JPH0531489A - Controlling device for sludge amount in activated sludge process - Google Patents

Abstract

PURPOSE:To follow a disturbance factor in a simple system with sufficient precision by calculating the target value of excess sludge amount according to fuzzy rule on the basis of suspended solid amount of activated sludge in an aeration tank, sludge volume index, the set value of retaining time of sludge and the flow rate of excess sludge. CONSTITUTION:A sludge volume index (SVI) meter 7 is provided which inputs both the activated sludge suspended solid amount MLSS of an activated sludge suspended solid meter 5 and the activated sludge sedimentation efficiency SV of a meter 6 therefor. A fuzzy controlling part 9 is constituted of both an arithmetic processor 8 which inputs SVI and the flow rate Qw of excess sludge in a flowmeter 4 therefor and performs fuzzy calculation and this SVI meter 7. The SVI meter 7 is provided in the vicinity of the outlet of an aeration tank 1. A detection signal is inputted to the arithmetic processor 8. This arithmetic processor 8 calculates the targeted sludge withdrawal amount from the SVI value and the set value (SRTset value) of the sludge retaining time. The operation time of an excess sludge pump 3 and an excess sludge flow rate controlling valve are controlled on the basis of this value.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a sludge amount control device for an activated sludge process.

[0002]

2. Description of the Related Art Generally, wastewater is decomposed aerobically (including partly anaerobic process when denitrification and dephosphorization are required) by activated sludge microorganisms. At the same time as this decomposition, the activated sludge microorganisms proliferate, so that a part of the sludge is discharged out of the treatment system as excess sludge.

To date, as a control system for excess sludge,
(1) A method to set the target amount of sludge to be drawn per day and pull out until the surplus integrated flow rate reaches the set value (quantitative pulling method), (2) Pull out a certain percentage of the amount of sludge in the aeration tank every day (sludge day (Age control, SA control), (3) withdrawing a certain proportion of the amount of sludge in the treatment system every day (average sludge retention time control, SRT control), etc. have been proposed and partially put into practical use.

[0004]

In the present excess sludge control system (quantitative extraction control, SA control, SRT control), the control may be in a divergent state when the sedimentation characteristics of sludge change. Here, the sedimentation characteristics of sludge are, for example, sludge volume index (hereinafter referred to as S).
(Abbreviated as VI) enables quantitative evaluation. Generally, when filamentous bacteria grow in a large amount, the sedimentation characteristics of sludge deteriorate and the SVI value increases. It is known that when the SVI rises, (a) the retention time of sludge in the final settling basin becomes longer, and (b) the compaction concentration of the settled sludge decreases.

When the sludge retention time in the final settling tank increases, the amount of sludge in the final settling tank increases, and as a result, the amount of sludge in the aeration tank decreases. That is, it can be said that when the SVI value fluctuates, the ratio (MF / MA ratio) between the final sludge pond sludge amount (MF) and the aeration tank sludge amount (MA) changes. Therefore, in the case of SA control in which a certain proportion of the amount of sludge in the aeration tank is withdrawn every day, the withdrawal target value changes due to SVI fluctuations, making proper control impossible. Further, as the SVI increases, the consolidation density of the sludge that has settled decreases, so that the concentration of returned sludge and excess sludge also decreases. In this case, in the quantitative withdrawal control, the net amount of the withdrawn solid matter (Kg / day) changes, and therefore the appropriate control cannot be performed as in the SA control. On the other hand, in the case of SRT control, it is difficult to put into practical use because it is difficult to accurately grasp the amount of sludge in the treatment system, especially the amount of sludge in the final settling basin.

The present invention has been made in view of the above problems, and an object thereof is to apply a fuzzy rule to a high-performance activated sludge control device capable of following a disturbance factor with sufficient accuracy by a simple system. Is to provide.

[0007]

In order to achieve the above object, the present invention provides a sludge capacity indicator for calculating a sludge capacity indicator based on the amount of activated sludge suspended matter in the aeration tank and the activated sludge sedimentation rate. A fuzzy control unit is configured by an arithmetic processing unit that calculates a target value of the excess sludge amount according to the fuzzy rule based on the sludge capacity indicator, the sludge retention time set value, and the excess sludge flow rate.

[0008]

In the present invention, SRT control is performed based on the fuzzy control rule. The output value from the SVI meter (SV meter + MLSS meter) is used as an input to this control device, and the target value of the excess sludge amount is output from this SVI value and the SRT target value according to the fuzzy control rule. The parameters included in the linear form of the consequent rule of fuzzy control are obtained by multiple regression analysis. In this case, the input data required for the multiple regression analysis can be directly measured in an actual processing facility or can be obtained by steady-state analysis using a mathematical model. When a mathematical model is used, the parameters for various plant models included in the mathematical model are optimized so that the difference between the output value by analysis and the measured value at the actual processing facility is minimized and then used.
Here, the input data to the multiple regression analysis is SRT-SVI-Qw data when the SVI value is stable.

[0009]

Embodiments of the present invention will be described below with reference to FIGS.

FIG. 1 shows a sludge amount control device for an activated sludge process according to an embodiment of the present invention. In FIG. 1, 1 is an aeration tank, 2 is a final settling tank, 3 is an excess sludge pump, and 4 is an excess sludge flow rate. It is total. 5 is an activated sludge suspension meter, 6 is an activated sludge sedimentation rate meter, 7 is an activated sludge flotation meter 5 of activated sludge suspended matter amount MLSS and activated sludge sedimentation rate 6 is an activated sludge sedimentation rate S
It is a sludge capacity indicator which calculates sludge capacity indicator (SVI) by inputting V. Reference numeral 8 is a processor for performing a fuzzy operation by inputting the sludge capacity indicator calculated by the sludge capacity indicator 7 and the surplus sludge flow rate Qw of the surplus sludge flow meter 4.
The arithmetic processor 8 and the sludge capacity indicator 7 constitute a fuzzy controller 9.

That is, in the sludge amount control device of FIG. 1, an SVI meter 7 (SV meter 5 + MLSS meter 6) is installed near the outlet of the aeration tank 1 and a detection signal is input to the arithmetic processing unit 8. The processor 8 calculates the target withdrawal sludge amount from the SVI value and the set value SRTset value of the sludge retention time SRT, and based on this value, the operating time of the excess sludge pump 3 (in the case of intermittent withdrawal) and the excess sludge flow control valve. Control (for continuous extraction).

There are various types of fuzzy control rules used in the fuzzy control section 9 and inference forms based on them, but here, the structure and the parameters are identified.

A general formula of the control rule by the fuzzy control unit 9 is shown in Formula 1.

[0014]

[Equation 1]

Where m is the number of input variables, i = 1, 2,
3, ... n: number of rules.

As an example, consider the following six control rules.

[0017]

[Equation 2]

Input (SRT set value (SRT0), SVI
(SVI0)) fitness of the antecedent part ω1, ω2, ω
3, ω4, ω5, ω6 are

[0019]

[Equation 3]

The overall inference result (output, Qw0) is a weighted average value of these.

[0021]

[Equation 4]

The parameter identification procedure is shown in FIG. The example here is the result of studying the target process by mathematical modeling and simulation. First, the relationship between the SVI and the excess sludge amount (Qw) at various SRT set values was obtained by steady-state analysis. The result is shown in FIG. From this, the relationship of Qw-SRT-SVI becomes clear.

Using this Qw-SRT-SVI relationship as input data, the parameters a11 to a63 included in the consequent part were identified by multiple regression analysis. First, the suitability ω1 to ω6 of the antecedent part is obtained using the control rule of the equation (2), and the excess sludge amount (Qw0) that is the output is calculated from the equation (4). However, here, FIG. 4 and FIG. 5 are used as the membership function of the antecedent part (thus, A11 = 3, A12 =
6, A21 = 4, A22 = 8, A23 = 12, A24 =
16, A31 = 14, A32 = 18, A41 = 50, A
42 = 300, A51 = 150, A52 = 400). In the multiple regression analysis, the consequent part parameters a11 to a11 are used so that the sum of squares of the difference between the Qw obtained by the steady analysis and the Qw (Qw0) calculated by the fuzzy rule described above is minimized.
63 and the constant term will be determined. Table 1 shows an example of the identification result.

[0024]

[Table 1]

As the performance verification by the dynamic simulation, the identified two parameters were used to perform simulations for the following two types of control systems (RUN1, RUN2) to verify the effectiveness of the fuzzy control system.

RUN1: This fuzzy control method RUN2: When the amount of surplus sludge per day is controlled to be constant. However, here, a simulation was performed in which the SVI as a disturbance factor was varied like a ramp as shown in FIG. .. The results are shown in FIGS. 7 and 8. In the case of this fuzzy control method (RUN1), by operating the excess sludge amount, the total sludge amount (M) in the process is maintained almost constant even when the SVI changes, while the excess sludge amount is kept constant. In the case of control (RUN2), the sludge distribution in the treatment system (MF / MA ratio fluctuation, etc.) changes as the SVI value rises, and the consolidation concentration and excess sludge concentration of sludge decrease, so quantitative extraction Under conditions, a substantial amount of drawn solids is reduced. As a result, as shown in FIG. 8, the sludge amount (M) in the treatment system gradually increases. That is, the SRT and F / M ratio cannot be maintained constant.

[0027]

The present invention is as described above, and fuzzy control is used for controlling excess sludge, and this fuzzy control unit is used to display the settling capacity based on the amount of suspended solids in the aeration tank and the activated sludge settling rate. It is composed of a sludge capacity indicator that calculates the target, and an arithmetic processor that calculates the target value of the excess sludge amount according to the fuzzy rule based on the sludge capacity indicator, the sludge retention time set value, and the excess sludge flow rate. .. Therefore,
The following effects can be obtained.

(A) It is possible to quickly follow changes in the SVI value and the SRT target value and control the sludge amount (M) in the treatment system and the F / M ratio corresponding to the SRT target amount. (B) Since the M and F / M ratio can be kept constant even when the SVI changes, the outflow of sludge from the final settling basin can be minimized. That is, it becomes possible to predict the amount of sludge in the final sedimentation basin due to SVI fluctuations and control the amount of excess sludge in a feedforward manner. (C) Only SVI (SV + MLSS concentration) is required for water quality measurement, and maintenance is easy. (D) By using the fuzzy rule, the control system is simplified.

[Brief description of drawings]

FIG. 1 is a block diagram of a sludge amount control device for an activated sludge process according to the present invention.

FIG. 2 is a flowchart showing a parameter identification procedure of a fuzzy control system used in the present invention.

[Fig. 3] Sludge capacity indicator SVI for surplus sludge flow rate Qw
Characteristic diagram of.

FIG. 4 is a fuzzy calculation characteristic diagram of an SRT.

FIG. 5 is a fuzzy calculation characteristic diagram of SVI.

FIG. 6 is a characteristic diagram of SVI.

FIG. 7 is a characteristic diagram of the excess sludge flow rate Qw and the total sludge amount.

FIG. 8 is a characteristic diagram of the excess sludge flow rate Qw and the total sludge amount.

[Explanation of symbols]

1 ... Aeration tank, 2 ... Final settling tank, 4 ... Excess sludge flowmeter, 7
... Sludge capacity indicator, 8 ... Arithmetic processor, 9 ... Fuzzy controller.

Claims (1)

Claims: 1. A sludge capacity indicator that calculates a sludge capacity indicator based on the amount of suspended sludge in the aeration tank and the activated sludge sedimentation rate, and this sludge capacity indicator and sludge retention time. A sludge amount control device for an activated sludge process, characterized in that a fuzzy control unit is constituted by an arithmetic processing unit that calculates a target value of the excess sludge amount according to a fuzzy rule based on a set value and an excess sludge flow rate.