pump (5) is connected to the tail end pipeline of the tail treatment tank (4), and the tail control pump (5) and the intermediate control pump (11) are electrically connected with a control host (8) mutually.
4. An electrochemical-based water treatment device according to claim 1, wherein: the device is characterized in that a liquid level sensor, a flow rate sensor, a temperature sensor, a ph meter, a dissolved oxygen sensor and a conductivity measuring instrument are arranged in the raw water tank (6), the open aeration tank (2), the secondary treatment tank (3), the tail treatment tank (4), the auxiliary tank (9) and the primary reaction tank (10), and are electrically connected with the control host (8).
5. The application flow of an electrochemical-based water treatment apparatus of claim 1, wherein: the application flow comprises the following modules:
and (3) a water inlet system: introducing water to be treated into the treatment device, and communicating the water with the treatment device through a pipeline and/or a pump;
and (3) a water outlet system: discharging the treated water from the treatment device, and communicating the treated water through a pipeline and/or a pump;
solution circulation system: circulating electrolyte from the processing device to the reservoir;
an automatic control system: realizing automatic control and monitoring of parameters including temperature, PH value, current, voltage and the like in the processing device;
a data processing system: the method comprises the steps of monitoring various parameters in a processing device in real time and collecting data, and realizing evaluation and optimization of water treatment effect through data processing and analysis;
and (3) optimizing a control system: and (3) learning and optimizing the processed data, and adjusting specific physicochemical parameters in each processing flow.
6. The application process of the water treatment device based on the electrochemical method according to claim 5, wherein: the detailed flow of the water inlet system comprises the following steps:
sp1, the water inlet receives water flow from a tap water pipeline and filters out larger impurities through a filter;
sp2, the magnetic valve controls the inflow, and monitor the inflow in real time through the water flow meter;
sp3, the PH value of the inlet water is monitored in real time by a PH sensor;
sp4, a water quality sensor for inflow water monitors indexes such as total dissolved solids, COD, BOD, TOC, ammonia nitrogen and the like of inflow water in real time;
sp5, the inlet water is sent into an electrochemical reaction tank through a pressure pump.
7. The application process of the water treatment device based on the electrochemical method according to claim 5, wherein: the detailed flow of the water outlet system comprises the following steps:
sp1, effluent flows out from the electrochemical reaction tank and is precipitated by the precipitation tank;
sp2, sediment in the sedimentation tank is discharged through a sludge pump;
sp3, regulating the flow of the effluent through a magnetic valve, and monitoring the water yield in real time through a water flowmeter;
sp4 and a water quality sensor for outlet water monitor the total dissolved solids, COD, BOD, TOC and ammonia nitrogen indexes of the outlet water in real time;
sp5, the effluent is disinfected by a UV lamp and discharged through a water outlet.
8. The application process of the water treatment device based on the electrochemical method according to claim 5, wherein: the detailed flow of the solution circulation system is as follows:
sp1, the solution flows out of the electrochemical reaction tank, and larger impurities are filtered by a filter;
sp2, controlling the flow of the solution by a magnetic valve, and monitoring the flow of the solution in real time by a water flow meter;
sp3, the PH value of the solution is monitored in real time by a PH sensor of the solution;
sp4, the total dissolved solids, COD, BOD, TOC and ammonia nitrogen indexes of the solution are monitored in real time by a solution water quality sensor;
sp5, the solution is heated by a heater and finally flows into the electrochemical reaction tank again.
9. The application process of the water treatment device based on the electrochemical method according to claim 5, wherein: the detailed flow of the automatic control system is as follows:
sp1, the system acquires real-time data of water inflow, water outflow and solution through a sensor;
sp2, the automatic control system regulates the flow of the water inlet and the solution through a PID algorithm;
sp3, the automatic control system regulates the heater through a PID algorithm;
sp4, the automatic control system adjusts the potential in the electrochemical reaction tank through a PID algorithm;
sp5, remote monitoring and control of an automatic control system.
10. The application process of the water treatment device based on the electrochemical method according to claim 5, wherein: the specific flow of the data processing system is as follows:
sp1, acquisition data: the method comprises the following steps of monitoring various parameters in a water inlet, water outlet and solution circulating system in real time through a sensor and a monitoring instrument;
sp2, stored data: storing the acquired real-time data in a database for subsequent processing and analysis;
sp3, data cleaning: cleaning and processing the acquired data to remove abnormal values and noise interference;
sp4, data analysis: analyzing and mining historical data through a data mining and analyzing technology, and finding out problems and abnormal conditions existing in the water treatment process;
sp5, predictive modeling: modeling and predicting a water treatment process by using technologies such as machine learning, a neural network and the like, and predicting possible abnormal conditions in the future;
sp6, real-time monitoring: and (5) carrying out real-time monitoring and control on the water treatment process according to the analysis and prediction results.
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