JPH06296987A - Waste water treating system - Google Patents

Abstract

PURPOSE:To provide the waste water treating system with which a stable purification effect is automatically obtainable. CONSTITUTION:This waste water treatment control system constitutes an activated sludge treating plant 12 by which the raw water supplied to an adjusting vessel is admitted as flow-in water to an aeration tank and the water mixture mixed with activated sludge contg. aerobic microorganisms is supplied to a settling vessel and the supernatant water of the settling vessel is discharged as treated water. Manupulated variable is subjected to fuzzy inference by a measurement fuzzy control section 70 in accordance with the measurement result by sensor groups 32, 34, 36 for measuring the respective vessels and water. The manipulated variable is subjected to fuzzy inference in a function diagnosing fuzzy control section 72 in accordance with the function diagnosis by an administrator, etc. The final manipulated variable is subjected to the fuzzy inference by an overall fuzzy inference section 74 from the results of the inference to control the activated sludge treating plant 12.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a wastewater treatment control system, and more particularly to a wastewater treatment control system which can be automatically operated by an activated sludge method using measurement control and functional diagnostic control.

[0002]

2. Description of the Related Art Conventionally, sewage treatment plants and the like purify sewage and wastewater such as wastewater into fresh water. The activated sludge method is widely known as this purification method. This activated sludge method promotes purification by continuously culturing aerobic organisms such as protozoa and filamentous microorganisms (so-called activated sludge) using sewage wastewater as a culture medium.
That is, it is a method of purifying the sewage wastewater by the complicated ecological behavior of microorganisms and the like.

Since this activated sludge method utilizes the ecological behavior of microorganisms and the like, complicated and many kinds of monitoring items are generated. For this reason, conventionally, the system was operated by the experience and intuition of the administrator. According to this, the measurement control device only needs to prepare the minimum necessary device, and although the equipment cost can be reduced, it greatly contributes to the skill and ability of the administrator, and the grasping degree, judgment, and management operation Variations will occur in the content etc. In other words, the criteria used as this index are sensual ones that the manager has acquired empirically,
For example, it is an ambiguous one that is judged from color, turbidity, transparency, odor, firing, sludge properties, and the like.

There is an increasing demand for an automation technique that allows even an inexperienced manager or the like to obtain and maintain a certain cleaning action without causing such variations.

In the above activated sludge method, however, the state of the activated sludge also changes if the culture state (growth, etc.) of the microorganisms changes. Therefore, in order to continuously obtain a certain purification action, the internal sludge contained in the activated sludge is required. The purification condition of sludge including the behavior of living organisms must be controlled.

For example, a method for stably achieving discharge standards such as BOD (biochemical oxygen demand) and SS (suspended substance concentration) of treated water from measured information measured in each process such as inflow water and aeration tank ( (Measurement control), the state of influent or treated water, the state of sludge in the aeration tank or the sedimentation tank, and the state of microbial flora in the aeration tank, etc., and the information is useful for operation and maintenance. There is a method (function diagnosis).

However, the conventional automatic control mainly supports the measurement control, uses a large number of measuring devices, and obtains a target value to be set by the administrator by PID control or the like based on the measured value. To do. In this case as well, it greatly contributes to the skill level of the skilled manager, and measurement at a large number of points is required.

Therefore, since the control fluctuates according to the judgment of the manager, it is impossible to carry out the management and maintenance to obtain a constant purifying action without causing the fluctuation.

Fuzzy control is a control for eliminating ambiguity. For example, as shown in FIG. 15, it is necessary to simply judge and control each information at the same level so that a skilled manager can fuzzyly infer and control based on the measured value as a fuzzy rule. The time delay of the effect due to the reaction cannot be reflected in the system, and the processing failure such as foaming cannot be reflected in the system.

In view of the above facts, an object of the present invention is to obtain a wastewater treatment system which can automatically obtain a stable purifying action.

[0011]

In order to achieve the above object, a wastewater treatment system according to the invention of claim 1 stores an aeration tank for aerating the inflowing raw water and a mixed water discharged from the aeration tank. In a wastewater treatment system comprising a sedimentation tank for storing as water, the raw water is treated using an activated sludge method, and the supernatant liquid of the sedimentation tank is discharged as treated water, a supply means for supplying oxygen to the aeration tank, and Returning means for returning the sediment deposited in the sedimentation tank to the aeration tank, discharge means for discharging the sediment deposited in the sedimentation tank, and measurement for measuring the contamination state of the raw water, the mixed liquid and the treated water Means, and a control means for controlling the supply means, the return means and the discharge means so that treated water purified to a predetermined state based on the measurement information measured by the measuring means is obtained. It is characterized in that.

The waste water treatment system according to the second aspect of the present invention comprises an aeration tank for aerating the inflowing raw water and a settling tank for storing the mixed water flowing out of the aeration tank as storage water, and the raw water is activated. In a wastewater treatment system that treats using a sludge method and discharges the supernatant of the sedimentation tank as treated water, supply means for supplying oxygen to the aeration tank, and the precipitate deposited in the sedimentation tank is returned to the aeration tank. Return means for discharging, discharge means for discharging the precipitate settled in the settling tank, the raw water, the treated water, the functional diagnostic means for inputting functional diagnostic information representing the state of the mixed water and the stored water, A control means for controlling the supply means, the return means and the discharge means so that treated water purified to a predetermined state based on the function diagnosis information input to the function diagnosis means is obtained. It is characterized in.

The waste water treatment system according to the third aspect of the present invention comprises an aeration tank for aerating the inflowing raw water and a settling tank for storing the mixed water discharged from the aeration tank as storage water, and the raw water is activated. In a wastewater treatment system that treats using a sludge method and discharges the supernatant of the sedimentation tank as treated water, supply means for supplying oxygen to the aeration tank, and the precipitate deposited in the sedimentation tank is returned to the aeration tank. Returning means, discharging means for discharging the precipitate settled in the settling tank, measuring means for measuring the dirty state of the raw water, the mixed solution and the treated water, the raw water, the treated water, the mixed water And a function diagnostic means for inputting functional diagnostic information indicating the state of the stored water, and a predetermined state based on the measurement information measured by the measuring means and the functional diagnostic information input to the functional diagnostic means. Is characterized in that reduction is treated water is said supply means so as to obtain, and a control means for controlling the return means and the discharge means.

The invention according to claim 4 is the wastewater treatment system according to claim 1 or 3, wherein the measurement information is pH, concentration of suspended matter and BOD of the raw water and the treated water, respectively, and the mixture is obtained. It is characterized by measuring DO, suspended matter concentration and returned sludge concentration for water.

According to a fifth aspect of the present invention, in the wastewater treatment system according to the first aspect, the control means assigns a priority to the measurement information and makes a fuzzy inference from the measurement information having a high priority. , Further providing a fuzzy inference based on the obtained inference result and other measurement information.
It is characterized in that the control amounts of the returning means and the discharging means are obtained and controlled.

According to a sixth aspect of the present invention, in the wastewater treatment system according to the second aspect, the control means gives a priority order to the functional diagnosis information and fuzzy inference from the high-priority functional diagnosis information. In addition, it is characterized by further performing a fuzzy inference based on the obtained inference result and other functional diagnosis information to obtain and control the control amounts of the supply means, the return means and the discharge means.

According to a seventh aspect of the present invention, in the wastewater treatment system according to the third aspect, the control means performs fuzzy inference for each information of the measuring means and the function diagnosing means and is obtained. The present invention is characterized in that fuzzy inference is comprehensively performed based on the inference result to obtain and control the control amounts of the supply means, the return means and the discharge means.

The invention described in claim 8 is the wastewater treatment system according to any one of claims 1 to 7, further comprising an outflow means for outflowing the raw water to the aeration tank, and temporarily supplying the raw water. The control means further controls the outflow means.

The invention described in claim 9 is the wastewater treatment system according to any one of claims 1 to 8,
A scraping means for scraping the sludge settled in the sedimentation tank is provided, and the control means further controls the scraping means.

[0020]

The waste water treatment system of the present invention comprises an aeration tank for aerating the inflowing raw water and a settling tank for storing the mixed water discharged from the aeration tank as reservoir water. This raw water is treated using the activated sludge method, and the supernatant of the settling tank is discharged as treated water. Oxygen is supplied to the aeration tank by the supply means. The returning means returns the precipitate containing the aerobic organisms settling in the settling tank to the aeration tank. Excess sludge and other precipitates settled in the settling tank are discharged by the discharging means.

According to the first aspect of the present invention, the measuring means measures the contamination state of the raw water, the mixed liquid and the treated water. The control means controls the supply means, the return means, and the discharge means so that the treated water purified to a predetermined state can be obtained based on the measured measurement information. Therefore, it is possible to control so as to obtain the treated water that can be in an optimum state only by the measured dirt state. This control means
As described in claim 5, the measurement information is given a priority, and fuzzy inference is performed from the measurement information having a high priority, and
Based on the obtained inference result and other measurement information, further fuzzy inference can be performed to obtain and control the control amounts of the supplying means, the returning means and the discharging means. Therefore, it is possible to obtain the treated water that has a high degree of importance and is purified to a predetermined state based on the measurement information that is desired to be reflected preferentially in the control amount.

According to the second aspect of the present invention, the function diagnosis means inputs the function diagnosis information indicating the states of the raw water, the treated water, the mixed water and the stored water. This function diagnosis information includes a state and diagnosis obtained by visual observation by an administrator or the like, on-site measurement, and measurement (water quality analysis) of a sludge sample in a laboratory. The control means controls the supply means, the return means, and the discharge means so that the treated water purified to a predetermined state is obtained based on the input function diagnosis information. Therefore, it is possible to control so as to obtain the treated water that can be in the optimum state only by the functional diagnosis of the administrator or the like. As described in claim 6, the control means gives priority to the function diagnosis information and performs fuzzy inference from the function diagnosis information having a high priority, and uses the obtained inference result and other function diagnosis information. Further, based on the fuzzy reasoning, the control amounts of the supplying means, the returning means and the discharging means can be obtained and controlled. Therefore, similarly to the first aspect, it is possible to obtain the treated water which has a high degree of importance and which has been purified to a predetermined state based on the measurement information to be preferentially reflected in the control amount.

In the third aspect of the invention, the measuring means measures the state of contamination of the raw water, the mixed solution and the treated water, and the function diagnostic means has the function of showing the states of the raw water, the treated water, the mixed water and the stored water. Diagnostic information is entered. The control means controls the supply means, the return means and the discharge means so that treated water purified to a predetermined state is obtained based on the measurement information measured by the measurement means and the function diagnosis information input to the function diagnosis means. Control. Therefore, it is possible to perform control so as to obtain the treated water that can be in an optimum state based on the measured dirt state and the information related to the function diagnosis of the administrator or the like which is unique or mutually related. As described in claim 7, the control means makes a fuzzy inference for each piece of information of the measuring means and the function diagnosing means, and comprehensively makes a fuzzy inference based on the obtained inference result, and supplies the means and returns. The control amount of the discharging means and the discharging means can be obtained and controlled.

In the above invention, as described in claim 4, the raw water and the treated water have pH and
It is possible to use the measurement values obtained by measuring the concentration and the BOD of the suspended matter, and the measurement values obtained by measuring the DO, the concentration of the suspended matter and the concentration of the returned sludge for the mixed water.

Further, in the wastewater treatment system of the above-mentioned invention, as described in claim 8, it has an outflow means for outflowing the raw water to the aeration tank and is provided with an adjusting tank for temporarily storing the raw water. The control means can be adjusted from the raw water by further controlling the outflow means. Further, as described in claim 9, a scraping unit for scraping the sludge settled in the settling tank is provided, and the control unit further controls the scraping unit to efficiently remove the precipitate settled in the settling tank. Can be scraped. Further, since the inside of the precipitation tank is agitated by scraping the precipitate, if the scraping means is further controlled, the precipitate is scraped without changing the boundary with the supernatant of the precipitate. be able to.

[0026]

Embodiments of the present invention will now be described in detail with reference to the drawings. In this embodiment, the present invention is applied to a wastewater treatment control system using the activated sludge method.

[Configuration of Wastewater Treatment Control System 10] FIG. 1
As shown in, the wastewater treatment control system 10 of the present embodiment.
Is equipped with a conditioning tank 14, an aeration tank 16 and a sedimentation tank 18, and after raw water supplied to the conditioning tank 14 flows out to the aeration tank 16 and is mixed with activated sludge composed of aerobic (micro) organisms. The activated sludge treatment plant 12 is configured so as to be supplied to the settling tank 18 and to flow out supernatant water above the activated sludge settled in the settling tank 18 as purified treated water.

The adjusting tank 14 is provided with a pump 22 for supplying raw water to the aeration tank 16 via the communication passage 37.
The pump 22 has an inflow water amount Q flowing into the aeration tank 16.
It is for controlling s. The pump 22 has a flow meter (not shown) and a pump controller (hereinafter,
It is called Qs controller. ) 40 via controller 50
It is connected to the.

A sensor group 32 for measuring the state of raw water (hereinafter referred to as inflow water) flowing into the aeration tank 16 is provided on the aeration tank side of the communication passage 37 (near the inflow portion of the aeration tank 16). ing. The sensors included in the sensor group 32 include a densitometer for measuring the BOD concentration LS, a densitometer for measuring the suspended matter concentration SS, a water temperature sensor, a pH sensor, and the like, which are each connected to the control device 50. . The sensor group 32 only needs to be able to measure the raw water, may be provided anywhere between the adjusting tank 14 and the aeration tank 16, and may be a value measured in advance. The BOD concentration LS may be replaced with a UV value measured by a UV meter that measures the transmittance of ultraviolet rays, and the suspended matter concentration SS may be replaced with a turbidity TB simply measured by visible light. .

In the aeration tank 16, the raw water flowing from the adjusting tank 14 and the activated sludge are completely mixed. This aeration tank 16
Is equipped with a dissolved oxygen (hereinafter, DO) meter 42 for measuring the dissolved oxygen in the aeration tank 16. This DO total 42
Are connected to the control device 50. Air is blown into the aeration tank 16 by a blower 28 for controlling the amount of dissolved oxygen. The blower 28 has an air flow meter (not shown) and is connected to the control device 50. From the DO meter 42, the measured dissolved oxygen concentration is output as dissolved oxygen information MLDO.

A sensor group 34 for measuring the state of the mixed water in the aeration tank 16 is provided in the aeration tank 16. As the sensors included in the sensor group 34,
There are a water temperature sensor, a pH sensor, a densitometer, etc., which are each connected to the control device 50. The concentration measured by this densitometer is output as activated sludge concentration MLSS.

In the settling tank 18, the sludge from the aeration tank 16 is stored and the activated sludge is settled. The settling tank 18 is provided with a sludge scraper 20, and the sludge scraper 20 collects the precipitated activated sludge. The supernatant water above the activated sludge is discharged from the settling tank 18 as purified treated water. This sludge scraper 20
Is driven by a motor 30, and this motor 30 is a motor controller (hereinafter referred to as M controller) 4
4 is connected to the control device 50. Depending on the number of rotations of the motor 30, the sludge in the settling tank 18 can be agitated in addition to the action of scraping the precipitated activated sludge.

A sensor group 36 for measuring the state of treated water is provided on the outflow side of the settling tank 18.
The sensors included in the sensor group 36 include a densitometer for measuring the BOD concentration LE, a densitometer for measuring the suspended matter concentration SE, a water temperature sensor, a pH sensor, etc., each of which is connected to the control device 50. .

The aeration tank 16 and the settling tank 18 are communicated with each other through communication passages 38A, 38B and 38C. The communication passage 38B is connected to the communication passage 38C via the pump 24, and a predetermined amount of sludge in the settling tank 18 is returned to the aeration tank 16 as return sludge Qr. The pump 24 has a flow meter (not shown) and a pump controller (hereinafter, Qr).
It is called a controller. ) 46 is connected to the control device 50.

The communication passage 38A is connected to the pump 26 via the communication passage 38C. The pump 26 is for extracting a predetermined amount of activated sludge from the activated sludge in which the organisms have excessively grown in the settling tank 18 as excess sludge Qw. The pump 26 has a flow meter (not shown) and is a pump controller (hereinafter referred to as a Qw controller).
It is connected to the control device 50 via 48.

As shown in FIG. 2, the control device 50 includes a read only memory (ROM) 52, a random access memory (RAM) 54, a central processing unit (CPU) 56, an input / output port 58, and data connecting these. It is configured to include a bus 60 such as a bus and a control bus. Note that the ROM 52 stores fuzzy inference rules, control programs, and the like, which will be described later.

The input / output port 58 has a sensor group 32,
34 and 36 and the blower 28 are connected. The pump 22 is connected to the Qs controller 4 at the input / output port 58.
0, the pump 24 is connected to the Qr controller 4
6 and the pump 26 is connected to the Qw controller 4
8 and the motor 30 is connected to the M controller 44.
Connected through. A keyboard 62 and a display 64 are connected to the input / output port 58.

The control device 50 of this embodiment will be described below together with the operation of the wastewater treatment control system 10.

[Process Configuration of Fuzzy Control] First, the control concept of the wastewater treatment control system 10 of the present embodiment will be described.
Fuzzy theory (fuzzy control) is used for the control of this embodiment. As shown in FIG. 3, in the wastewater treatment control system 10, a fuzzy control unit (hereinafter referred to as a measurement fuzzy control unit) 70 that makes a fuzzy inference based on measurement information measured in real time, and a functional diagnosis of a driving operator or the like. A fuzzy control unit (hereinafter referred to as a function diagnosis fuzzy control unit) 72 that performs a fuzzy inference based on the result, and a fuzzy control unit (hereinafter referred to as a total fuzzy control unit) 74 that comprehensively performs a fuzzy inference based on the control amount of these inference results 74. And constitute a hierarchical fuzzy control unit.

The measurement information in the measurement fuzzy control unit 70 is not limited to real-time measurement, and a measurement value obtained by manual analysis may be input, or a value obtained by using a separate measuring instrument. May be input. Further, an example in which the wastewater treatment control system 10 is automatically controlled based on the obtained control amount will be described below, but the present invention is not limited to this, and the control amount that is obtained midway or finally can be set. You may make it display on the display 64 and perform control operation manually.

Further, each of the measurement fuzzy control unit 70 and the function diagnosis fuzzy control unit 72 also obtains an inference result which is hierarchically inferred as described later (see FIG. 5).

[Abbreviations of Information and Rules] Here, in the present embodiment, the control rules for operating the system are defined corresponding to the obtained information. The measurement information is described in the order of information type, information number, serial number. In the present embodiment, there are two types of information, inflow information and processing information. Inflow information corresponds to INF and processing information corresponds to EFF. For example, the first of the inflow information 1 is described as "INF11".

The control rule corresponding to the measurement information includes the type of information for defining the rule, the information number, the serial number,
Are described in this order. In the present embodiment, the control rule defined by the inflow information corresponds to IDEC, and the control rule defined by the processing information corresponds to EDEC. For example, inflow information 1
The control rule defined by the first is described as "IDEC11".

The operating variables in this embodiment are the amount of returned sludge Qr, the amount of drawn sludge Qw, and the amount of dissolved oxygen DO in the aeration tank 16.
It is limited to four types (corresponding to the air flow rate Gs) and the motor rotation speed of the scraper 20 (corresponding to the stirring speed M of the settling tank). In addition, when the adjustment tank 14 is not installed, it is necessary to consider the fluctuation of the inflow water amount, but since the system having the adjustment tank 14 is assumed in this embodiment (see FIG. 1), the aeration tank The amount Qs of inflow water to 16 was considered to be constant and was excluded from the manipulated variables.

The fluctuation width ΔQr of the returned sludge quantity Qr, the fluctuation width ΔQw of the drawn-out sludge quantity Qw, the fluctuation width ΔMLDO of the DO concentration MLDO, and the fluctuation width Δ of the stirring speed M, which are the operation variables.
The fluctuation range of each of M, NL, NM, NS, ZE, P
It is represented by seven types of language values of S, PM, and PL. NL
Represents negative and large, NM represents negative and medium, NS represents negative and small, ZE represents 0 or maintains the same state, and PS represents positive and small. PM is positive and medium, and PL is
Positive and large. For example, in the table below
When the fluctuation width ΔQr of the amount of returned sludge Qr is negatively reduced, it is expressed as Qr; NS.

In addition, operational variables other than DO (Qr, Qw,
M) has a stop function, and is given a standard such as stopping Qs (amount of water flowing into the aeration tank) when a poison enters.

The fluctuation range represented by the language value is quantified as an operation variable by the membership function shown in FIG. 9 during fuzzy inference. The operation level (control rule, details will be described later) is determined based on an event considered from a combination of various types of information described below.

[Measurement Fuzzy Control] The measurement fuzzy control unit 70 measures the inflow water (raw water) into the aeration tank 16 (hereinafter referred to as inflow information) and the measurement information of the treated water discharged from the settling tank 18 (hereinafter referred to as “inflow information”). , Processing information), and the fuzzy control is basically performed by individually setting a judgment standard.

The inflow information includes the inflow information 1 depending on the pH of the inflow water, the suspended matter concentration SS of the inflow water and the BO of the inflow water.
Feed-forward (hereinafter abbreviated as FF) for water quality water quantity fluctuation using inflow information 2 by D concentration LS
Control. Further, the treatment information includes treatment information 1 based on the pH of the treated water, suspended matter concentration SE of the treated water, and BO of the treated water.
By using the processing information 2 based on the D concentration LE, feedback control (hereinafter abbreviated as FB) is performed so that the purification processing state is stable.

FIG. 4 is a block diagram showing the flow of processing based on this measurement information. Hereinafter, description will be given along this flow.

When inquiring each judgment rule, conflicting rules may be inferred. For example, even if it is determined from the inflow information 2 that the load amount is low or high, the process information 2 may determine that it is in a normal state. In this case, the state of the treated water may change in the future, such as when the control rule is divided. Therefore, it is preferable to give priority to the inflow information.

(Control by inflow information 1) pH of inflow water
Is determined to be normal only in the case of “M”. Also,
“S” and “L” are determined as control rules that determine that the measurement is abnormal or the pH of the inflow water is abnormal, and that the fault handling process or the neutralization process of the adjusting tank 14 is performed. In addition, "S" represents a pH of less than 6.0, "M" represents a pH of 6.0 to 8.0, and "L" represents that the pH exceeds 8.0 (each is a crisp). Is a set).

The criteria for judging the inflow information 1 are shown in Table 1 below in association with the control rules. The correspondence between the measurement information and the control rule is set based on the knowledge of a skilled manager or expert.

[0054]

[Table 1] (Control by inflow information 2) Next, suspended matter concentrations SS and B
As a criterion for the inflow information 2 based on the OD concentration LS, an event can be assumed from the raw water turbidity SI corresponding to the suspended matter concentration SS and the raw water organic matter concentration LS corresponding to the BOD concentration LS (for example, the value of the UV concentration). Table 2 below shows the control rules corresponding to the measures to solve the above problem. In addition, the correspondence between the event assumed from the inflow information 2 and the control rule is set based on the knowledge of a skilled manager or an expert. In addition, as will be described below, the setting of a phenomenon expected from the obtained information and a control rule corresponding to the countermeasure is also performed based on the knowledge of a skilled manager or an expert.

[0055]

[Table 2] Since the control based on the inflow information is basically FF control, it is important to correspond to the control based on the processing information (treated water) described below.

(Fuzzy Inference) Next, one example of the fuzzy control inference process of this embodiment will be briefly described with reference to FIGS. 6 and 7. In this fuzzy inference, so-called if ~ the
As fuzzy rules represented by n, the measurement information is associated with the antecedent part and the control rule is associated with the consequent part. Further, for the sake of simplicity, the case where the raw water turbidity SI and the raw water organic matter concentration LS are obtained will be described only in the cases of the above INF21 and INF22. Further, FIG. 7 (1) shows an example of the membership function of the raw water turbidity SI, the raw water organic matter concentration LS, the operation variable ΔGs of the air flow rate Gs, and the operation variable ΔQw of the amount of drawn sludge Qw. The operation variables ΔQw, ΔG
Although s is shown in the same figure, different values (min, max) can be set for each.

First, the raw water turbidity SI and the raw water organic matter concentration LS are obtained (step 102), and in the next step 104, the degree of coincidence of the judgment criteria is calculated based on the obtained SI and LS. That is, in the case of INF21, the fuzzy set of SI is "S" and the fuzzy set of LS is "S" (see FIG. 7 (2)). Based on this membership function, Calculate the degree of coincidence. Also, IN
In the case of F22, the fuzzy set of SI is “M” and the fuzzy set of LS is “S” (see FIG. 7 (3)). Based on this membership function, the degree of coincidence of the judgment criterion which is the antecedent part Is calculated. Next, the logical product of the degree of coincidence in each criterion, that is, the minimum value wi of the degree of coincidence between SI and LS (i is the rule number), that is, the degree of conformity is calculated (step 10
6) Then, a set (logical product) Wi weighted by the fitness wi is obtained from a set of membership functions that quantify the control state of the consequent part (step 108). Next, the inference result (set W) is obtained from the inference result (each set Wi) calculated for each criterion by a union (step 110),
The center of gravity of this set W is obtained, and the control amount (operation variable ΔQw, ΔGs) corresponding to the obtained center of gravity value is obtained (step 11).
2, FIG. 7 (4)).

(Control by Treatment Information 1) Since the pH of the treated water, which is the treated information 1, has ambiguity, the suspended matter concentration SS (hereinafter SE) of the treated water and the organic matter concentration (hereinafter LE) of the treated water. We do not let you judge at the same level as, but set an independent judgment standard. The membership function with respect to this measured pH is shown in FIG.

In the judgment of the pH of the treated water, "S" is a decrease due to progress of nitrification reaction (nitration or nitrite) or sensor abnormality, "M" is proper, "L" is only organic matter removal progress (nitrification). The reaction does not proceed), the occurrence of algae abnormalities, jumps to the functional diagnostic system, or sensor abnormalities (see Table 3). Based on this criterion, fuzzy inference is performed in the same manner as above to obtain the operation variable. It should be noted that, in the case of ordinary treated water, the pH is rarely lower than 5.0 or higher than 9.0, so that the possibility of sensor abnormality is high.

[0060]

[Table 3] However, according to the control rule in Table 3 above, when it is assumed that the sensor is abnormal, the process moves to the troubleshooting.

(Control by Treatment Information 2) Next, the events and causes that can be assumed from the measurement information of the concentration SE that is the SS concentration of the treated water and the concentration LE that is the organic substance concentration of the treated water, and the judgment criteria of the control rules are shown in Table 4. It was shown to.

[0062]

[Table 4] Based on this criterion, fuzzy inference is performed in the same manner as above to obtain the operation variable. Since these are the basics of FB control, there are many things that are deeply related to the inflow information 2 and the processing information 1, and moreover, judgment is made on the measurement item (MLDO) of the aeration tank 16 and the function diagnosis fuzzy control (obstruction by microorganisms). It also contains the content to entrust.

In the contents to which such a large number of judgment criteria should be added, the contents related to the inflow information 2 and the processing information 1 are prioritized, and an event caused by a special microorganism is specified from the judgment criteria of the function diagnosis fuzzy control. There is a need to.

(Confirmation of control effect by measurement information of aeration tank & return) SS (suspended substance concentration), SR (returned sludge concentration or drawn sludge concentration) are the above control rules (ΔQR and ΔQ).
W) represents the state quantity as a result of execution. Therefore, it should be used as a monitoring element to confirm the control effect. Also, in order to prevent the control results from diverging, SS and SR
It is necessary to set the upper and lower limits for each value of (return or drawn sludge concentration). For example, in the standard activated sludge method, 500 mg / liter ≦ SS ≦ 3000 mg / liter, SS ≦ S
The setting of R ≦ 8000 mg / liter is general.

The dissolved oxygen information (MLDO) is a state quantity resulting from the operation of the blown air volume Gs, and may be used to confirm the control effect in the same manner as above, but the cause is searched out from many events. The value of MLDO can also be used as a judgment criterion in case of doing (control rule EDEC22, 2 in Table 4).
3, 24). In this case, it is necessary to always feed back the judgment criteria as a monitoring element. It is preferable to set the upper limit value and the lower limit value for MLDO as well. For example,
The setting of 0.5 mg / liter ≦ MLDO ≦ 5.0 mg / liter is preferable. (Proper MLDO value is 1.0-2.0 mg / liter).

[Function Diagnosis Fuzzy Control] The function diagnosis fuzzy control uses the judgment result of the operator as a basic input value,
Fuzzy control is performed by setting judgment criteria for each place such as inflow water, treated water, aeration tank, and sedimentation tank. Measurements that require this criterion can be broadly classified into visual observation, field measurement, and laboratory measurement. The relationship between the judgment place and the judgment level is shown in FIG. This judgment level may be set according to the processing time required by the system, because the time until the result is obtained increases in the order of visual observation, field measurement, and laboratory measurement. The correspondence between the judgment level and the judgment place is shown in Table 5. Fuzzy inference similar to the above is carried out for each judgment level based on the judgment standard of each, and the operation variable is obtained. Hereinafter, the judgment criteria will be described for each judgment level.

[0067]

[Table 5] (Visual Observation-Judgment Criteria Based on Level 1) Generally, a driving operator visually observes hue, odor, oil film, sludge state, suspended matter and the like. Hereinafter, each item will be described with reference numeral <>.

<Hue> The hue of the inflow water is influenced by suspended matter and the like, but can be roughly classified into white, gray white, gray black and the like.

In the case of each hue, the countermeasure is white waste water containing a large amount of inorganic substances such as white paper. Therefore, when raw water is stirred in the adjusting tank 14, stirring in the adjusting tank 14 is performed.・ Can be dealt with by stopping circulation and allowing only the supernatant to flow. If it is grey-white, it is a general wastewater, so the current operation can be continued. In the case of grey-black color, the waste water is putrefaction and the adjusting tank 14 is in an anaerobic state. Therefore, it can be dealt with by strengthening the stirring and circulation. Alternatively, the adjusting tank 14 may be aerated. As an example of the above other hues,
If special wastewater such as dyeing factory inflow, stop the inflow or take measures against the source.

The hue of the aeration tank depends on the state of activated sludge.
Grey-white, grey-brown (or yellow-brown, grey-black-brown), grey-black,
It can be divided into other categories.

When dealing with each of the hues, the adjustment tank 1 is used because the activated sludge containing a large amount of inflowing inorganic substances when the color is grey-white.
If stirring and circulation are performed in 4, the stirring and circulation can be stopped and only the supernatant liquid can be allowed to flow into the aeration tank 16. If it is grey-brown, yellow-brown, or grey-black-brown, it is a general activated sludge, so the current operation may be continued. In the case of grey-black, the activated sludge is in an anaerobic state and the adjusting tank 14 is in an anaerobic state. Therefore, it can be dealt with by strengthening stirring and circulation in the aeration tank. In this case, the adjusting tank 14 may be aerated, or the MLDO of the aeration tank may be increased.

The hue of the treated water is white, tan (transparent),
It is roughly classified into white turbidity and yellowish brown turbidity.

When the hue is white or yellowish brown (transparent), the treated water is general treated water, and the current treatment may be continued. In case of cloudiness, high load,
Low loading, over-aeration, low MLDO in the aeration tank, or contamination with poisons is assumed. When the load is high, it is compared with the inflow information 2, when it is low, it is compared with the inflow information 2, when it is over-aeration, it is compared with the treatment information 2, and when the MLDO of the aeration tank is low, it is treated as the treatment information 2. Deal by comparing. If poisonous substances are expected to be mixed in, check by measuring the respiratory rate (level 3, later), stop inflowing water, and add diluting water.

Since the judgment of the hue is highly subject to the driving operator, the determination of the judgment result is fuzzy inferred in this embodiment. The membership function for this hue is shown in FIG. The membership function relating to the hue of the inflow water is shown in FIG. 11 (1), and white is “WH”, yellowish white is “WY”, yellow is “YE”, brown is “YB”, and blackish brown is “BL”. It is represented by language values classified into stages.
The membership function for the hue of treated water is shown in FIG.
Shown in (2), colorless and transparent is "CL" and light yellow is "C".
Y ”, yellow“ YE ”, blackish brown“ BL ”, white turbidity“ W ”
It is represented by language values classified into 5 levels of "H". The judgment setting by the operator or the administrator is to visually check the hue of the inflowing water or the treated water and enter the selection numbers corresponding to the five kinds of membership functions of white, yellowish white, yellow, brown, and blackish brown, It is possible to input as a value with ambiguity.

<Odor> Hydrogen sulfide odor, nightmarine odor, rotten odor, humus soil odor, chemical odor, and mold odor can be roughly classified. This odor can correspond to the above hues, hydrogen sulfide odor corresponds to a grayish black hue, nightmarine odor corresponds to a high load of cloudiness, rotten odor corresponds to a grayish black hue, and chemical odor Corresponds to the hue when cloudy poison is mixed. Other than this, it can be dealt with by corresponding to a general hue.

Since the judgment of the odor is highly subject to the driving operator, in the present embodiment, the determination of the judgment result is fuzzy inferred. The membership function for this odor is shown in FIG. This odor is classified into four levels: "ZO" for not feeling odor, "SS" for feeling a little odor, "MM" for feeling odor, and "LL" for feeling strong odor. It is expressed by the language value.
The operator's or manager's judgment setting for this odor is entered as a value with ambiguity by inputting the sensory state by the odor with the selection number corresponding to the four kinds of membership functions, as in the above hue. Is possible.

<Oil Film> If the oil film is not present, normal processing is continued, and the current processing may be continued. On the other hand, if there is an oil film, it is assumed that skimming or foaming may occur. With respect to this foaming, Table 6 shows the evaluation criteria for the foaming phenomenon, taking an aeration tank as an example.

[0078]

[Table 6] <Sludge state> Table 7 shows the evaluation criteria for the deterioration of the separation ability of the individual liquid in the settling tank.

[0079]

[Table 7] In addition to the above, phenomena such as scum, floating matter, and algae generation are conceivable, and these can be processed according to the inflow information and the processing information.

By replacing the above judgment criteria of visual observation (level 1) and countermeasures (control rules) with fuzzy inference rules and performing fuzzy inference, the inference result (control amount) in visual observation can be obtained.

(Field Measurement-Level 2-Judgment Criteria)
Next, description will be made for each item to be measured by a measuring instrument or a simple measuring method at the site of the activated sludge treatment plant 12.

<Water Temperature> The water temperature of the aeration tank has a great influence on the treatment efficiency, and has a close relationship with the nitrification reaction. It often changes depending on the season, so if the water temperature drops, Q
r; PM, Qw; NM, Gs; PS.

<Transparency> The transparency of the inflow water corresponds to the information on the floating material of the level 1 because it is related to the amount of the floating material, and it is dealt with by comparing it with the inflow information 2. Similarly, the transparency of treated water is dealt with by comparing with treated information 2. However, when the transparency of the treated water is 30 cm or more, it is empirically highly likely that it is appropriate, and therefore it is determined that it is appropriate.

<PH, DO> As described above, pH corresponds to inflow information 1 and processing information 1, and dissolved oxygen DO corresponds to processing information 2. Since the redox potential ORP is deeply involved in the sludge state and the nitrification reaction, this may be dealt with.

<SV> A simple method (SV ₃₀ ) for determining the sludge interface state is to determine the separation point of a 1-liter graduated cylinder as shown in FIG. 13, and the separation is shown in Table 8 below. The classification by points is shown.

[0086]

[Table 8] The inference result (control amount) in the field measurement can be obtained by substituting the fuzzy inference rules with the above judgment criteria and measures (control rules) of the field measurement (level 2).

(Laboratory Measurement-Judgment Criteria According to Level 3-) Various analysis items of water quality and sludge are used for accurately confirming and correcting the control and functional diagnosis system according to the measurement items. In this embodiment, the tests are roughly divided into a bacterial test and various analyzes of water quality and sludge.

The presence of filamentous microorganisms and the identification of microorganisms in the bacterial test are measured by a microscope or the like, but the determination is ambiguous, and therefore the determination is made by the membership function shown in FIG. Language value 1, L, M, S, 0 at this time
Represents "only filamentous microorganisms are present" ... 1 "Abundant presence" ... L "Ordinary presence" ... M "Slightly present" ... S "Not present" ... 0 .

Further, since the countermeasures for microorganisms differ depending on the bacterial species, Table 9 below shows an example of countermeasures against microorganisms. As a result, the degree of the manipulated variable is inferred using the membership function shown in FIG. It is also possible to make a judgment based on the microflora.

[0090]

[Table 9] The inference result (control amount) in the laboratory measurement can be obtained by substituting the fuzzy inference rule with the fuzzy inference rule for the determination standard and the countermeasure (control rule) of the laboratory measurement (level 3).

In addition, examples of various analyzes of water quality and sludge will be given below as indicators of the judgment criteria. Various analyzes of water quality and sludge include analysis of carbon-based substrates that specify the pollution degree of wastewater, analysis of nitrogen-based substrates that specify the nutrients of wastewater, and analysis of phosphorus-based substrates.

The degree of pollution is specified by analyzing the amount of carbon contained in the wastewater, and the amount of carbon is analyzed according to the types of BOD, COD (chemical oxygen demand), TOC (total organic carbon), and the like.

The analysis of the nitrogenous substrate includes the analysis of total nitrogen contained in the wastewater (TN), the analysis of nitrogen contained as ammonia in the wastewater (NH ₄ -N), and the analysis of nitrogen contained as nitrite in the sludge. Analysis (NO ₂ -N) and nitrogen contained in the sludge as nitric acid (NO ₃ -N). Similarly, analysis of the phosphorus-based substrate is performed by analysis of total phosphorus contained in sludge (TP) and analysis of phosphorus contained in sludge as phosphoric acid (PO ₄ -P).

Further, it is possible to analyze SS (suspended matter concentration) and VSS which is a volatile SS component in this sludge. This VSS is an index showing sludge from which non-volatile substances such as inorganic substances contained in the sludge remaining due to intense combustion are removed.

Further, alkalinity indicating the buffer capacity of waste water, n-hexane indicating the oil content, heavy metal indicating the iron content, MBAS and phenol indicating the surfactant can be used. . It is also possible to use SVI (sludge volume index), which indicates the tightening volume in sludge per unit weight, which can be obtained from the measured value of SV ₃₀ measurement, and SS, and respiration rate, which indicates the oxygen consumption per unit time of activated sludge microorganisms. it can.

Therefore, fuzzy inference can be performed by setting control rules according to the analysis results of various analyzes of water quality and sludge.

The inference result based on the function diagnosis of each level described above is given a priority order or a combination is set based on the knowledge of a skilled manager or an expert, so that the operation variable that effectively reflects the function diagnosis information. Can be asked.

[Comprehensive Fuzzy Control] In the above, the operation variables which are unique or interrelated are obtained by the measurement fuzzy control and the function diagnostic fuzzy control. With respect to the operation variable obtained by the determination standard and the control rule, the operation variable is obtained by using the priority or the comprehensive determination standard, so that the wastewater treatment control system in which smooth operation and maintenance can be expected can be obtained.

As described above, in this embodiment, a measurement item having a high degree of influence on the activated sludge method is selected from a large number of measurement items, and fuzzy inference is performed on this measurement item, without making ambiguous control. , Operation and maintenance can be performed with quantified operational variables. Further, since the skilled knowledge of the driver can be reflected in the fuzzy inference, the obtained operation variable has a high accuracy and has an appropriate value.

Further, even in the function diagnosis based on the subjective judgment standard of the driving operator, since the fuzzy inference which controls while making the judgment standard ambiguous is used, the operator is an inexperienced driver. Even if it is operated and maintained by a highly-skilled operator, the same operation can be performed.

Further, when the operation variable is obtained according to the judgment standard, since various kinds of information are handled hierarchically, it is possible to easily reflect the information of high priority in the operation and maintenance. In this case, since the measurement information and the function diagnosis information are used independently and the information is transmitted mutually, the wastewater treatment control system using the activated sludge method can be easily automated.

In the above embodiment, the case where both the measurement fuzzy control and the function diagnosis fuzzy control are performed is explained. However, when the measurement item (measured value by the sensor) cannot be obtained, only the function diagnosis fuzzy control is performed. It suffices to drive the vehicle, and when the function diagnosis is not performed, the vehicle may be driven only by the measurement fuzzy control.

[0103]

As described above, according to the first aspect of the invention, since the supply means, the return means and the discharge means are controlled by the measurement information, the feedforward control and the feedback control can be performed immediately. The effect is that information can be reflected in a physical manner.

According to the second aspect of the present invention, since the supply means, the returning means and the discharging means are controlled by the function diagnosis based on the judgment criteria of the manager or the like, the knowledge is reflected to the expert or the expert. There is an effect that it is possible to perform the controlled.

According to the third aspect of the present invention, it is possible to perform control based on each of the measurement information obtained by measuring the state of sludge and the functional diagnosis information obtained by the functional diagnosis, and they are related to each other. It is possible to perform control that reflects finally effective information by information or original information,
There is an effect.

[Brief description of drawings]

FIG. 1 is a block diagram showing a conceptual configuration of a wastewater treatment control system for purifying sewage wastewater by an activated sludge method.

FIG. 2 is a block diagram showing a configuration of a control device.

FIG. 3 is an image diagram showing a block configuration for each function of the control device.

FIG. 4 is an image diagram showing a flow of processing of measurement fuzzy control.

FIG. 5 is an image diagram for explaining a processing route in information-based control.

FIG. 6 is a flowchart showing a flow of fuzzy inference.

FIG. 7 is an image diagram for explaining an inference process of fuzzy inference.

FIG. 8 is a diagram showing a membership function relating to pH of treated water.

FIG. 9 is a diagram showing a membership function regarding an operation variable.

FIG. 10 is an image diagram showing a schematic configuration of a judgment place and a judgment level based on functional diagnosis.

FIG. 11 shows a membership function relating to hue at the time of function diagnosis, (1) is a diagram showing a membership function of inflow water, and (2) is a diagram showing a membership function of treated water.

FIG. 12 is a diagram showing a membership function relating to odor at the time of function diagnosis.

FIG. 13 is an image diagram classifying the interface states of sludge in SV ₃₀ measurement.

FIG. 14 is a diagram showing a membership function relating to the frequency of appearance of filamentous microorganisms.

FIG. 15 is an image diagram for explaining a determination process of conventional fuzzy control.

[Explanation of symbols]

 10 Wastewater Treatment Control System 14 Adjustment Tank 16 Aeration Tank 18 Settling Tank 20 Sludge Scrubber 22 Pump (Outflow Means) 24 Pump (Return Means) 26 Pump (Discharge Means) 28 Blower (Supply Means) 32, 34, 36 Sensor Group (Measurement Means) 50 Control Device (Control Means)

 ─────────────────────────────────────────────────── ─── Continuation of the front page (72) Inventor Tatsumi Ara 4-1-1-13 Honmachi, Chuo-ku, Osaka City, Osaka Prefecture Takenaka Corporation, Osaka Main Store (72) Masanori Fujita 2nd Yamadaoka, Suita City, Osaka Prefecture No. 1 Department of Environmental Engineering, Faculty of Engineering, Osaka University (72) Keisuke Iwahori 2-1, Yamadaoka, Suita City, Osaka Prefecture Department of Environmental Engineering, Faculty of Engineering, Osaka University

Claims (9)

[Claims] 1. An aeration tank for aerating the inflowing raw water and a settling tank for storing the mixed water discharged from the aeration tank as storage water, wherein the raw water is treated using an activated sludge method, In a wastewater treatment system that discharges a supernatant liquid as treated water, a supply unit that supplies oxygen to the aeration tank, a return unit that returns a precipitate that has precipitated in the precipitation tank to the aeration tank, and a precipitate in the precipitation tank. Discharge means for discharging the precipitate, measuring means for measuring the dirty state of the raw water, the mixed solution, and the treated water, and treated water purified to a predetermined state based on the measurement information measured by the measuring means is obtained. And a control means for controlling the supply means, the return means, and the discharge means. 2. An aeration tank for aerating the inflowing raw water, and a settling tank for storing the mixed water discharged from the aeration tank as storage water, wherein the raw water is treated using an activated sludge method, In a wastewater treatment system that discharges a supernatant as treated water, a supply unit that supplies oxygen to the aeration tank, a return unit that returns a precipitate that has settled to the precipitation tank to the aeration tank, and a settling device that settles on the precipitation tank. Discharge means for discharging sediment, function diagnosis means for inputting function diagnosis information indicating the states of the raw water, the treated water, the mixed water and the stored water, and function diagnosis information input to the function diagnosis means A wastewater treatment system comprising: a control unit that controls the supply unit, the return unit, and the discharge unit so that treated water purified to a predetermined state based on the above is obtained. 3. An aeration tank for aerating the inflowing raw water, and a settling tank for storing the mixed water discharged from the aeration tank as storage water, wherein the raw water is treated using an activated sludge method, In a wastewater treatment system that discharges a supernatant liquid as treated water, a supply unit that supplies oxygen to the aeration tank, a return unit that returns a precipitate that has precipitated in the precipitation tank to the aeration tank, and a precipitate in the precipitation tank. Discharging means for discharging sediment, measuring means for measuring the dirty state of the raw water, the mixed solution and the treated water, and functional diagnostic information indicating the states of the raw water, the treated water, the mixed water and the stored water Is input so that the treated water purified to a predetermined state is obtained based on the measurement information measured by the measurement means and the function diagnosis information input to the function diagnosis means. A wastewater treatment system comprising: a supply means, a return means, and a control means for controlling the discharge means. 4. The measurement information is characterized by measuring pH, suspended matter concentration and BOD of the raw water and treated water respectively, and measuring DO, suspended matter concentration and returned sludge concentration of the mixed water. The wastewater treatment system according to Item 1 or 3. 5. The control means gives priority to the measurement information and performs fuzzy inference from the measurement information having a high priority, and further performs fuzzy inference based on the obtained inference result and other measurement information. The wastewater treatment system according to claim 1, wherein the control means obtains and controls the control amounts of the supply means, the return means, and the discharge means. 6. The control means assigns priority to the function diagnosis information and performs fuzzy inference from the function diagnosis information having a high priority, and further based on the obtained inference result and other function diagnosis information. The wastewater treatment system according to claim 2, wherein the wastewater treatment system controls the supply means, the return means and the discharge means by performing a physical reasoning. 7. The control means makes a fuzzy inference for each piece of information of the measuring means and the function diagnosing means, and comprehensively makes a fuzzy inference based on the obtained inference result, and then the supplying means and the returning means. The wastewater treatment system according to claim 3, wherein the wastewater treatment system controls by obtaining a control amount of the discharging means and the discharging means. 8. An adjusting tank is provided which has an outflow means for outflowing the raw water to the aeration tank and which temporarily stores the raw water,
The wastewater treatment system according to any one of claims 1 to 7, wherein the control means further controls the outflow means. 9. The scraping means for scraping sludge settled in the settling tank is provided, and the control means further controls the scraping means. Wastewater treatment system according to paragraph.