JPH03184101A - Controller for number of pump groups - Google Patents

Abstract

PURPOSE:To attain the control of the number of pump groups with high reliability by obtaining the evaluation value of the start/stop control of the pump groups after performing a fuzzy inference in terms of the obtained estimated value of the inflow rate and the estimated maximum and minimum water levels. CONSTITUTION:An inflow rate estimating part 11 estimates an inflow rate from the input water level and total pumping volume. A water level estimating part 12 finds the minimum and maximum water levels at a prescribed time. A pre-processing part 14 operates the input value of a fuzzy inference from the minimum and maximum vater level and the estimated value of the inflow rote. A fuzzy inferring part 13 applies a fuzzy inference to the evaluation value of the pump start/stop control based on the input value obtained at the part 14 and by reference to a fuzzy rule stored in a knowledge base 16. A start/stop deciding part 17 decides the start/stop of a pump based on the obtained fuzzy inference result. A target water level arithmetic part 18 operates the target water level in response to the start/stop level set for each pump and gives this target level to a PI control arithmetic part 19. Thus the target speed control value of the pump is operated at the part 19. Then the pump speed is controlled.

Description

DETAILED DESCRIPTION OF THE INVENTION [Object of the Invention] (Industrial Application Field) The present invention relates to a pump group number control device that optimally controls the number of operating sewage pumps for sewage water, rainwater, etc. in a sewer system.

(Conventional carcass) Sewage pumping equipment for sewage and rainwater in the sewer system consists of a pump well connected to a pipe buried underground, a suction side connected to the pump well, and a discharge side connected to the primary settling tank of the sewage treatment plant. and a group of connected pumps. and,
Sewage discharged from homes and factories and rainwater falling in urban areas flow into pump wells through pipes, and treated water stored in these pump wells is discharged to the primary settling pond by a group of pumps.

The total amount of water repelled by the pump group is adjusted step by step by changing the number of operating pumps, and continuously by speed control and discharge valve opening control.

By the way, the 19 water discharged from homes and factories changes over time depending on the activities of human groups, depending on whether it is weekly or daily.

However, whether we consider rainwater as a time-series change in the amount of rainfall by region or as a time-series change in rainwater flowing into a pump well through a pipe, both change from time to time. Poor reproducibility. For this reason, it is difficult to obtain predicted values over a period sufficient to make a daily pump operation plan in advance.

Therefore, in the conventional pump group control device, the operation is controlled based on the total amount of water repelled by the pumps, which is determined according to the water level of the pump well.

(Issue to be solved by the invention I ¥1) However, as mentioned above, with the conventional pump group control device, the water level rises quickly due to sudden changes in the inflow flow rate, such as during typhoons or torrential rains. In this case, it takes a considerable amount of time to start/stop the required number of pumps, and rapid changes in water level cannot be followed by changes in pump well water level alone, resulting in delays in pump operation. Furthermore, the pump starts/stops frequently, which shortens the life of the pump.

In order to deal with this, monitors must be stationed at the sewage treatment plant, and they must manually control the number of pumps in operation while monitoring the water level in the pump wells, which also creates problems that require manpower. there were.

This invention has been made in view of these conventional problems, and provides a pump group number control device that dynamically controls the number of operating pumps for sewage such as sewage and rainwater. The purpose is to

[Structure of the Invention] (Means for Solving the Problems) The present invention provides a number control device for a group of pumps that stores sewage water such as sewage and rainwater flowing from a pipe in a pump well and then discharges it to a sewage treatment facility using a group of pumps. Input means for sequentially inputting the water level and total amount of water repelled detected by a water level meter provided in the pump well and a flow meter provided on the discharge side of the pump group; An inflow flow rate estimating means for estimating the flow rate of sewage flowing into the well, and a maximum value of the total water repellent amount of the pump group based on the operation/stop command of the existing pump group and the pumping amount versus water level characteristic of each pump set in advance. pumping amount maximum and minimum calculation means for calculating the maximum and minimum values of the total water repellent amount, the estimated inflow flow rate value, and the current 1lFI value of the water level gauge of the pump well for a predetermined time ahead. The maximum and minimum values of the pump well water level are T-
A pump control fuzzy system that calculates an evaluation value for start/stop control of the pump group by fuzzy inference from the pump well water level prediction means for measuring and calculating, the obtained maximum predicted water level value and minimum predicted value, and the estimated inflow flow rate value. an inference means; a knowledge base storing fuzzy control rules referred to in the fuzzy inference; The apparatus includes a start/stop determination means for determining start/stop.

(Operation) In the pump group number control device of the present invention, the inflow flow estimation means estimates and calculates the inflow flow rate of sewage into the pump well based on the pump well water level from the input means and the total water repellent amount of the pump group. The pumping amount maximum and minimum calculating means calculates the maximum and minimum values of the total water repellent amount by the pumps in operation of the pump group,
Further, the water level prediction means predicts the maximum and minimum values of the water level for a predetermined period of time based on the estimated maximum and minimum values of the total amount of water repelled, the estimated inflow flow rate, and the current 7 [11Fl value of the water level meter].

The fuzzy inference means performs fuzzy inference based on the fuzzy control rules stored in the knowledge base regarding the estimated inflow flow rate and the maximum and minimum predicted water levels to determine the evaluation value for the start/stop control of the pump group. demand.

Then, the start/stop determination means determines whether to start/stop the required number of pump groups based on the evaluation value of the start/stop F control of the pump group obtained in this way, and controls the number of pump groups based on the determination. By doing this, the pump well water level can be controlled to always be kept at the optimum level.

(Example) Hereinafter, embodiments of the present invention will be explained in detail based on the drawings.

FIG. 1 shows the system configuration of an embodiment of the present invention, where 1 indicates a process and 2 indicates a device for controlling the number of pumps in a group.

First, to explain the outline of process 1, sewage such as sewage and rainwater flowing from the blue channel buried underground is transferred to the settling pond.
After passing through a screen (not shown) to remove solids, it flows into the pump well 4. The sewage that flows into this pump well 4 is sent to multiple pumps PI,...Pk
The water is pumped to the initial settling tank 6 of the sewage treatment facility by a pump group 5 consisting of.

The pump well 4 is equipped with a water level gauge 7, and the pump JL
For the water level No. 4, an upper limit value and a lower limit value are determined. Further, a flow meter 8 is provided on the discharge side of the pump group 5, so that the total amount of water repelled by the pump group 5 is successively detected. The water level detected at the water level g17 and the total amount of water repelled at the flow It=18 are taken into the pump group number control device 2.

The configuration of the pump group number control device 2 is as follows.

The pump group number control device 2 receives input signals such as the pump well water level from the process 1, the measured value of the total amount of water repelled by the pump group 5, the measured value of the number of four rotations of each operating pump, and pump start/stop commands. a process interface that performs human output control for output signals such as pump flow rate and pumping amount target value; a total water repellency amount upper/lower limit calculation unit 10 that calculates the pumping amount range width in which pump flow rate control is possible; an inflow flow rate estimating unit 11 that estimates the water level, and a water level prediction unit 111 that predicts the water level for a predetermined time ahead based on the output of the total water repellent amount upper/lower limit calculation unit 10 and the output of the inflow flow rate estimation unit 11. It is equipped with

Furthermore, the pump group number control device 2 includes a fuzzy inference unit 13 that performs fuzzy inference on evaluation values for pump start/stop control, a preprocessing unit 14 that calculates input values input to this fuzzy inference unit 13, and a control parameter a control parameter storage unit 15 that stores the information, and a knowledge base 1 that stores fuzzy rules that are referenced in the fuzzy inference unit 13.
6.

Furthermore, the pump group number control device 2 uses the pump start/stop +I: '3 output from the fuzzy inference section 13.
A start/stop determination section 17 that determines pump start/stop based on the evaluation value of 'f1F14 and outputs a pump start/stop command; and a water level target value calculation section that calculates a pump well water level target value based on the operating status of the pump group 5. 18, and P for calculating the pumped water target value so that the process follows the water level target value.
The summer control calculation unit 19 is also provided.

The control parameter storage unit 15 and the knowledge base 16 are operated by an operator 2 in order to modify and add the control parameters and fuzzy rules stored therein.
A man-machine interface 21 that interfaces with 0 and a CRT 22 are connected.

Next, the operation of the pump group number control device 2 having the above configuration will be explained.

The control parameter storage section 15 stores in advance parameters such as the number control water level level, pump characteristics, pump well structure, etc., and also inputs predicted time and other control commands. Furthermore, the knowledge base 16 includes fuzzy rules (i
f..., then...,) format.

In this state, the control of the number of pumps in the pump group and the calculation of the pumped water target value are executed as follows.

First, the inflow flow rate estimating unit 11 obtains a self-recurrence model from the input water level and total amount of water repelled.

That is, at discrete time k, if n, +1 series data Q (i) (i-1, 2, ..., k) of inflow flow rate has been manually completed, the r1 self-recursive model based on this is as follows. It is obtained by equation (1).

Q (k) -Σ x, *Q(k-i+1)-11 Q (k-1)- IH(k)-H(k-1)1 *A (H(k-1)) +Qp (k- 1)・
... (1) Here, the parameters x, (i-1, 2, . . . , m) are determined by the method of least squares. Also, the parameter Q
(k) is the inflow flow rate value detected at discrete times. Furthermore, m is an explanatory variable, and the value of this explanatory variable m is the AIC that determines how many data should be used for regression.
It is determined in advance using data related to the subject using a standard such as (Akaike Information Criterion). Further, H indicates the detected water level, A indicates the cross-sectional area of the pump well 4 when the water level is H, and Qp indicates the total amount of water repelled by the pump well 4 detected.

Then, the parameter X of the autoregressive model in equation (1) above
, the state! I! ! , treated as the quantity 'x, (k), 1
1 If we interpret that the observations include noise, then this IIF
The state transition equation and output observation equation of the J system are obtained by the following equations (2) and (3).

X(k+1)−X(k)−(2)
Q (k) = H (k) ” *X (k)
+v (k)... (3) Here, X (k) is the state vector at discrete time, and
(k)=(x(k),X2(k),−,x
m(k))''.H(k) is a known vector at discrete time, and H(i −(Q(k)), Q(
k-1) , -, Q (k-m))'', and V
(k) is a scalar and is normal white noise. That is, v
(k) is considered to follow a normal distribution with jll average O1 variance σ2.

By applying the Kalman filter theory to the autoregressive model determined in this way, an estimated value of the inflow flow rate is obtained as follows.

That is, Kalman's filter theory can be applied to the system expressed by the above equations (2) and (3), and specifically, the optimal estimated value of the state quantity can be obtained by the following calculation.

Father(k)l−Father(k−1) +G(k) *B(k
)B (k) − Q (k) − H(k) T*Father (k−1)... (4
) G (k) −P (k-1) *H(k)
*(1+Hl)T *p ()c-1)*H(k))
... (5) P (k) − fl −G (k) *H(k)” )I
*P (k-1)... (6) Here, ×(k) is the estimated value of X (k), G (k) is the Kalman gain vector, and P (k) is the covariance of the estimation error. , 1t1 The matrix divided by the noise variance σ2, ■ is! 11
It is a rank matrix.

Therefore, the estimated value Q (k) of the inflow jlQ (k)
is obtained by the following equation (7).

Q (k) −H(k) ” *Father (k) ・・
・(7) In this way, in this example, an n-self regression model is obtained, and by applying the Kalman filter theory to this zero-self regression model, the current value is calculated from the measured water level of the pump well and the total amount of water repelled by the pump. The inflow flow ff1Q flowing into the pump well is estimated.

The total water repellent amount upper/lower limit calculation unit 10 calculates the range of total lift and water amount based on the current pump operation/stop signal and from a preset pump characteristic, that is, the total water repellent amount versus water level characteristic. This is to find the minimum amount of water pumped and the maximum amount of pumped water from the current operating state of the pump, and the obtained minimum value is Qpn and the maximum value is Qpx.

Next, the water level prediction unit 12 calculates the minimum value Qpn and maximum value Qpx of the total amount of water repelled obtained as described above, the set prediction time ΔT, the current water level H(k), and the inflow flow rate. From the estimated value Q (k), the minimum value n and maximum value Rx of the water level after ΔT time are determined by the following equations (8) and (9).

1:jn−H(k)+(Q(k)−Qpx)*ΔT/A(
H(k))... (8) RX-H(k) + (Q(k)-Qpn)* ΔT/A (H(
k) )... (9) The pre-processing unit 14 calculates the minimum water level nn and maximum water level nx1 after the prediction time ΔT determined by the water level prediction unit 12 and the estimated inflow flow rate determined by the inflow flow rate estimation unit 11. From Q (k), the input value for fuzzy inference is calculated using the following equations (10) to (12).

Fin(Q) (Qpx-Qpn)/2... (12) However, here, HH: Pump start water level relative to the number of pumps in operation LL: Pump stoppage relative to the number of pumps in operation 1. , water level Fln(R
x), Fln (Rn), Fin (Q): Fuzzy input variables.

Here, the fuzzy input variables Fin(Rx), Fin(R
The meaning of n) will be explained using FIG. H(k) is currently 7
[Pump well water level at time k.

RX and 9n are pre-fl by definition? I indicates the predicted water level after the gap ΔT, and therefore 1Rx-Rnl indicates the water level control range at a time ahead by the predicted time ΔT relative to the number of pumps in operation, and if the range is around the pump starting water level HH, the pump is started. If the water level is around the pump stop level LL, it is better to stop the pump.
I can refuse. Therefore, in FIG. 2, the roughness of starting/stopping is shown as α% and β%, respectively, with 1nx-Rnl being 100%.

Furthermore, Fin(Q) is the inflow flow f with respect to the number of pumps in operation.
This shows the possibility of pumping water control in f1Q(k).

The fuzzy inference unit 13 uses the fuzzy rules stored in the knowledge base 16 from Fln (Rx), Fin (Rn), and Fin (Q) obtained in the preprocessing unit 14 to perform pump start/stop control. Perform fuzzy inference on the evaluation value of.

Input 1FIn (FTX
), Fln(Rn), and Fln(Q) are elements of a fuzzy set. For example, the input variable space of Fin(11x) is divided by the following fuzzy set.

P U M P + (Rx): 11 by RX
1 constant is pump activation.

HOLD (1”lx): The determination by RX is to maintain the pump.

Also, for example, the input variable space of pan(Rx) is divided by the following fuzzy set.

PUMP (Rn): At t and 11 constant due to 9n, the pump is stopped.

HOLD (1’(n): The determination based on 11n is to maintain the pump.

Also, for example, the input variable space of Fin(Q) is divided into the following fuzzy sets.

PUMP+(Q): The determination based on Q is to start the pump.

HOLD (Q): The determination based on Q is to maintain the pump.

PUMP (Q): When Q is set, the pump is stopped.

Then, the fuzzy inference unit 13 performs an evaluation using a fuzzy set based on the input value from the preprocessing unit 14, and divides the output variable space into the following fuzzy set, for example.

40MP+(nx, nn, Q); The i-set by RX, Rn, and Q is the pump activation.

HOLD (Rx, f’tn, Q): The i-set by RX, 1"In, and Q is pump maintenance.

PUMP (Rx, Rn, Q): Judgment based on RX, Rn, and Q indicates pump stop. It is stopped.

Each of these fuzzy sets is represented by a membership function as shown in FIG. In FIG. 3, X indicates each input variable value, and μY indicates the degree of attribute belonging to each fuzzy set.

For example, if the input variable value regarding RX is 0.0, P
UMP + (Rx) = 0, 5 =
HOL D(Rr), and if the input variable value regarding nn is 0°2, then PUMP (Rn)-1,0,HO
If LD (Rn)-0 and the input variable value regarding Q is 1.0, then 40MP+(Q)-0°5-HO
LD (Q).

The fuzzy inference method in the fuzzy inference section 13 is to perform fuzzy operations based on the membership function of the rule condition section, the membership function of the viscosity section, and the input value, based on the rules stored in the knowledge base 16. A method is used in which a fuzzy set is created, the maximum value of this synthetic fuzzy set is used as an output synthesis function, and the center of gravity of this output synthesis function is used as the output of fuzzy inference.

Simply put, for each rule, for each input value, create a fuzzy set with the minimum value of the membership function of the conditional part as the weighting coefficient, and the maximum value of the membership function of the viscosity part as the weighting coefficient. Then, iterate for all rules, the sum of the fuzzy sets obtained for each rule is the composite fuzzy set, the maximum value of the composite fuzzy set is the output composite function,
The center of gravity of this output synthesis function is calculated using the following equation (13), and this value is used as the fuzzy inference evaluation value.

PUMPIn"- (13) However, here, PUMPInf is a fuzzy inference value, X is a fuzzy output variable, and B is an output synthesis function.

Here, an example of fuzzy rules stored in the knowledge base 16 and referenced by the fuzzy inference section 13 is as follows.

if Fin (Rx) is P U M P + (Rx) and
Fln(Q) is PUMP(Q)
40MP+(Rx, Rn, Q) if Fin(Rx
) is HOLD (Rx) and Fin (qn) is HOLD (Rn) and Fin (Q) is HOLD (Q) then PUMPInfi is HOLD (RX, In, Q) if
Fin(9n) is PUMP (Rn) and Fin(Q) is PUMP (Q) then PUMPInr is PUMP (9x, inn, Q) Based on the fuzzy inference result obtained in this way, the start/stop judgment unit 17 determines whether to start or stop the pump based on the following conditional expression, and outputs the result to the process 1 side through the process interface 9.

PUMPin">on -(1
4) off ≦PUMPInr ≦ On
- (15) PUMPln "<of f -
- (1, 6) However, here: on: pump start condition boundary value off: pump stop I-condition boundary value.

In other words, if conditional expression (14) is satisfied, a pump start command is output, if conditional expression (15) is satisfied, the number of pumps is maintained at the current level, and if conditional expression (16) is satisfied, the pumps are stopped. Output the command.

In addition, in FIG. 3, on=0.5. off--0
, 5.

After obtaining the pump start/stop command in this way,
The water level target value calculation section 18 calculates a water level target value according to the start/stop levels HH and LL set for each pump, and provides it to the PI control calculation section 19 . Then, the PI control calculation section 19 performs a PI calculation on the deviation between the water level target value and the detected water level. Then, the pump speed control [1 target value is determined from the output of the PI control go calculation unit 19,
The signal is output to the process interface 9 to execute pump speed control.

In this embodiment, the amount of pumped water is controlled by variable speed control of the pump motor, but the pump is configured to have a fixed speed and the amount of pumped water is controlled by controlling the opening and closing of a valve provided on the pump discharge side. Good too.

[Effects of the Invention] As described above, according to the present invention, it is possible to control the optimal number of pumps in operation according to the inflow flow rate without an operator, and moreover, the rules regarding starting/stopping are based on ambiguous expressions that are characteristic of fuzzy reasoning. Since the pump is expressed as

Furthermore, since pump start/stop control is performed using fuzzy reasoning based on the pump well water level and pump well inflow flow rate, for example, if the pump well water level is on the rise but the pump well inflow flow is on the decline, the pump The number of pumps can be controlled accurately even when the pump does not need to be started, or when the water level in the pump well remains constant but the inflow flow rate from the pump well rises rapidly and the pump needs to be started quickly. It is possible to achieve highly reliable control of the number of pumps in a group.

[Brief explanation of drawings]

Fig. 1 is a system configuration diagram of an embodiment of the present invention, and Fig. 2 is a system configuration diagram of an embodiment of the present invention.
FIG. 3 is an explanatory diagram illustrating the stop determination operation. FIG. 3 is an explanatory diagram illustrating an example of fuzzy division of the input/output variable space using membership functions for fuzzy inference in the above embodiment. 1...Process 2...Pump group number control device 3...Sand settling basin 4...Pump well 5...
Pump group 6... Initial settling basin 7... Water level gauge
8...Flow meter 9...Process interface 10...Total water repellent amount upper/lower limit calculation section 11...Inflow flow rate estimation section 12...Water level prediction section 3.
...Fuzzy inference unit 14...Preprocessing unit 5...Control parameter storage unit 6...Knowledge base 17...Start/stop correct determination unit 8...Water level 11 Target value calculation unit 9... PI control calculation section

Claims (1)

[Scope of Claim] A pump group control device for storing sewage such as sewage and rainwater flowing from a pipe in a pump well and then discharging it to a sewage treatment facility by a pump group, comprising: a water level gauge provided in the pump well; input means for sequentially inputting the water level and total pumped water detected by a flow meter provided on the discharge side of the pump group; and an inflow means for estimating the flow rate of sewage flowing into the pump well from the input water level and total pumped water. a flow rate estimation means, and a pumped water maximum/minimum calculation means for calculating the maximum and minimum values of the total water repellent amount of the pump group from the current operation/stop command of the pump group and preset pumped water vs. water level characteristics of each pump. Predicting and calculating the maximum and minimum values of the pump well water level for a predetermined period of time from the obtained maximum and minimum values of the total pumped water amount, the estimated inflow flow rate, and the current pump well water level measurement value. pump control fuzzy inference means for calculating an evaluation value for start/stop control of the pump group by fuzzy inference from the obtained maximum and minimum predicted water level values and the estimated inflow flow rate; , a knowledge base that stores fuzzy control rules referred to in the fuzzy inference, and starting/stopping of the required number of pump groups based on the evaluation value of start/stop control of the pump group obtained by the fuzzy inference.
A device for controlling the number of pumps in a group of pumps, comprising a start/stop determination means for determining stoppage.