KR20130118682A

KR20130118682A - Optimized coagulant feeding devices based on the prediction of phosphorus concentrations

Info

Publication number: KR20130118682A
Application number: KR1020120041778A
Authority: KR
Inventors: 이대성; 홍종인; 서영득
Original assignee: 경북대학교 산학협력단
Priority date: 2012-04-20
Filing date: 2012-04-20
Publication date: 2013-10-30

Abstract

PURPOSE: An optimal coagulant injecting device through the phosphorus concentration prediction is provided to have the optimal coagulant injection amount by predicting the phosphorus component concentration with using the feed-forward neural network and the moving-window neural network of the artificial neural network all together. CONSTITUTION: An optimal coagulant injecting device (100) comprises a storage (110) in which the processing water is flowed in; a soft sensor which is located in the storage; a data processing part (150) which is connected with the soft sensor; a control unit (160) which controls the operation of the pump which supplies the processing water to the storage. The data processing part collects the data which is measured from the soft sensor, predicts the phosphorus concentration after calculating; the control unit controls the amount of the coagulant which is injected to the storage according to the amount of the predicted phosphorus concentration.

Description

[0001] The present invention relates to an optimum coagulant feeding apparatus for predicting phosphorus concentration,

The present invention relates to an optimum coagulant injecting apparatus by predicting phosphorus concentration, and more particularly, to a method of predicting phosphorus concentration by predicting phosphorus concentration through a program and controlling the amount of coagulant based on the result, The present invention relates to an optimum coagulant injecting apparatus through concentration prediction.

The water management policy from 1993 to 2005 resulted in a cost of 26 trillion won in sewage facilities, accounting for 91% of the budget used during the same period. As a result, the load of organic matter pollution in the four major rivers and streams has decreased, mainly due to the BOD, but the total phosphorus (T-P) etc. have increased due to the neglect of the measures related to nutrients.

Eutrophication of rivers causes rapid fluctuation of dissolved oxygen. At daytime, the concentration of dissolved oxygen increases greatly due to the photosynthesis of the attached algae, but at night, the dissolved algae fluctuates significantly because the attached algae use oxygen in the water. This phenomenon adversely affects creatures such as fishes.

The prosperity of birds makes water treatment difficult and causes the taste and smell of the water to be eaten. In 2007, the total concentration of Soyang Dam, which is the upper stream of the Han River system, was 0.032㎎ / ℓ and the Paldang Dam was 0.056㎎ / ℓ. As a result of the eutrophication phenomenon of Korea 's rivers, it can be pointed out that it is a sewage treatment plant effluent standard which is much higher than that of foreign countries.

Usually eutrophication occurs at the same station as the lake, but the eutrophication in the river is also serious in Korea. The decrease in the flow rate of the fresh water and the increase of the residence time are also caused by the effluent from the sewage treatment plant.

The concentration of the effluent in Korea is 2 mg / ℓ, but it is significantly different from the standards of major countries such as USA, Germany, and Japan. The total concentration of 2 mg / ℓ of discharged water in Korea is 100 times that of eutrophication standard, and should be strengthened to 0.1 mg / ℓ or less in the area where eutrophication occurs.

As of the end of 2007, there are a total of 357 public sewage treatment facilities operating nationwide. There are 245 altitude treatment methods (A20, SBR, etc.) for the removal of nitrogen, 69% of the total, and the conventional method (standard activated sludge, long term aeration, etc.) for removing BOD is 112% It is a trend to switch.

The removal rates of BOD, COD, and SS (94.3%, 85.3%, and 96.0% in 2007), respectively, were higher than the average removal rate of each item in the 2006 and 2007 wastewater treatment plants in Korea TN and TP removal rates (67.6% and 75.3% as of 2007), respectively. The average discharged water quality of T-P is 1.2mg / ℓ in 2007, but it is more than 50 times higher than the eutrophication standard (2mg / ℓ). Considering that 69% of the public sewage treatment plants in Korea are equipped with advanced treatment methods, it can be inferred that the present advanced treatment facilities do not have a significant effect on the removal of T-P to prevent eutrophication.

In addition, the pollutants contained in the inflow water into the river or lake are inflowed from point sources and non-point sources, and the pollutants introduced from non-point sources are 60 to 70% Respectively. Non-point pollutants are discharged by rainfall and flow into rivers or lakes, which are more difficult to treat than point pollutants.

Most of the country employs confluent sewer pipes, and the outflow of pollutants occurs during the initial rainfall. This is called CSO (Combined Sewer Overflow), and due to the initial rainfall, the pollutants on the surface and the contaminants settled in the consolidation sewers flow out at the same time, resulting in a very high concentration and load of the pollutants.

In the case of rainfall, the amount of sewage flowing into the sewage treatment plant increases. In this case, the sewage treatment plant receives the flow corresponding to 3 times (3Q) of the design flow quantity (Q) And only 1Q is processed in biological treatment facilities.

In this case, as the flow rate to the primary settling tank increases, the efficiency of the primary settler decreases, and the high concentration of the return water fed from the sludge treatment system of the sewage treatment plant flows into the inflow sewage, There is an adverse effect that the amount of pollutants flowing into the water system such as river or lake is not reduced but rather increased.

Conventionally, a wastewater treatment type coagulation treatment apparatus has been developed. Examples thereof include a URC (Ultra Rapid Coagulation) system, an Actiflo (TM) system, an upflow system using floating media, a non- There is a high efficiency coagulation sedimentation method.

Conventional URC type wastewater treatment type flocculation treatment apparatus includes a rapid reaction tank, an aggregation tank, a slow aggregation tank, and a high speed slope plate sedimentation tank. URC type sewage treatment type flocculation treatment apparatus is characterized in that coagulant and coagulation assistant are administered and a slope plate settling tank is used.

The URC-type wastewater treatment type coagulation treatment device has an advantage that the reaction and precipitation time can be reduced 10 times or more as compared with the conventional coagulation sedimentation type device.

However, in the rapid reaction tank, the water to be treated and the flocculant are rapidly mixed and agitated by a stirrer rotating at a high speed. In the rapid reaction tank, mixing of the water to be treated and the flocculant must be performed within 1 minute. There is a problem that the mixing is not sufficiently performed.

Also, in the rapid reaction tank, the flocculation tank and the slow flocculation tank, the water to be treated and the flocculant are mixed using a stirrer, and a dead space is formed in the corners of each tank to generate a rotating current. There is a problem that the stirring and mixing efficiency is lowered.

In order to solve this problem, a method of increasing the amount of the coagulant (chemical agent) may be considered. However, in this case, the amount of sludge generated increases and the economical efficiency of the process is deteriorated. .

In addition, there has been a problem in terms of economy and maintenance depending on the characteristics of intermittently operating.

In addition, it is excellent for suspension aggregation, but there is a problem that dissolved organic substances and especially TN are inferior in the removal effect.

An object of the present invention to solve the above problems is to collect the data measured through the PH sensor, redox unit sensor, conductivity sensor and dissolved oxygen sensor in the data acquisition board and the lab and the feed-forward neural network of the artificial neural network It is to provide an optimal flocculant injection device through the phosphorus concentration prediction to predict the concentration of the phosphorus component using the moving-window neural network together.

It is also an object of the present invention to provide an optimum coagulant injection device by predicting phosphorus concentration for determining the amount of coagulant by constructing a model for predicting phosphorus concentration after storing measured data in real time.

It is another object of the present invention to provide an optimum coagulant injecting device by predicting phosphorus concentration, which effectively removes phosphorus by injecting a coagulant through predicted phosphorus concentration.

According to an aspect of the present invention, there is provided an optimum coagulant injection device for predicting phosphorous concentration, A soft sensor disposed in the storage; An information processing unit connected to the soft sensor; And a controller for controlling the operation of the pump for supplying the treatment water to the storage unit, wherein the information processor estimates the phosphorus concentration after collecting and calculating the measured data from the soft sensor, And the amount of the coagulant injected into the storage portion is controlled according to the amount of phosphorus concentration.

Preferably, the soft sensor is at least one selected from the group consisting of a PH sensor for measuring the phosphorus concentration of the treated water in the storage portion, an oxidizing exchange unit sensor, a conductivity sensor, and a dissolved oxygen sensor.

Preferably, the data collected through the information processor is collected through a data collection board and a labview, the phosphorus concentration is a feed-forward backpropagation algorithm and moving-window. window) algorithm is performed in parallel.

Preferably, the calculation of the phosphorus concentration comprises computing the total phosphorus (TP) through the input layer, the hidden layer and the output layer, and estimating the phosphorus concentration as the calculated data is accumulated and operated again .

Preferably, the reaction unit is output with a current of 4 ~ 20mA when the data measured from the PH sensor, the redox unit sensor, the conductivity sensor and the dissolved oxygen sensor is transmitted to the control unit, and the reaction unit After converting to a voltage of 1 ~ 5V through a resistor of 250Ω mounted, the analog signal is stored in the information processing unit every 5 minutes.

Preferably, the storage unit may include an oxygen sensor that receives the PH sensor, the oxidation unit unit sensor, and the conductivity sensor; An anaerobic tank containing the PH sensor and the oxidation unit sensor; An aerobic tank receiving the PH sensor, the oxidation unit unit and the dissolved oxygen sensor; And a flocculant feed tank adjacent to the aerobic tank.

Preferably, the reaction unit further includes a membrane disposed inside the oxic tank.

Preferably, the optimum flocculant injector further comprises a rotating member rotatable in the anoxic tank, the anaerobic tank, the aerobic tank, and the coagulant feed tank.

Preferably, the optimum flocculant injector further comprises a circulation pump for transferring the treated water in the aerobic tank to the anoxic tank and circulating the treated water.

As described above, the present invention collects data measured through a PH sensor, a redox unit sensor, a conductivity sensor, and a dissolved oxygen sensor in a data acquisition board and a lab, and uses a feed-forward neural network and a moving-window neural network of an artificial neural network. Used together to predict the concentration of the phosphorus component, the effect of providing an optimal flocculant injection amount occurs.

In addition, the present invention provides a model for estimating the phosphorus concentration after storing the measured data in real time, determining the amount of the flocculant, and injecting the flocculant to effectively remove the phosphorus component.

In addition, according to the present invention, the concentration of phosphorous components in the treated water can be precisely predicted, so that the operation state of the process can be grasped, and an appropriate amount of coagulant can be injected to prevent economic damages such as excessive flocculation of coagulant do.

BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 is a schematic view of an optimum coagulant injection device through phosphorus concentration prediction according to an embodiment of the present invention;
2 is a structural diagram of a moving-window neural network applied to a reaction part of an optimum flocculant injection device through phosphorus concentration prediction according to an embodiment of the present invention, and
Figure 3 is a comparison showing the learning and prediction of the TP applied to the moving-window neural network model of the optimal flocculant injection device through the phosphorus concentration prediction according to an embodiment of the present invention.

The components constituting the optimum flocculant injecting apparatus through the phosphorus concentration prediction according to the present invention can be used integrally or individually. In addition, some components may be omitted depending on the usage form.

A preferred embodiment of an optimum flocculant injecting apparatus 100 through phosphorus concentration prediction according to the present invention will be described with reference to Figs. In this process, the thicknesses of the lines and the sizes of the components shown in the drawings may be exaggerated for clarity and convenience of explanation. In addition, terms to be described below are terms defined in consideration of functions in the present invention, which may vary according to the intention or convention of a user or an operator. Therefore, the definitions of these terms should be described based on the contents throughout this specification.

In the present invention, the term " T-P, Total Phosphrus " means one of indicators indicating eutrophication of rivers, lakes and the like, and means the total amount of phosphorus contained in water, i.e., the total amount of phosphorus compounds.

In the present invention, the term "total nitrogen (T-N) refers to the sum of inorganic nitrogen such as ammonia nitrogen, nitrite nitrogen, and organic nitrogen such as protein, urea, and amino acid.

In the present invention, the soft sensor means any one or more of a PH sensor, an oxidation currency unit sensor, a conductivity sensor, and a dissolved oxygen sensor.

Hereinafter, an optimum coagulant injector 100 according to one embodiment of the present invention will be described with reference to FIGS. 1 to 3.

The optimum coagulant injection apparatus 100 according to an embodiment of the present invention includes a storage unit 110 into which process water flows, a PH sensor 121 disposed in the storage unit 110, an oxidizing unit sensor A pH sensor 121, and an oxidizing unit sensor (not shown) for supplying a coagulant to the storage part 110, a reaction part including the conductivity sensor 123 and the dissolved oxygen sensor 124, 122, an information processing unit 150 connected to the conductivity sensor 123 and the dissolved oxygen sensor 124, and a controller 160 for controlling the operation of the pump.

The optimal coagulant injector 100 for predicting phosphorus concentration according to an embodiment of the present invention includes a treatment water inflow conduit 101 as a passage through which the initial treatment water flows into the anoxic tank 111, A second conveyance line 103 that is a passage through which the treated water of the anaerobic tank flows into the oxic tank 113 and a treated water of the oxic tank 113 are introduced into the coagulating agent feed tank 114 A purified water discharging pipe 105 as a passage through which the treated water in the coagulant injecting tank 114 is discharged to the outside and a treated water in the flocculating water injecting tank 114 as an anoxic tank 111. The third conveying pipe 104, And a circulation duct 106 which is a passage through which the refrigerant is circulated.

1, the storage unit 110 includes an anoxic tank 111, an anaerobic tank 112, an aerobic tank 113, and a coagulant infusion tank 114.

1, the pH sensor 121, the oxidation unit unit sensor 122, and the conductivity sensor 123 are located inside the anoxic tank 111 .

The anaerobic tank 112 is a reservoir into which the treated water flows, and is located adjacent to one side of the anoxic tank 111.

A pH sensor 121, an oxidation unit sensor 122 and a rotating member 130, which will be described later, are located in the inside of the anaerobic tank 112.

Like the anaerobic tank 112, the aerobic tank 113 is a reservoir into which the treated water flows, and is located at one side of the anaerobic tank 112.

A PH sensor 121, an oxidizing exchange unit sensor 122 and a dissolved oxygen sensor 124 and a rotating member 130, which will be described later, are located inside the oxic tank 113.

The flocculant feed tank 114 is a reservoir into which treated water flows, and is located adjacent to one side of the aerobic tank 113.

Coagulant may be injected into the coagulant injection tank 114 by a pump described below, and mixed water mixed with treated water and a coagulant may be discharged to the outside.

When the measured data from the soft sensor is transmitted to the control unit 160, the reaction unit outputs a current of 4 to 20 mA.

In addition, the voltage is converted into a voltage of 1 to 5 V through a resistance of 250? Installed in the reaction part, and the analog signal is stored in the information processing part 150 every 5 minutes.

This reaction unit includes a pH sensor 121, an oxidation unit sensor 122, a conductivity sensor 123, a dissolved oxygen sensor 124, and a membrane 125.

The pH sensor 121 is located inside the anoxic tank 111, the anaerobic tank 112 and the oxic tank 113 to measure the hydrogen ion exponent of the treated water.

The Oxidation-Reduction Potential sensor 122 is located inside the anoxic tank 111, the anaerobic tank 112, and the oxic tank 113, so that the standard of a solution containing a substance that oxidizes or reduces each other through exchange of electrons The oxidation-reduction potential and the intensity of the oxidizing power or reducing power determined by the activity (approximate concentration) of the oxidized substance and the reducing substance are measured.

The conductivity sensor 123 measures the conductivity of the treated water by being located inside the anoxic tank 111.

The dissolved oxygen sensor 124 is located inside the oxic tank 113 to measure the dissolved oxygen amount of the mixed water in the coagulant inlet tank 114.

The membrane 125 is located inside the aeration tank 113, which membrane 125 separates the mixture by selectively passing certain components.

Rotating member 130 is located in the anaerobic tank 111, anaerobic tank 112 and the aerobic tank 113 is rotatable as needed.

The pump is located adjacent to the anoxic tank 111, the anaerobic tank 112, the aerobic tank 113 and the coagulant infusion tank 114. This pump includes a treatment water infusion pump 141 for injecting the first treated water into the anoxic tank 111 A flocculation water injection pump 142 for injecting coagulation water into the flocculant feed tank 114, a feed pump 143 for feeding the treated water in the aerobic tank 113 to the flocculation water feed tank 114, And a circulation pump 144 for transferring the treated water in the anoxic tank 114 to the anoxic tank 111 for circulation.

Here, the flocculation water infusion pump 142 injects the flocculant into the flocculant infusion tank 114 by the control of the controller 160.

On the other hand, the circulation pump 144 allows the microorganisms of a certain concentration or more to stay in the anoxic tank 111 through the transportation of the microorganisms.

In addition, by supplying a carbon source in the oxygen-free tank 111 through the return of the organic material to improve the denitrification rate to facilitate the removal of nitrogen, promote the activity of the microorganism to help more effective removal of BOD.

The information processor 150 estimates phosphorus concentration after collecting and calculating data measured from the PH sensor 121, the oxidation exchange unit sensor 122, the conductivity sensor 123, and the dissolved oxygen sensor 124. The description will be described later.

The controller 160 adjusts the amount of flocculant injected into the reservoir 110 according to the predicted amount of phosphorus concentration.

Hereinafter, the moving-window neural network structure of the optimal coagulant injector 100 through phosphorus concentration prediction according to an embodiment of the present invention will be described with reference to FIG. 2.

As shown in FIG. 2, data measured from the PH sensor 121, the redox unit sensor 122, the conductivity sensor 123, and the dissolved oxygen sensor 124 are positioned below the moving-window neural network structure.

The measured data is collected through a data acquisition board and a labview, and the phosphorus concentration is calculated in parallel with a feed-forward backpropagation algorithm and a moving-window algorithm.

Also, the calculation of the phosphorus concentration computes the total phosphorus (T-P) through the input layer, the hidden layer, and the output layer, and the phosphorus concentration is estimated by accumulating the calculated data again.

More specifically, the data is the data measured every 10 minutes.

The first measured data, as shown by the C group in Figure 2, are oxygen deficiency-anoxic-cond (t-2), oxygen deficiency-pH (t-2) (T-2), Anaerobic-pH (t-2), Anaerobic-ORP (t-2), Aerobic- (t-2), aerobic-oxidation unit (Aerobic-ORP) (t-2) and aerobic-dissolved oxygen amount (aerobic-DO) (t-2) are input to the input layer.

Data of T-N (t-2) and T-P (t-2) are also input to the input layer.

Next, data is accumulated through the hidden layers 1 and 2 and the output layer.

Anoxic-cond (t-1), anoxic-pH (t-1), oxygen deficiency-oxidation unit (Anoxic-Con) (T-1), anaerobic-pH (t-1), anaerobic-oxidation unit (anaerobic-ORP) (t-1), aerobic-pH data of aerobic-oxygen exchange unit (Aerobic-ORP) (t-1) and aerobic-dissolved oxygen amount (Aerobic-DO) (t-1) are input again to the input layer and accumulated.

At this time, data of T-N (t-1) and T-P (t-1) are also input to the input layer and accumulated.

Anoxic-Con (t), Anoxic-pH (t), Anoxic-ORP (Anoxic-ORP) t), Anaerobic-pH (t), Anaerobic-ORP (t), Aerobic-pH (t), Aerobic -ORP) (t), and the aerobic-dissolved oxygen amount (Aerobic-DO) (t) are input again to the input layer.

Through the above-described process, TP (t) is finally calculated, and the phosphorus concentration is predicted according to the result, and then the amount of flocculant is adjusted and injected.

Hereinafter, with reference to Figure 3 will be described by comparing the learning and prediction of the T-P applied to the moving-window neural network model of the optimal flocculant injection device through the phosphorus concentration prediction according to an embodiment of the present invention.

Both learning and prediction use a neural network, which uses a combination of the feed-forward backpropagation algorithm and the moving-window algorithm.

Each layer is composed of an input layer, a hidden layer, and an output layer.

In the case of the neural network using the artificial neural network, prediction based on the tendency is performed based on the learning result of the initial predetermined section. Accordingly, the graph of FIG. 3 is a graph showing the results of prediction based on learning from 25 days to 45 days, which was used for learning data for 25 days.

In the graph of FIG. 3, the value represented by the dot (measured T-P) represents the concentration of phosphorus in the actual experiment, and the value represented by the line (calculated T-P) is a simulation value according to the construction of the model.

As a result, in the learning section, the exact values were obtained through the model, and it was confirmed that the experimental values (measured T-P) and the predicted values (calculated T-P) were similar in the expected interval.

That is, the present invention can optimally adjust the amount of the flocculant injected through comparison of the actually measured TP with the calculated TP, thereby achieving an economic effect.

The optimal coagulant injector 100 for predicting phosphorus concentration according to an embodiment of the present invention as described above may be configured to calculate the optimal flocculation amount of each flocculant according to the predicted phosphorus concentration, To the pump by a digital signal, and the coagulant is automatically injected from the pump.

Accordingly, it is possible to remove the coagulant injection and the phosphorus component by determining the amount of the flocculant showing the optimum removal rate based on the predicted data.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or scope of the invention as defined in the appended claims. It will be understood that the present invention can be changed.

100: Optimum Coagulant Injection System through Prediction of Phosphorus Concentration
101: treated water inflow conduit
102: first conveying duct
103: Second conveying duct
104: Third conveying duct
105: purified water outlet pipe
106: circulation duct
110: storage unit
111: Anoxic
112: anaerobic tank
113: a rooster
114: coagulant feed tank
121: PH sensor
122: Oxidation exchange unit sensor
123: Conductivity sensor
124: Dissolved oxygen sensor
125: Membrane
130: rotating member
141: Process water injection pump
142: Flocculation water injection pump
143: Feed pump
144: circulation pump
150: Information processor
160:

Claims

A storage unit into which treated water flows;
A soft sensor disposed in the storage;
An information processing unit connected to the soft sensor; And
And a control unit for controlling an operation of a pump for supplying treatment water to the storage unit,
The information processing unit estimates the phosphorus concentration after collecting and calculating the data measured from the soft sensor,
The control unit is characterized in that for adjusting the amount of flocculant injected into the storage in accordance with the expected amount of phosphorus concentration,
Optimum coagulant injector.

The method of claim 1,
Wherein the soft sensor is any one or more of a group consisting of a PH sensor for measuring the phosphorus concentration of the treated water in the storage portion, an oxidation currency unit sensor, a conductivity sensor and a dissolved oxygen sensor.
Optimum coagulant injector.

The method of claim 1,
The data collected through the information processing unit is collected through a data collection board and a labview,
The phosphorus concentration is calculated in parallel with a feed-forward backpropagation algorithm and a moving-window algorithm,
Optimum coagulant injector.

The method of claim 3, wherein
The calculation of the phosphorus concentration is that the collected data to calculate the total phosphorus (TP) through the input layer, hidden layer and output layer,
The calculated data may be accumulated and recalculated to predict the phosphorus concentration.
Optimum coagulant injector.

The method of claim 1,
Wherein the reaction unit outputs a current of 4 to 20 mA when data measured from the PH sensor, the oxidation unit unit sensor, the conductivity sensor, and the dissolved oxygen sensor are transmitted to the control unit, and
Converting the analog signal into a voltage of 1 to 5 V through a resistance of 250? Installed in the reaction unit, and then storing the analog signal in the information processing unit every 5 minutes.
Optimum coagulant injector.

The method of claim 5, wherein
Wherein,
An anoxic tank for receiving the PH sensor, the oxidation unit unit sensor and the conductivity sensor;
An anaerobic tank containing the PH sensor and the oxidation unit sensor;
An aerobic tank receiving the PH sensor, the oxidation unit unit and the dissolved oxygen sensor; And
And a flocculant feed tank adjacent to the aerobic tank.
Optimum coagulant injector.

The method according to claim 6,
The reaction unit,
Further comprising: a membrane positioned within the aerobic unit;
Optimum coagulant injector.

The method according to claim 6,
The optimum flocculant injection device comprises:
Further comprising a rotating member rotatable within the anoxic tank, the anaerobic tank, the aerobic tank, and the coagulant feed tank.
Optimum coagulant injector.

The method according to claim 6,
The optimum flocculant injection device comprises:
Further comprising a circulation pump for transferring the treated water in the aerobic tank to the anoxic tank and circulating the treated water in the aerobic tank.
Optimum coagulant injector.