CN111847617A - Dephosphorization adds medicine intelligence control system - Google Patents
Dephosphorization adds medicine intelligence control system Download PDFInfo
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- CN111847617A CN111847617A CN202010853187.5A CN202010853187A CN111847617A CN 111847617 A CN111847617 A CN 111847617A CN 202010853187 A CN202010853187 A CN 202010853187A CN 111847617 A CN111847617 A CN 111847617A
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- control system
- total phosphorus
- dosing
- flowmeter
- flow meter
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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
- C02F1/58—Treatment of water, waste water, or sewage by removing specified dissolved compounds
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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
- C02F2209/005—Processes using a programmable logic controller [PLC]
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- Hydrology & Water Resources (AREA)
- Engineering & Computer Science (AREA)
- Environmental & Geological Engineering (AREA)
- Water Supply & Treatment (AREA)
- Chemical & Material Sciences (AREA)
- Organic Chemistry (AREA)
- Removal Of Specific Substances (AREA)
Abstract
The invention discloses a dephosphorization dosing intelligent control system, which comprises a first total phosphorus on-line detector and a first flowmeter, wherein the input ends of the first total phosphorus on-line monitor and the first flowmeter extend into sewage, the output ends of the first total phosphorus on-line monitor and the first flowmeter are connected with a PLC (programmable logic controller) and an on-line learning control system, the on-line learning system comprises a hardware part and a software part, the hardware part is an ARM development board, and the input end of the AMR development board is respectively connected with the feedforward control system, a third flowmeter, a second flowmeter and a second total phosphorus on-line monitor from left to right. The system combines an online learning model based on a deep learning algorithm with a feedforward dephosphorization control system, solves the problems of nonlinearity, time variation and time lag of the traditional dephosphorization dosing control system, can well meet production requirements, has strong feasibility and practicability, and can reduce the dosing amount on the premise of stable standard reaching of effluent.
Description
Technical Field
The invention relates to the technical field of sewage treatment, in particular to a dephosphorization dosing intelligent control system.
Background
At present, effluent treated by a town sewage treatment plant reaches a first-class A discharge standard, the Total Phosphorus (TP) concentration of the effluent is less than 0.5mg/L, the effluent of biological phosphorus removal generally ranges from 0.3 to 1.5mg/L, and the effluent can not stably reach the first-class A standard, so that chemical auxiliary phosphorus removal is needed. The dosage of the phosphorus removal agent is a main influence factor of chemical phosphorus removal, and the dosage is directly related to the standard discharge and the operation cost of treated effluent.
However, the dosing and phosphorus removal of sewage plants are mostly manually adjusted and controlled for a long time, the dosing amount is determined by operators according to the quality of inlet water, the quality of outlet water and experience, and excessive phosphorus removal agents are often added to ensure that the total phosphorus of the outlet water reaches the standard, so that a large amount of agents are wasted, and the operation cost of the sewage plants is increased. Therefore, the person skilled in the art provides an intelligent control system for phosphorus removal and dosing to solve the problems mentioned in the background art.
Disclosure of Invention
The invention aims to provide an intelligent dephosphorization and dosing control system to solve the problems in the background art.
In order to achieve the purpose, the invention provides the following technical scheme:
the utility model provides a dephosphorization adds medicine intelligence control system, includes feedforward control system, feedforward control system includes that the first is total to phosphorus on-line measuring appearance and first flowmeter, the first input of total phosphorus on-line measuring appearance and first flowmeter extends to in the sewage, and the first output of total phosphorus on-line measuring appearance and first flowmeter is connected with PLC controller and online study control system.
The online learning system comprises a hardware part and a software part, wherein the hardware part is an ARM development board, the input end of the AMR development board is respectively connected with a feedforward control system, a third flow meter, a second flow meter and a second total phosphorus online monitor from left to right, and the output end of the AMR development board is connected with a PLC controller.
As a still further scheme of the invention: the software part of the online learning system is a phosphorus removal agent dosage prediction model based on a deep learning algorithm, and the dosage prediction model is written by MATLAB language.
As a still further scheme of the invention: the input ends of the second total phosphorus on-line monitor and the second flowmeter are connected to the water outlet, and the output ends of the second total phosphorus on-line monitor and the second flowmeter are connected to the ARM development board.
As a still further scheme of the invention: the output end of the medicament feeding pump is connected with a third flowmeter, and the output end of the third flowmeter is connected with a mixer.
As a still further scheme of the invention: the input end of the PLC is connected with the feedforward control system and the online learning control system, and the output end of the PLC is connected with the medicament dosing pump.
As a still further scheme of the invention: the input end of the mixer is connected with a third flow meter, and the output end of the mixer is connected with a filter tank.
Compared with the prior art, the invention has the beneficial effects that: the system combines an online learning model based on a deep learning algorithm with a feedforward dephosphorization control system, solves the problems of nonlinearity, time variation and time lag of the traditional dephosphorization dosing control system, reduces the influence of other interference factors on the system, can well meet production requirements, has stronger feasibility and practicability, and can realize the remarkable reduction of dosing amount on the premise of stable standard reaching of effluent.
Drawings
FIG. 1 is a schematic structural diagram of an intelligent control system for phosphorus removal and dosing;
FIG. 2 is a flow chart of an intelligent control system for phosphorus removal and dosing;
FIG. 3 is a fitting curve of the dosing amount for different total phosphorus concentrations of influent water configured in a laboratory in an intelligent control system for phosphorus removal and dosing;
FIG. 4 is a diagram of a deep belief network prediction model in an intelligent control system for phosphorus removal and dosing;
FIG. 5 is a graph of a depth belief network prediction model in an intelligent control system for phosphorus removal and dosing versus predicted and actual dosing values;
FIG. 6 is a fitting curve of the dosing amount of the actual data of the sewage plant in the dephosphorization dosing intelligent control system.
Detailed Description
Referring to fig. 1 to 6, in an embodiment of the present invention, an intelligent phosphorus removal and dosing control system includes a feedforward control system, and is characterized in that the feedforward control system includes a first total phosphorus on-line detector and a first flowmeter, input ends of the first total phosphorus on-line monitor and the first flowmeter extend into sewage, output ends of the first total phosphorus on-line monitor and the first flowmeter are connected to a PLC controller and an on-line learning control system, and AMR development boards in the PLC controller and the on-line learning control system are respectively used for measuring a concentration of total phosphorus in intake water and a flow rate of intake water.
The online learning system comprises a hardware part and a software part, wherein the hardware part is an ARM development board, the input end of the AMR development board is respectively connected with a feedforward control system, a third flow meter, a second flow meter and a second total phosphorus on-line monitor from left to right, the output end of the AMR development board is connected with a PLC controller, the software part of the online learning system is a phosphorus removing agent dosage prediction model based on a deep learning algorithm, the dosage prediction model is written by MATLAB language, the model collects historical data of total phosphorus (TPinf) of water inlet, total phosphorus (TPeff) of water outlet, water outlet and dosage, establishes a deep learning model of 4-input (TPinf, Qinf, TPeff, Qeff)1 output (dosage), the model utilizes historical data to conduct autonomous learning and predict the current dosing amount, and the prediction accuracy of the model is higher when the data amount is larger.
The input of the total phosphorus on-line monitoring appearance of second and second flowmeter is connected in going out water, and total phosphorus on-line monitoring appearance of second and second flowmeter output are connected in ARM development board, the input and the feedforward control system and the online learning control system of PLC controller are connected, and the output of PLC controller is connected with the medicament and throws the pump, the output that the pump was thrown to the medicament is connected with the third flowmeter, the output of third flowmeter is connected with the blender, the input of blender is connected with the third flowmeter, and the output of blender is connected with the filtering ponds, sewage flows into the blender earlier and flows into the filtering ponds again, go out water after filtering at last.
A dephosphorization adds medicine intelligence control system, includes the following step:
s1, firstly, respectively acquiring total phosphorus concentration (Tinf) and inflow flow (Qinf) of sewage by using a first total phosphorus on-line detector and a first flow meter in a feedforward control system, and transmitting the acquired data to a PLC (programmable logic controller);
s2, the PLC calculates the adding amount of the phosphorus removal agent according to the input Tinf and Qinf and the set target value of the total phosphorus concentration of the effluent, and transmits the adding amount to the variable-frequency dosing pump, and the calculation of the adding amount of the agent depends on the accuracy of the obtained mathematical formula, so that the proper mathematical model is selected to ensure the adding precision of the agent and the stability of the total phosphorus of the effluent; wherein S2 includes the steps of:
s201, collecting 30L water samples of the front end of an anoxic tank, the effluent of an aerobic tank and the effluent of a secondary sedimentation tank of a Longgui sewage plant in Guangzhou city and 1L of aluminum sulfate reagent on site, and immediately measuring the total phosphorus content as follows, wherein the effluent of the anoxic tank is: 8.53mg/L, aerobic tank effluent: 0.30 mg/L, secondary sedimentation tank effluent: 0.06 mg/L (the average value is obtained by measuring four times), and then the solution with the total phosphorus concentration of 0.3-3 is prepared by utilizing the effluent of the aerobic tank and the front end of the anoxic tank.
S202, simulating a field dosing process and a reaction process in a laboratory according to a national standard method, measuring the concentration of total phosphorus after dosing, then performing data Fitting through a 'current Fitting' tool box in MATLAB, and establishing a corresponding functional relation between the medicament and the total phosphorus content, wherein a Fitting Curve is shown in figure 3, and a formula is as follows:
y=3.613(x-obj)+1.704；
when the inflow rate is Q (m3/h), the medicine adding amount is Y (L/h), and Y = yQ/200;
wherein x is total phosphorus (mg/L) of inlet water, obj is total phosphorus (mg/L) of target outlet water, and y is the dosage (ml) added in 100ml of wastewater;
s203, the model is built by using forty groups of data of the experiment, due to the manual operation error in the experiment process, the correlation coefficient of the fitting model is 0.8629, but the model shows that the dosing is linear and is consistent with the theory of a chemical reaction formula, and the model is correct, so the model can be put into practical use.
S204, during initial dosing, the dosing amount is multiplied by a safety factor, the water outlet safety is ensured, and after a certain amount of data exist in data tested by an online total phosphorus tester at the later stage, the formula is fitted again, so that the deviation of the model can be reduced, and the model is more accurate.
S3, the medicine adding pump adds the medicine into the mixer according to the calculated medicine adding amount S2;
s4, in order to improve the accuracy of the medicament dosing amount, predicting the dosing amount by using a deep belief network model in an online learning control system, calculating the required dosing amount again and transmitting the required dosing amount to the PLC; the modeling steps of the dosage prediction model are as follows:
s401, acquiring input and output variables and 800 sets of historical data of a dosing quantity prediction model of a sewage plant from Longzhou city, wherein the input variables are total phosphorus inflow (TPinf), total phosphorus inflow (Qinf), total phosphorus outflow (TPeff) and water outflow (Qeff), the output variables are dosing quantities, and the structure of a deep belief network model is shown in FIG. 4;
s402, combining input and output variables with 800 groups of historical data to construct a Deep Belief Network (DBN) model;
s403, training the DBN model by using a Contrast Divergence (CD) algorithm to obtain a trained DBN model;
s404, 200 groups of test sets are obtained according to the ratio of the training set to the test set of the deep learning model being 8:2, the 200 groups of test sets are predicted by the trained DBN model, and a prediction result is obtained, wherein the prediction result of the deep belief network model is shown in FIG. 5.
And S5, the medicament adding pump is used for adding the medicament to the mixer for the second time according to the prediction result in the S4, and the medicament adding amount is calculated for the second time through the autonomous learning of the model in the online learning control system, so that the medicament adding amount can be determined more accurately, and the stability of the total phosphorus in the effluent is ensured.
S6, when the system runs for a period of time and has a certain amount of data, the formula is fitted again, the deviation of the model is reduced, and the model is more accurate, wherein the S6 comprises the following specific steps:
s601, deriving 20 groups of data consisting of inlet water total phosphorus, outlet water total phosphorus and dosing amount from an online learning control system, then performing data Fitting again through a 'Curve Fitting' tool box in MATLAB, reestablishing a corresponding functional relation between the medicament and the total phosphorus content, wherein a Fitting Curve at the moment is shown in FIG 6, and a Fitting formula is as follows:
y=0.0173(x-obj)+0.004571
when the inflow rate is Q (m3/h), the medicine adding amount is Y (L/h), and Y = yQ;
wherein x is total phosphorus (mg/L) of inlet water, obj is total phosphorus (mg/L) of target outlet water, and y is the dosage (L) added in the waste water of 1m 3;
s602, the correlation coefficient of the formula is 0.9382, the precision is higher than that of the first fitting formula, and the dosage can be calculated more accurately;
therefore, the system combines the on-line learning model based on the deep learning algorithm with the feedforward dephosphorization control system, solves the problems of nonlinearity, time variation and time lag of the traditional dephosphorization dosing control system, reduces the influence of other interference factors on the system, can well meet the production requirement, has stronger feasibility and practicability, and can realize the remarkable reduction of the dosing amount on the premise of stable standard reaching of effluent.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention are equivalent to or changed within the technical scope of the present invention.
Claims (7)
1. The utility model provides a dephosphorization adds medicine intelligence control system, includes feedforward control system, its characterized in that, feedforward control system includes first total phosphorus on-line measuring appearance and first flowmeter, the input of first total phosphorus on-line measuring appearance and first flowmeter extends to in the sewage, and the output of first total phosphorus on-line measuring appearance and first flowmeter is connected with PLC controller and online study control system.
2. The online learning system comprises a hardware part and a software part, wherein the hardware part is an ARM development board, the input end of the AMR development board is respectively connected with a feedforward control system, a third flow meter, a second flow meter and a second total phosphorus online monitor from left to right, and the output end of the AMR development board is connected with a PLC controller.
3. The phosphorus removal and dosing intelligent control system as claimed in claim 1, wherein the software part of the online learning system is an addition prediction model of phosphorus removal agent based on a deep learning algorithm, and the addition prediction model is written in MATLAB language.
4. The intelligent dephosphorization dosing control system according to claim 1, wherein the input ends of the second total phosphorus on-line monitor and the second flow meter are connected to the effluent, and the output ends of the second total phosphorus on-line monitor and the second flow meter are connected to the ARM development board.
5. The intelligent control system for phosphorus removal and drug addition of claim 1, wherein the output end of the drug adding pump is connected with a third flow meter, and the output end of the third flow meter is connected with a mixer.
6. The intelligent phosphorus removal and dosing control system of claim 1, wherein the input end of the PLC is connected with the feedforward control system and the online learning control system, and the output end of the PLC is connected with a medicament dosing pump.
7. The intelligent control system for phosphorus removal and dosing according to claim 1, wherein the input end of the mixer is connected with a third flow meter, and the output end of the mixer is connected with a filter tank.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202010853187.5A CN111847617A (en) | 2020-08-22 | 2020-08-22 | Dephosphorization adds medicine intelligence control system |
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| CN202010853187.5A CN111847617A (en) | 2020-08-22 | 2020-08-22 | Dephosphorization adds medicine intelligence control system |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113636723A (en) * | 2021-08-20 | 2021-11-12 | 广州市华绿环保科技有限公司 | Dephosphorization of handling domestic sewage adds medicine automated control system |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113636723A (en) * | 2021-08-20 | 2021-11-12 | 广州市华绿环保科技有限公司 | Dephosphorization of handling domestic sewage adds medicine automated control system |
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Application publication date: 20201030 |
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