CN112759063A - Pre-ozone adding control method and control system thereof - Google Patents

Pre-ozone adding control method and control system thereof Download PDF

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CN112759063A
CN112759063A CN202011582288.XA CN202011582288A CN112759063A CN 112759063 A CN112759063 A CN 112759063A CN 202011582288 A CN202011582288 A CN 202011582288A CN 112759063 A CN112759063 A CN 112759063A
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ozone
value
effluent
turbidity
adding amount
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沈恺乐
李宗强
韦文博
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Shanghai Water Engineering Design and Research Institute Co Ltd
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Shanghai Water Engineering Design and Research Institute Co Ltd
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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
    • C02F1/72—Treatment of water, waste water, or sewage by oxidation
    • C02F1/78—Treatment of water, waste water, or sewage by oxidation with ozone
    • 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
    • C02F2209/005—Processes using a programmable logic controller [PLC]
    • 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]
    • 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
    • C02F2209/11—Turbidity
    • 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
    • C02F2209/40—Liquid flow rate

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  • Life Sciences & Earth Sciences (AREA)
  • Hydrology & Water Resources (AREA)
  • Engineering & Computer Science (AREA)
  • Environmental & Geological Engineering (AREA)
  • Water Supply & Treatment (AREA)
  • Chemical & Material Sciences (AREA)
  • Organic Chemistry (AREA)
  • Treatment Of Water By Oxidation Or Reduction (AREA)

Abstract

The invention discloses a pre-ozone automatic adding control method, which comprises the following steps: water inlet flow, water inlet turbidity and water inlet COM of pre-ozone contact tank are monitored in real timeMnValue, effluent turbidity and effluent COMMnA value; the inflow water flow, the inflow water turbidity and the inflow water COM are measured through a pre-ozone adding amount prediction modelMnFitting the value to obtain a predicted value of the pre-ozone adding amount; according to the effluent turbidity and the effluent COMMnCorrecting the predicted ozone adding amount value to obtain an accurate pre-ozone adding amount value; and adding ozone into the pre-ozone contact tank according to the accurate pre-ozone adding amount value. Also discloses a control system for realizing the pre-ozone automatic adding control method. The invention improves the dosing accuracy and reduces the workload of production personnel.

Description

Pre-ozone adding control method and control system thereof
Technical Field
The invention relates to the technical field of pretreatment of water plants, in particular to a pre-ozone adding control method and a pre-ozone adding control system.
Background
Ozone can remove chromaticity and algae, improve odor and degrade macromolecular organic matters into micromolecular substances due to strong oxidizing property, and is widely applied to water treatment. At present, the pretreatment processes of a plurality of domestic water plants adopt a pre-ozone contact oxidation process, the ozone adding amount is usually determined according to the water quality detection result of a laboratory and the production experience of operators, the precision is not high, and the waste of medicaments and the increase of water production cost are caused by the excessive adding amount. Therefore, the realization of the automatic adding control of the pre-ozone process in the water plant to improve the water production efficiency is a technical problem to be solved urgently in the industry. To this end, the applicant has sought, through useful research and research, a solution to the above-mentioned problems, in the context of which the technical solutions to be described below have been made.
Disclosure of Invention
One of the objects of the present invention is: the automatic pre-ozone adding control method is provided to reduce the workload of production personnel, improve the water production efficiency and further improve the water supply safety.
The second purpose of the invention is: provides a control system for realizing the pre-ozone automatic adding control method.
The invention relates to a pre-ozone automatic adding control method, which is taken as a first aspect and comprises the following steps:
water inlet flow, water inlet turbidity and water inlet COM of pre-ozone contact tank are monitored in real timeMnValue, effluent turbidity and effluent COMMnA value;
the inflow water flow, the inflow water turbidity and the inflow water COM are measured through a pre-ozone adding amount prediction modelMnFitting the value to obtain a predicted value of the pre-ozone adding amount;
according to the effluent turbidity and the effluent COMMnCorrecting the predicted ozone adding amount value to obtain an accurate pre-ozone adding amount value;
and adding ozone into the pre-ozone contact tank according to the accurate pre-ozone adding amount value.
In a preferred embodiment of the present invention, the pre-ozone dosage prediction model is a BP neural network-based pre-ozone dosage prediction model.
As a second aspect of the present invention, a control system for implementing the above-mentioned pre-ozone automatic dosing control method includes:
the flow instrument is arranged at the water inlet end of the pre-ozone contact tank and is used for monitoring the water inlet flow of the pre-ozone contact tank in real time;
the inlet water turbidity monitor is arranged at the water inlet end of the pre-ozone contact tank and is used for monitoring the inlet water turbidity of the pre-ozone contact tank in real time;
COD monitor of intaking, the COD monitor of intaking is installed the department of intaking of ozone contact tank in advance is used for real-time supervision the influent water COM of ozone contact tank in advanceMnA value;
the effluent turbidity monitor is arranged at the effluent end of the pre-ozone contact tank and is used for monitoring the effluent turbidity of the pre-ozone contact tank in real time;
the COD monitor of the effluent is installed at the water outlet end of the pre-ozone contact tank and used for monitoring the effluent COM of the pre-ozone contact tank in real timeMnA value;
the pre-ozone dosage prediction model is respectively connected with the flowmeter, the inflow turbidity monitor and the inflow COD monitor and is used for receiving the inflow, the inflow turbidity and the inflow COM monitored in real timeMnValue and for said inlet water flow, inlet water turbidity and inlet water COMMnFitting the value to obtain a predicted value of the pre-ozone adding amount;
the PID feedback controller is respectively connected with the effluent turbidity monitor and the effluent COD monitor and is used for receiving the effluent turbidity and the effluent COM monitored in real timeMnValue according to the turbidity and COM of the effluentMnCalculating the value to obtain a corrected value of the pre-ozone adding amount;
and the PLC is respectively connected with the pre-ozone adding amount prediction model and the PID feedback controller, and is used for receiving the pre-ozone adding amount predicted value and the pre-ozone adding amount corrected value obtained by processing, calculating according to the pre-ozone adding amount predicted value and the pre-ozone adding amount corrected value to obtain a pre-ozone adding amount accurate value, and controlling the ozone adding device to add ozone into the pre-ozone contact tank according to the calculated pre-ozone adding amount accurate value.
In a preferred embodiment of the present invention, the pre-ozone dosage prediction model is a BP neural network-based pre-ozone dosage prediction model.
In a preferred embodiment of the present invention, the ozone adding apparatus comprises:
the frequency converter is connected with the PLC and used for receiving a control signal of the PLC and carrying out frequency conversion regulation according to the control signal; and
and the metering pump is connected with the frequency converter and is used for pre-adding ozone through the frequency converter.
Due to the adoption of the technical scheme, the invention has the beneficial effects that: the invention utilizes a pre-ozone adding amount prediction model to monitor the inflow water flow, the effluent turbidity and the inflow COD of the water on lineMnFitting the value to obtain the pre-ozone adding amount, and according to the effluent turbidity and the effluent CODMnThe required pre-ozone adding amount is corrected, the adding accuracy is improved, the workload of production personnel is reduced, the water supply safety is further improved, the water production cost is saved, and a new theoretical thought is provided for the automatic control of the pre-ozone process operation of a water plant.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of the structure of the pre-ozone automatic dosing control system of the present invention.
FIG. 2 is a diagram of a neural network for predicting the amount of pre-ozone added in accordance with the present invention.
FIG. 3 is a training scenario of a prediction model for pre-ozone dosing in accordance with the present invention.
Detailed Description
In order to make the technical means, the creation characteristics, the achievement purposes and the effects of the invention easy to understand, the invention is further explained below by combining the specific drawings.
The invention discloses a pre-ozone automatic adding control method, which comprises the following steps:
step S10, monitoring the inflow flow, the inflow turbidity and the inflow COM of the pre-ozone contact tank in real timeMnValue, effluent turbidity and effluent COMMnThe value is obtained.
Step S20, the inflow water flow, the inflow water turbidity and the inflow water COM are measured through a pre-ozone adding amount prediction modelMnAnd fitting the values to obtain a predicted value of the pre-ozone adding amount. In the present embodiment, the pre-ozone dosage prediction model is a BP neural network-based pre-ozone dosage prediction model.
Step S30, according to the effluent turbidity and the effluent COMMnAnd correcting the predicted ozone adding amount value to obtain an accurate pre-ozone adding amount value.
And step S40, adding ozone into the pre-ozone contact tank according to the accurate pre-ozone adding amount value.
Referring to fig. 1, the figure shows a pre-ozone automatic dosing control system, which comprises a flow meter 1, an inlet water turbidity monitor 2, an inlet water COD monitor 3, an outlet water turbidity monitor 4, an outlet water COD monitor 5, a pre-ozone dosing prediction model 6, a PID feedback controller 7 and a PLC controller 8.
The flow meter 1 is installed at the water inlet end of the pre-ozone contact tank 20 and is used for monitoring the water inlet flow of the pre-ozone contact tank 20 in real time.
The inlet water turbidity monitor 2 is installed at the inlet water end of the pre-ozone contact tank 20 and is used for monitoring the inlet water turbidity of the pre-ozone contact tank 20 in real time.
The water inlet COD monitor 3 is arranged at the water inlet end of the pre-ozone contact tank 20 and is used for monitoring the water inlet COM of the pre-ozone contact tank 20 in real timeMnThe value is obtained.
The effluent turbidity monitor 4 is installed at the effluent end of the pre-ozone contact tank 20 and is used for monitoring the effluent turbidity of the pre-ozone contact tank 20 in real time.
The effluent COD monitor 5 is arranged at the effluent end of the pre-ozone contact tank 20 and is used for monitoring in real timeWater outlet COM of pre-ozone contact tank 20MnThe value is obtained.
The pre-ozone dosage prediction model 6 is respectively connected with the flow meter 1, the intake water turbidity monitor 2 and the intake water COD monitor 3 and is used for receiving the intake water flow, intake water turbidity and intake water COM monitored in real timeMnValue, and for inlet water flow, inlet water turbidity and inlet water COMMnAnd fitting the values to obtain a predicted value of the pre-ozone adding amount. In the present embodiment, the pre-ozone dosage prediction model 6 is a BP neural network-based pre-ozone dosage prediction model.
The PID feedback controller 7 is respectively connected with the effluent turbidity monitor 4 and the effluent COD monitor 5 and is used for receiving the effluent turbidity and the effluent COM monitored in real timeMnValue according to the turbidity of the effluent and the COM of the effluentMnAnd calculating the value to obtain a corrected value of the pre-ozone adding amount. The PID feedback controller 7 compares the collected data with a reference value, and uses the difference value to calculate a new input value, so that the system can achieve the purpose of stable operation, and is a common feedback loop component in industrial control.
The PLC 8 is respectively connected with the pre-ozone adding amount prediction model 6 and the PID feedback controller 7 and is used for receiving the pre-ozone adding amount predicted value and the pre-ozone adding amount corrected value obtained through processing, calculating according to the pre-ozone adding amount predicted value and the pre-ozone adding amount corrected value to obtain a pre-ozone adding amount accurate value, and controlling the ozone adding device to add ozone into the pre-ozone contact tank 20 according to the calculated pre-ozone adding amount accurate value through the PLC 8.
The ozone adding device comprises a frequency converter 9 and a metering pump 10. The frequency converter 9 is connected with the PLC controller 8, and is configured to receive a control signal of the PLC controller 8 and perform frequency conversion adjustment according to the control signal. The metering pump 10 is connected with the frequency converter 9 and is used for pre-ozone feeding through the frequency converter 9.
The following is a description of a prediction model of pre-ozone dosage based on a BP neural network.
Because ozone adding is a flow with multiple interferences, nonlinearity, multivariable, time-varying and large hysteresis, establishing an accurate and reliable control model aiming at ozone adding has certain difficulty, and a conventional dosing control system has strong dependence on the model and is difficult to meet the control requirement with higher accuracy. The BP neural network is a neural network utilizing an error back propagation training algorithm, can fit any complex nonlinear relation, can establish the complex nonlinear relation among different water quality parameters by applying the BP neural network to water quality prediction, has relatively objective prediction results, and can greatly reduce workload.
In order to solve the problem that different variable magnitude levels in the same data set are not uniform, dispersion standardization processing is carried out on the data, all values are mapped to 0-1, and the influence of abnormal values on the whole sample is weakened. The dispersion normalization used the following formula:
Figure BDA0002864687330000061
in the formula: min (x) is the minimum value of the samples, max (x) is the maximum value of the samples, and y (x) is the dispersion normalized value of x.
The number of hidden layer nodes is very important to the learning effect of the neural network, and the number q of the nodes of the optimal hidden layer is calculated by referring to the following empirical formula:
Figure BDA0002864687330000062
in the formula: m is the number of input nodes, L is the number of output nodes, and C is a constant between 1 and 10.
The establishment of the prediction model of the pre-ozone dosage based on the BP neural network uses the operation production data of a certain water plant in Zhejiang province, wherein the inflow, the inflow turbidity and the inflow CODMnValue, effluent turbidity and effluent CODMnThe values are obtained through an online detector in the water inlet and outlet pipelines, 100 groups of data are selected from the actual production operation of the water plant as samples, 70 groups of data are randomly selected from the samples as training data samples, 15 groups of data are selected as verification data samples, and 15 groups of data are selected as test data samples.
After the data is subjected to dispersion standardization processing, a BP-based neural network is established through MatlabPrediction model of ozone adding amount in accordance with inflow water flow, inflow water turbidity and inflow water CODMnThe values were used as input parameters, the pre-ozone addition was used as output parameters, and the prediction model used a 3-layer BP neural network, in which the number of input layer neurons was 3, the number of cryptic layer neurons was 10, and the number of output layer neurons was 1, as shown in fig. 2.
FIG. 3 shows the training situation of the ozone dosage prediction model provided by the present invention. The correlation coefficient of the whole fitting is 0.8631, the correlation coefficient of the training set is 0.9002, and the correlation coefficient of the testing set is 0.7629, which shows that the relation between the water quality parameter of the inlet water and the ozone adding amount can be established by inputting the water quality parameter of the inlet water, so that the BP neural network model has good self-learning capability and high generalization performance.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (5)

1. A pre-ozone automatic adding control method is characterized by comprising the following steps:
water inlet flow, water inlet turbidity and water inlet COM of pre-ozone contact tank are monitored in real timeMnValue, effluent turbidity and effluent COMMnA value;
the inflow water flow, the inflow water turbidity and the inflow water COM are measured through a pre-ozone adding amount prediction modelMnFitting the value to obtain a predicted value of the pre-ozone adding amount;
according to the effluent turbidity and the effluent COMMnCorrecting the predicted ozone adding amount value to obtain an accurate pre-ozone adding amount value;
and adding ozone into the pre-ozone contact tank according to the accurate pre-ozone adding amount value.
2. The pre-ozone automatic dosing control method according to claim 1, wherein the pre-ozone dosing prediction model is a BP neural network-based pre-ozone dosing prediction model.
3. A control system for implementing the pre-ozone automatic dosing control method of claim 1 or 2, comprising:
the flow instrument is arranged at the water inlet end of the pre-ozone contact tank and is used for monitoring the water inlet flow of the pre-ozone contact tank in real time;
the inlet water turbidity monitor is arranged at the water inlet end of the pre-ozone contact tank and is used for monitoring the inlet water turbidity of the pre-ozone contact tank in real time;
COD monitor of intaking, the COD monitor of intaking is installed the department of intaking of ozone contact tank in advance is used for real-time supervision the influent water COM of ozone contact tank in advanceMnA value;
the effluent turbidity monitor is arranged at the effluent end of the pre-ozone contact tank and is used for monitoring the effluent turbidity of the pre-ozone contact tank in real time;
the COD monitor of the effluent is installed at the water outlet end of the pre-ozone contact tank and used for monitoring the effluent COM of the pre-ozone contact tank in real timeMnA value;
the pre-ozone dosage prediction model is respectively connected with the flowmeter, the inflow turbidity monitor and the inflow COD monitor and is used for receiving the inflow, the inflow turbidity and the inflow COM monitored in real timeMnValue and for said inlet water flow, inlet water turbidity and inlet water COMMnFitting the value to obtain a predicted value of the pre-ozone adding amount;
the PID feedback controller is respectively connected with the effluent turbidity monitor and the effluent COD monitor and is used for receiving the effluent turbidity and the effluent COM monitored in real timeMnAccording to said valueEffluent turbidity and effluent COMMnCalculating the value to obtain a corrected value of the pre-ozone adding amount;
and the PLC is respectively connected with the pre-ozone adding amount prediction model and the PID feedback controller, and is used for receiving the pre-ozone adding amount predicted value and the pre-ozone adding amount corrected value obtained by processing, calculating according to the pre-ozone adding amount predicted value and the pre-ozone adding amount corrected value to obtain a pre-ozone adding amount accurate value, and controlling the ozone adding device to add ozone into the pre-ozone contact tank according to the calculated pre-ozone adding amount accurate value.
4. The pre-ozone automatic dosing control system of claim 3 wherein the pre-ozone dosing prediction model is a BP neural network based pre-ozone dosing prediction model.
5. The pre-ozone automatic dosing control system of claim 3 wherein the ozone dosing means comprises:
the frequency converter is connected with the PLC and used for receiving a control signal of the PLC and carrying out frequency conversion regulation according to the control signal; and
and the metering pump is connected with the frequency converter and is used for pre-adding ozone through the frequency converter.
CN202011582288.XA 2020-12-28 2020-12-28 Pre-ozone adding control method and control system thereof Pending CN112759063A (en)

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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113759726A (en) * 2021-09-27 2021-12-07 西南石油大学 Natural gas odorization control system and control method thereof
CN114229990A (en) * 2021-12-28 2022-03-25 北京首创生态环保集团股份有限公司 Ozone adding control system and method for ozone catalytic oxidation process
CN116282250A (en) * 2023-02-23 2023-06-23 浙江数翰科技有限公司 Intelligent ozone adding method and system
CN117886432A (en) * 2024-03-14 2024-04-16 湖南大学 Open ozone on-line monitoring and intelligent throwing system based on cloud computing platform
CN117886432B (en) * 2024-03-14 2024-07-02 湖南大学 Open ozone on-line monitoring and intelligent throwing system based on cloud computing platform

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113759726A (en) * 2021-09-27 2021-12-07 西南石油大学 Natural gas odorization control system and control method thereof
CN113759726B (en) * 2021-09-27 2024-02-06 西南石油大学 Natural gas odorizing control system and control method thereof
CN114229990A (en) * 2021-12-28 2022-03-25 北京首创生态环保集团股份有限公司 Ozone adding control system and method for ozone catalytic oxidation process
CN116282250A (en) * 2023-02-23 2023-06-23 浙江数翰科技有限公司 Intelligent ozone adding method and system
CN117886432A (en) * 2024-03-14 2024-04-16 湖南大学 Open ozone on-line monitoring and intelligent throwing system based on cloud computing platform
CN117886432B (en) * 2024-03-14 2024-07-02 湖南大学 Open ozone on-line monitoring and intelligent throwing system based on cloud computing platform

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