JPH07324598A - Tunnel ventilation control device - Google Patents

Abstract

PURPOSE:To accurately control the value of contaminant concentration into the allowable range, suppress drop frequency of the concentration value, and achieve the energy saving operation with the requisite power for ventilator reduced. CONSTITUTION:The contaminant concentration is controlled by operating a ventilator installed in a tunnel on the basis of the measurements given by a concentration meter and a wind direction/wind speed meter 4 which are installed in a tunnel, wherein an input processing means 5 computes the concentration deviation and wind speed deviation from the respective target values, and the first step fuzzy reasoning means 6 is fed with these deviations to calculate the first correction amount for the ventilator operation through a fuzzy reasoning. On the other hand, a concentration change rate calculating means is fed with the measurement of the concentration meter to compute the change rate with time of the contaminant concentration. When the second step fuzzy reasoning means 8 is fed with the first correction amount and the rate of change of the contaminant concentration and calculates the second correction amount for the ventilator operation, an output processing means 9 adds the obtained second correction amount to the ventilator operation amount and determines the control output.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a ventilation control device for a tunnel, and more particularly to a tunnel ventilation control device suitable for controlling a pollution concentration in a road tunnel within an allowable range.

[0002]

2. Description of the Related Art In a road tunnel, the exhaust gas of an automobile pollutes the inside of the tunnel. Therefore, mechanical ventilation is performed in a tunnel having a long length or a high traffic volume so that the pollution concentration is kept below an allowable value. There is.

There are various kinds of pollutants in the tunnel, but in many cases, the smoke concentration, which causes the visibility in the tunnel, is mainly controlled. The soot concentration is generally measured as a VI value (VI: Visibility Index). The VI value is a value representing the light transmittance, and the closer to 100%, the better the visibility and the lower the soot concentration. this
A device for measuring VI value is a haze transmittance meter (hereinafter referred to as VI meter).

The ventilation system of a road tunnel includes a vertical flow ventilation system and a lateral flow ventilation system, but in recent years, the vertical flow ventilation system has become the mainstream. In a vertical-flow ventilation type road tunnel, the road itself is regarded as a ventilation duct, and ventilation air is passed in the longitudinal direction of the road to dilute pollutants. In other words, it is a method of sucking in fresh air from one of the wells and discharging polluted air from the other well. A jet fan installed on the ceiling of the roadway is often used as a device for flowing the ventilation air.

As the conventional control method, feedback control based on the measured value of VI is most often used.
As a concrete method, for example, a target area of VI value is set, and several levels of management levels are provided above and below the area,
When the VI value is below the control level below the target area, the number of operating ventilators is increased, and when the VI value is above the control level above the target area, the operating number of ventilators is increased. There is a method to reduce the value of VI to keep the VI value within the target area.

Further, in the case of face-to-face traffic in a vertical flow ventilation type tunnel, since there is a vehicle traveling in the direction opposite to the ventilation direction, the wind speed in the roadway is likely to decrease, and the VI value is also delayed and decreases accordingly. Therefore, in some cases, a control method that incorporates not only the VI value but also the wind speed in the roadway as a feedback element is adopted.

[0007]

In the conventional control method,
Since we have not checked whether the VI value is decreasing or rising, there were problems as shown in (a), (b), and (c) below. (a) Since the ventilator is operated after the VI value falls below the target value, the VI value cannot be prevented from falling in time, and the VI value often falls below the allowable value. (b) When the VI value is slightly lower than the target value but tends to rise, in the conventional control, the VI value is lower than the allowable value, the number of ventilators operating and the air volume are increased, and the ventilators are operated more than necessary. was there. (c) Due to the problems of (a) and (b) above, the VI value fluctuates greatly, and it is necessary to set the VI target value higher in order to maintain the VI value above the allowable value. Power consumption increases.

The present invention has been made to solve the above problems, and controls the pollution concentration value within a permissible range with high accuracy, suppresses the frequency of decrease of the pollution concentration value, and reduces the required power of the ventilator. An object of the present invention is to provide a tunnel ventilation control device that can achieve reduced energy-saving operation.

[0009]

In order to solve the above problems, a tunnel ventilation control device according to claim 1 is based on a pollution concentration meter installed in a tunnel and a measured value of an anemometer. When operating the ventilator installed in the tunnel to control the pollution concentration, the input processing means calculates the pollution concentration deviation and the wind speed deviation with respect to the respective target values, and the first-stage fuzzy inference means inputs these deviations. While the fuzzy reasoning is used to calculate the first correction amount of the ventilation operation, the pollutant concentration change rate calculating means inputs the measured value of the pollutant concentration meter to calculate the pollutant concentration time change rate.
Therefore, when the second-stage fuzzy inference means inputs the first correction amount of the ventilator operation and the time change rate of the pollution concentration to calculate the second correction amount of the ventilator operation, the output processing means outputs the ventilator operation The control output is determined by adding the second correction amount to the operating amount.

In the tunnel ventilation control device according to the second aspect, when the input processing means calculates the pollution concentration deviation and the wind speed deviation with respect to the respective target values, the first-stage fuzzy inference means inputs these deviations to perform fuzzy inference. While calculating the first correction amount of the ventilator operation, the wind speed change rate calculation means inputs the measurement value of the wind direction anemometer to calculate the time change rate of the wind speed, and the second stage fuzzy reasoning means then operates. When the second correction amount of the ventilation operation is calculated by inputting the first correction amount of the ventilation operation and the time rate of change of the wind speed, the output processing means outputs the second correction amount to the operation amount of the ventilation operation. The control output is determined by addition.

In the tunnel ventilation control device according to the third aspect, when the input processing means calculates the pollution concentration deviation and the wind speed deviation with respect to the target value, respectively, the first-stage fuzzy inference means inputs these deviations to perform fuzzy inference. While calculating the first correction amount of the ventilator operation, the pollution concentration change rate calculation means inputs the measurement value of the pollution concentration meter to calculate the time change rate of the pollution concentration, and then the second stage fuzzy reasoning. The means inputs the first correction amount of the ventilation operation and the time change rate of the pollution concentration to calculate the second correction amount of the ventilation operation, while the wind speed change rate calculation means inputs the measurement value of the wind direction anemometer. To calculate the time change rate of the wind speed, and
When the stage fuzzy inference means inputs the second correction amount of the ventilation operation and the time change rate of the wind speed to calculate the third correction amount of the ventilation operation, the output processing means determines the operation amount of the ventilation operation by the first correction amount. The control output is determined by adding the correction amounts of three.

[0012]

In the tunnel ventilation control device according to the first aspect, in addition to the deviation of each measured value of the pollution concentration meter and the anemometer from the target value, the rate of change of the pollution concentration value with time is also input. Since the correction amount of the exhaust gas operation is determined by the fuzzy reasoning of the stages, the control can be performed by catching the decreasing tendency or the increasing tendency of the pollution concentration value.

In the tunnel ventilation control device according to the second aspect, in addition to the deviation of each measured value of the pollution concentration meter and the wind direction anemometer from the target value, the temporal change rate of the wind speed is also input, and the two-step control is performed. Since the amount of modification of the exhaust gas operation is determined by fuzzy reasoning, control can be performed by catching the tendency of the wind speed to decrease or rise.

In the tunnel ventilation control device according to the third aspect, in addition to the deviation of each measured value of the pollution concentration meter and the wind direction anemometer from the target value, there is also a time change rate of the pollution concentration and a time change rate of the wind speed. Since the correction amount of the exhaust machine operation is determined by inputting the fuzzy reasoning in three stages, the control can be performed by catching the decreasing tendency or rising tendency of the pollution concentration value and the wind speed.

[0015]

The present invention will be described in detail below with reference to the embodiments shown in the drawings. FIG. 1 is a schematic configuration diagram of an embodiment corresponding to the tunnel ventilation control device according to the first aspect. The road tunnel 1 is a two-way, vertical-flow ventilation type tunnel. Therefore, there are vehicles traveling from the A well entrance to the B well entrance and some vehicles traveling from the B well entrance to the A well entrance. As the ventilator, n jet fans 21, ..., 2n are installed, and these jet fans ventilate the inside of the tunnel. The direction of ventilation air flow is from the A well entrance to the B well entrance. In addition, as a sensor, a VI meter that measures VI value 3
1, 32 and an anemometer 4 for measuring the wind speed in a predetermined direction in the roadway are installed. In order to control the operating number of the jet fans 21, ..., 2n based on the outputs of these sensors, the input processing means 5, the first-stage fuzzy inference means 6,
VI change rate calculation means 7, second stage fuzzy inference means 8, output processing means 9 and ventilator operation means 10 are provided.

Of these, the input processing means 5 includes VI totals 31, 32.
Is input, the measured value of the wind anemometer 4 and the preset VI target value and wind speed target value are input, and the VI deviation ΔVI and the wind speed deviation ΔVr are calculated. In the first stage fuzzy reasoning means 6 receives the VI deviation ΔVI and speed deviation ΔVr calculated by the input processing unit 5 calculates a correction amount .DELTA.N _JF1 jet fan operating volume by the fuzzy inference. Further, in the VI change rate calculation means 7, the measured value of the VI meter is input and the time change rate ΔVI / Δt of the VI value is calculated. Further, the second-stage fuzzy inference means 8 inputs the correction amount ΔN _JF1 of the ventilation operation calculated by the first-stage fuzzy inference means 6 and the VI change rate ΔVI / Δt calculated by the VI change rate calculation means 7. Then, the correction amount ΔN _{JF2 of the} number of operating jet fans is calculated by fuzzy inference. In the output processing means 9, the second
The control output N _JFOUT is determined by adding the correction amount ΔN _{JF2 of the} number of operating jet fans calculated by the stage fuzzy inference means 8 to the number of operating jet fans at that time. The ventilator operating means 10 operates the jet fans 21, ..., 2n so that the number of operating jet fans is equal to the control output determined by the output processing means 9.
It outputs a stop command.

The operation of this embodiment configured as described above will be described below. The input processing means 5 includes VI totals 31 and 32.
Input the measurement value of, the measurement value of the wind anemometer 4, the VI target value and the wind speed target value, and calculate the VI deviation ΔVI from the following equation (1).
The wind speed deviation ΔVr is calculated using equation (2).

[0018] _{ΔVI 1 = min (VI 1 -VI} ref1, VI 2 -VI ref2) ... (1) ΔVr = Vr-Vr ref ... (2) where VI _1: VI total of 31 of the measured value [%] VI _ref1: VI ₁ target value [%] VI ₂ : VI total 32 measured value [%] VI _ref2 : VI ₂ target value [%] Vr: Roadway wind speed measured value [m / s] Vr _ref : Roadway wind speed target The value is [m / s]. The target values VI _ref1 and VI _ref2 of VI and the target value Vr _{ref of the} wind speed are preset values.

Next, the first-stage fuzzy inference means 6 inputs the VI deviation ΔVI and the wind speed deviation ΔVr calculated by the input processing means 5, and calculates the correction amount ΔN _{JF1 of the} number of operating jet fans by fuzzy inference. Details of the processing contents are as follows.

First, the VI deviation ΔVI and the wind speed deviation ΔVr are normalized by the following equation.

ΔVI ← ΔVI / S _VI (3) ΔVr ← ΔVr / S _Vr (4) where S _VI : Scale factor [%] of ΔVI S _Vr : Scale factor [m / s] of ΔVr.

Next, input the normalized ΔVI and ΔVr,
A fuzzy reasoning is used to obtain the correction amount ΔN _{JF1 of the} number of jet fans operating. FIG. 2 shows the membership function for the input variables ΔVI and ΔVr, and FIG. 3 shows the membership function for the output variable ΔN _JF1 . Output variable Δ
N _{JF1 is} also normalized to the range of -1 to +1. Figure 2
3 and FIG. 3, the meaning of the label of the membership function is as follows.

NB: Negative Big NM: Negative Medium NS: Negative Small Z: Zero PS: positive Small PM: positive Medium PB: positive Big As fuzzy rules, for example, the following rules are used.

If ΔVI is NB and ΔVr is NB then ΔN _JF1 is PB ... (5) If ΔVI is Z and ΔVr is NB then ΔN _JF1 is PS ... (6) If ΔVI is Z and ΔVr is Z then ΔN _JF1 is Z … (7) If ΔVI is NS and ΔVr is PS then ΔN _JF1 is Z… (8) If ΔVI is PB and ΔVr is PB then ΔN _JF1 is NM… (9) The fuzzy rule of the above equation (5) is “VI If the value is significantly lower than the target value and the wind speed is also significantly lower than the target value,
Increase the number of jet fans operating. It means "." The fuzzy rule in Eq. (6) means "if the VI value is near the target value and the wind speed is considerably lower than the target value, the number of jet fans operating is increased a little." Even if the VI value is close to the target value, if the wind speed is low, the VI value is likely to decrease with a delay.Therefore, the rule in Eq. (6) increases the number of jet fans operating to prevent the VI decrease in advance. ing. FIG. 4 shows a diagram of such fuzzy rules. Fuzzy reasoning is a generally known method, and is described in detail, for example, in the document “Fuzzy Control” by Michio Kanno, Nikkan Kogyo Shimbun (1988).

Further, the VI change rate calculating means 7 includes a VI total 31 and VI.
Input a total of 32 measured values and calculate the rate of change of the VI value over time. The processing method is as follows.

Compare the current measured values of VI total 31 and VI total 32,
The time change rate is calculated for the smaller VI value by the following formula.

ΔVI / Δt = {VI (t) −VI (t−Δt)} / Δt (10) where ΔVI / Δt: change rate of VI [% / s] VI (t): current VI value [ %] VI (t−Δt): VI value after Δt seconds [%] Δt: time step [s].

The second-stage fuzzy inference means 8 has the above-mentioned first
The correction amount ΔN _JF1 of the jet fan operating number calculated by the stage fuzzy inference means 6 and the VI change rate ΔVI / Δt calculated by the VI change rate calculating means 7 are input, and the final number of jet fan operating units is determined by fuzzy inference. Correction amount ΔN _JF2
To calculate. The processing procedure is as follows.

First, the VI change rate is normalized by the following equation.

ΔVI / Δt ← (ΔVI / Δt) / S _DVI (11) where S _{DVI is} a scale factor [% / s] for the rate of change of VI.

The correction amount Δ of the number of jet fans operating
Since N _JF1 has been normalized by the first-stage fuzzy inference means 6, no normalization is performed here.

Next, the correction amount Δ of the number of jet fans operating
The N _JF1 and VI change rate [Delta] Vi / Delta] t as input variables, calculates the correction amount .DELTA.N _JF2 final jet fan operating volume by the fuzzy inference. The membership function for the VI change rate ΔVI / Δt is the same as the membership function for the VI deviation and the wind speed deviation shown in FIG. Also, the membership function for the correction amount ΔN _JF2 of the number of jet fan operating units, which is the output variable of the second-stage fuzzy inference means 8,
Output variable ΔN of the first stage fuzzy inference means 6 shown in FIG.
It is the same as the membership function for _JF1 .

As the fuzzy rules, for example, the following rules are used.

[0034] _{If ΔN JF1 is Zand ΔVI / Δt} is Ns then ΔN JF2 is PS ... (12) If ΔN JF1 is Zand ΔVI / Δt is Z then ΔN JF2 is Z ... (13) If ΔN JF1 is Zand ΔVI / Δt is PS then ΔN _JF2 is NS (14) In the case of the rule of the above equation (12), the first stage fuzzy inference means 6
Output is zero, but the VI value tends to decrease, so the number of jet fans operating is slightly increased. On the contrary, according to the rule of Eq. (14), since the VI value tends to increase, the number of jet fan operating units is slightly decreased. FIG. 5 shows the fuzzy rules used in the second-stage fuzzy inference means 8 in a table.

Since the correction amount ΔN _JF2 of the jet fan operating number calculated by the second-stage fuzzy inference means 8 is a normalized value, it is returned to the actual scale by the following equation.

[0036] _{ΔN JF2 ← S NJF · ΔN JF2} ... (15) where S _NJF: an inverse normalization coefficient of Δ _JF2 [platform].

The output processing means 9 inputs the inference result ΔN _JF2 of the second-stage fuzzy inference means 8 and calculates the control output, that is, the operating number of jet fans by the following equation.

N _JFOUT = N _JF + ΔN _JF2 (16) where N _{JF is} the actual operating number of jet fans at the time of control output calculation [units] N _JFOUT : Control output [units].

Finally, the ventilator operating means 10 receives the control output N _JFOUT from the output processing means 9 so that the number of jet fan operating units becomes N _JFOUT units.
Outputs a run / stop command for 2n.

According to the embodiment shown in FIG. 1, in addition to the measured value of the VI meter and the measured value of the anemometer, the rate of temporal change of the measured VI value is input, and two levels of Since the number of jet fans to be operated is determined by fuzzy inference, it is possible to control by controlling the decreasing tendency or increasing tendency of the VI value.
It is possible to control the VI value within a permissible range with high accuracy, reduce the number of times the jet fan is operated more than necessary, and reduce power consumption.

FIG. 7 is a schematic configuration diagram of an embodiment corresponding to the tunnel ventilation control device according to the second aspect. In the figure,
Those denoted by the same reference numerals as in the above indicate the same elements. This means that instead of the VI change rate calculation means 7 in FIG. 1, the wind speed change rate calculation means 11 and the second stage fuzzy inference means 8 are used.
The second stage fuzzy inference means 12 is provided instead of the above. The operation of this embodiment will be described below with respect to the embodiment shown in FIG.

As described above, the input processing means 5 causes the VI deviation Δ
VI and wind speed deviation ΔVr are calculated, and the first stage fuzzy reasoning means 6
Outputs the correction amount ΔN _{JF1 of the} number of jet fans operating. On the other hand, the wind speed change rate calculation means 11 inputs the measurement value of the wind direction anemometer 4 and calculates the time change rate of the wind speed by the following equation.

ΔVr / Δt = {Vr (t) −Vr (t−Δt)} / Δt (17) where ΔVr / Δt: Wind velocity change rate [(m / s) / s] Δt: Time step [s] Vr (t): Current wind speed [m / s] Vr (t−Δt): Wind speed [m / s] Δt seconds ago.

The second-stage fuzzy inference means 12 includes the correction amount ΔN _{JF1 of the} number of operating jet fans calculated by the first-stage fuzzy inference means 6 and the wind speed change rate ΔVr / Δt calculated by the wind speed change rate calculation means 11. Input and, and calculate the final correction amount ΔN _JF2 of the number of jet fan operating units by fuzzy reasoning. The processing procedure is as follows.

First, the wind speed change rate is normalized by the following equation.

ΔVr / Δt ← (ΔVr / Δt) / S _DVr (18) _However , S _DVr : Scale factor [(m /
s) / s].

The correction amount Δ of the number of jet fans operating
Since N _JF1 has been normalized by the first-stage fuzzy inference means 6, no normalization is performed here.

Next, the second-stage fuzzy inference means 12 uses the correction amount ΔN _JF1 of the operating number of jet fans and the wind speed change rate Δ.
The final correction amount ΔN _{JF2 of the} number of jet fan operating units is calculated by fuzzy inference using Vr / Δt as an input variable. The membership function for the wind speed change rate ΔVr / Δt is the same as the membership function for the VI deviation and the wind speed deviation shown in FIG.

As the fuzzy rules, for example, the following rules are used.

If ΔN _JF1 is Zand ΔVr / Δt is Ns then ΔN _JF2 is PS (19) If ΔN _JF1 is Zand ΔVr / Δt is Z then ΔN _JF2 is Z (20) If ΔN _JF1 is Zand ΔVr / Δt is PS then ΔN _JF2 is NS (21) The fuzzy rules used in the second stage fuzzy inference means 12 are summarized in a table in FIG.

Further, since the correction amount ΔN _{JF2 of the} number of operating jet fans calculated by the second-stage fuzzy inference means 12 is a normalized value, the actual scale is calculated by the equation (15) described in the embodiment of FIG. Return to.

The output processing means 9 determines the control output, that is, the number of jet fans to be operated, by the method described above.

According to the embodiment shown in FIG. 7, in addition to the measured value of the VI meter and the measured value of the wind direction anemometer, the temporal change rate of the measured wind speed is input, and the fuzzy operation in two stages is performed. Since the number of jet fans to be operated is determined by inference, control can be performed by catching the decreasing tendency or rising tendency of the wind speed, and the VI value can be accurately controlled within the allowable range by maintaining the stable wind speed.

FIG. 8 is a schematic configuration diagram of an embodiment corresponding to the tunnel ventilation control device according to the third aspect. In the figure,
Those denoted by the same reference numerals as in the above indicate the same elements. This is different from the configuration shown in the embodiment of FIG.
The output processing means 14 is provided instead of the output processing means 9, the wind speed change rate calculation means 11 described in the embodiment of FIG. 7 is added, and a third stage fuzzy inference means 13 is newly added. The operation of this embodiment will be described below with respect to the embodiment shown in FIG.

First, the input processing means 5, the first-stage fuzzy inference means 6, the VI change rate computing means 7, and the second-stage fuzzy inference means 8 perform the same operations as described with reference to FIG. Further, the wind speed change rate calculation means 11 calculates the wind speed change rate ΔVr / Δt by the same method as described with reference to FIG.

Next, the third-stage fuzzy inference means 13 uses the second
By _inputting the inference result ΔN _JF2 of the stage fuzzy inference means 8 and the wind speed change rate ΔVr / Δt, the final number of jet fans is operated by the same method as the second stage fuzzy inference means 12 described in the embodiment of FIG. The corrective correction amount ΔN _JF3 is calculated. Further, in the third-stage fuzzy inference means 13, the calculated correction amount ΔN _{JF3 of the} number of operating jet fans is a normalized value, and is therefore returned to the actual scale by the following equation.

[0057] _{ΔN JF3 ← S NJF · ΔN JF3} ... (22) where S _NJF: an inverse normalization coefficient of Δ _JF3 [platform].

The output processing means 14 is a third stage fuzzy inference means.
By inputting 13 inference results ΔN _JF3 , the control output, that is, the operating number of jet fans is calculated by the following equation.

N _JFOUT = N _JF + ΔN _JF3 (23) where N _{JF is} the actual number of jet fans operating at the time of control output calculation [units] N _JFOUT : Control output [units].

According to the embodiment shown in FIG. 8, in addition to the measured value of the VI meter and the measured value of the anemometer, the measured VI value and the temporal change rate of the wind speed are input and 3 Since the number of jet fans to be operated is determined by the fuzzy inference of stages, the control can be performed by catching the decreasing tendency and rising tendency of the VI value and the wind speed, and by keeping the wind speed stable, the VI can be controlled.
The value can be accurately controlled within an allowable range, and the jet fan is not operated more than necessary, so that the power consumption can be reduced.

In the above embodiments, the case where the present invention is applied to a road tunnel has been described, but the present invention is not limited to this and can be applied to a railway tunnel or a subway.

Further, in each of the above embodiments, the VI meter is used as the pollution concentration meter, but the pollution concentration does not target only the soot concentration, but, for example, when carbon monoxide is considered as a pollutant. Should use carbon monoxide densitometers, nitrogen oxides as pollutant densitometers when nitrogen oxides are considered as pollutants, and corresponding densitometers for other pollutants. Good.

Further, in the above embodiment, the ventilation of the road tunnel in which the air is sucked in from one well and the air is discharged from the other well has been described. However, as a method for ventilating the tunnel, dust collectors are provided at a plurality of places, Adjusting the air volume of this dust collector according to the pollution concentration, or installing vertical shafts at multiple locations in the tunnel and installing a fan for each vertical shaft, changing the number of operating units according to the pollution concentration, There are things that adjust the air volume. The dust collector and fan of this can also be considered as a kind of ventilator. Therefore,
When the air volume is controlled, the air volume is used as the operating volume of the ventilator, and the correction amount is obtained in the same manner as described above, whereby the same effect can be obtained.

[0064]

As described above, according to the tunnel ventilation control device of the first aspect, the measured value of the pollution concentration meter,
Since the number of jet fans to be operated is determined by two-step fuzzy inference by inputting the measured value of the wind anemometer and the rate of change of the pollution concentration over time, control the pollution concentration with high accuracy. In addition, the jet fan is less likely to be operated more than necessary, and the power consumption can be reduced to achieve energy saving operation.

Further, according to the tunnel ventilation control device of the second aspect, the fuzzy of two stages is input by inputting the measurement value of the pollution concentration meter, the measurement value of the wind direction anemometer, and the temporal change rate of the wind speed. Since the number of jet fans to be operated is determined by inference, control can be performed by catching the downward trend or rising trend of the wind speed, and by maintaining the stable wind speed, it is possible to accurately control the pollution concentration within the allowable range. You can

Further, according to the tunnel ventilation control device of the third aspect, the measurement value of the pollution concentration meter, the measurement value of the wind direction anemometer, the change rate of the pollution concentration with time, and the change rate of the wind speed with time. Is input to determine the number of jet fans to be operated by three-stage fuzzy reasoning, control can be performed by catching the decreasing tendency or rising tendency of the pollution concentration and wind speed, and maintaining stable wind speed. As a result, it is possible to accurately control the pollution concentration within an allowable range, and it is possible to reduce the power consumption by not operating the jet fan more than necessary.

[Brief description of drawings]

FIG. 1 is a schematic configuration diagram of an embodiment corresponding to a tunnel ventilation control device according to claim 1.

FIG. 2 is a diagram showing a membership function for an input variable of fuzzy inference of main elements of the embodiment shown in FIG.

FIG. 3 is a diagram showing a membership function for an output variable of fuzzy inference of a main element of the embodiment shown in FIG.

FIG. 4 is a diagram showing a rule table for fuzzy inference of main elements of the embodiment shown in FIG.

FIG. 5 is a diagram showing a rule table for fuzzy inference of main elements of the embodiment shown in FIG.

FIG. 6 is a diagram showing a rule table for fuzzy inference of main elements of the embodiment shown in FIG.

FIG. 7 is a schematic configuration diagram of an embodiment corresponding to the tunnel ventilation control device according to claim 2.

FIG. 8 is a schematic configuration diagram of an embodiment corresponding to the tunnel ventilation control device according to claim 3.

[Explanation of symbols]

 1 Road tunnel 21, ..., 2n Jet fan 31,32 VI meter 4 Wind direction anemometer 5 Input processing means 6 1st stage fuzzy inference means 7 VI change rate calculation means 8, 12 2nd stage fuzzy inference means 9, 14 Output processing Means 10 Ventilator operating means 11 Wind velocity change rate calculating means 13 Third stage fuzzy inference means

Claims (3)

[Claims] 1. A tunnel ventilation control for controlling a pollution concentration in the tunnel by operating a ventilator installed in the tunnel based on each measured value of a pollution concentration meter installed in the tunnel and an anemometer. In the device, the measurement value of the pollution concentration meter, the measurement value of the wind direction anemometer, the preset pollution concentration target value and the wind speed target value are input, and the pollution concentration deviation and the wind speed deviation for each target value are calculated. An input processing means, a first-stage fuzzy inference means for inputting the pollution concentration deviation and the wind speed deviation calculated by the input processing means, and calculating a first correction amount of the ventilation operation by fuzzy inference; The pollution concentration change rate calculating means for calculating the change rate of the pollution concentration over time, and the first operation of the ventilation operation calculated by the first-stage fuzzy inference means. Second-stage fuzzy inference means for inputting the positive amount and the pollution-concentration change rate calculated by the pollution-concentration change rate calculating means, and calculating a second correction amount of the ventilation operation by fuzzy inference; and the second-stage fuzzy inference means. A tunnel ventilation control device comprising: an output processing unit that determines a control output by adding a ventilation operation amount at that time to a second correction amount of the ventilation operation calculated by the inference unit. . 2. A tunnel ventilation control for controlling the pollution concentration in the tunnel by operating a ventilation machine installed in the tunnel based on the measured values of a pollution concentration meter installed in the tunnel and an anemometer. In the device, the measurement value of the pollution concentration meter, the measurement value of the wind direction anemometer, the preset pollution concentration target value and the wind speed target value are input, and the pollution concentration deviation and the wind speed deviation for each target value are calculated. Input processing means, first stage fuzzy inference means for inputting the pollution concentration deviation and wind speed deviation calculated by the input processing means, and calculating a first correction amount of the ventilation operation by fuzzy inference; The wind speed change rate calculation means for calculating the temporal change rate of the wind direction, and the first correction amount of the ventilation operation calculated by the first-stage fuzzy inference means, and The second-stage fuzzy inference means for inputting the wind-speed change rate calculated by the wind-speed change rate calculation means and calculating the second correction amount of the ventilator operation by fuzzy reasoning, and the second-stage fuzzy reasoning means A tunnel ventilation control device comprising: an output processing unit that determines a control output by adding a ventilation modification operation amount at that time to a second modification quantity of the ventilation operation. 3. A tunnel ventilation control device for controlling a pollution concentration by operating a ventilator installed in the tunnel based on each measured value of a pollution concentration meter installed in the tunnel and an anemometer. Input processing means for inputting a measurement value of a pollution concentration meter, a measurement value of the wind direction anemometer, a preset pollution concentration target value and a preset wind speed target value, and calculating a pollution concentration deviation and a wind speed deviation with respect to each target value. The first-stage fuzzy inference means for inputting the pollution concentration deviation and the wind speed deviation calculated by the input processing means and calculating the first correction amount of the ventilator operation by fuzzy inference, and the measured value of the pollution concentration meter Pollution concentration change rate calculating means for inputting and calculating a change rate of the pollution concentration over time, a first correction amount of the ventilation operation calculated by the first stage fuzzy inference means, and the pollution The pollutant concentration change rate calculated by the concentration change rate calculating means is input, and the second stage fuzzy inference means for calculating the second correction amount of the ventilation operation by fuzzy inference, and the measured value of the wind direction anemometer are input. , A wind speed change rate calculation means for calculating a temporal change rate of the wind speed, a second correction amount of the ventilation operation calculated by the second stage fuzzy inference means, and a wind speed calculated by the wind speed change rate calculation means. A third-stage fuzzy inference means for inputting a rate of change and calculating a third correction amount for ventilation operation by fuzzy inference, and a third correction amount for ventilation operation calculated by the third-stage fuzzy inference means. A tunnel ventilation control device comprising: an output processing unit that determines the control output by adding the operation amount of the ventilator at that time.