JPS5846721B2 - Plant optimization control method - Google Patents

Description

[Detailed Description of the Invention] [Field of Application of the Invention] The present invention relates to an optimization control method for a noisy plant, and in particular, not only to optimally operate the plant in a steady state, but also to stabilize the plant in a transient state. This invention relates to an optimization control method for a plant that can be operated.

[Background of the invention]

Generally speaking, there are various kinds of plants, but all of them have multiple inputs and multiple outputs.
The relationship between input signals or output signals is controlled to be a predetermined relationship.

Despite the importance of such plant optimization control, it is currently not being carried out well.

In addition to the main control system, a plant is provided with an optimization control device in order to optimally control, for example, the efficiency of the plant.

This involves superimposing a search signal such as efficiency on a part of the plant input and inputting it into the plant, and then using the plant output corresponding to this search signal to find the correlation between the plant's human output signals, thereby optimizing the plant. It's something you drive.

In other words, when performing optimization control of a plant, the plant input signal and the aforementioned search signal are added to the plant, and a component (hereinafter referred to as optimal optimization signal).

However, this method has the disadvantage that if the search signal itself is too large, it will cause disturbance to the plant, making the control system unstable.

On the other hand, if the amplitude of the search signal is made small, the amount of change in the output signal corresponding to the search signal becomes small, making it impossible to determine the correlation.

Furthermore, although it is desired to perform optimization control not only in the steady state of the plant but also in the transient state, there is no example of achieving this.

[Purpose of the invention]

The present invention has been developed in view of the above points, and its purpose is to provide an optimization control method for a plant that can determine the optimum operating point regardless of the steady state or transient state of the plant to be controlled. The objective is to provide a control method.

[Summary of the invention]

The present invention obtains a correction signal of the optimization signal of the plant from the output signal of the plant to be controlled, corrects the optimization signal when the correction signal is smaller than a predetermined value, and It is characterized by optimizing the output signal of the plant.

[Embodiments of the invention]

An optimization control method for controlling the efficiency of a thermal power generation boiler according to the present invention will be described.

In general, experiments have confirmed that in order to optimize boiler efficiency, it is best to control the main steam pressure based on the boiler's excess air ratio among multiple boiler inputs and boiler outputs in terms of responsiveness, effectiveness, etc. There is.

FIG. 1 is a diagram showing an embodiment in which the present invention is applied to a boiler for thermal power generation. Fuel, air, water, etc. are supplied to the boiler 7 as input amounts, and pressure, temperature, flow rate, etc. Output.

The control system is configured so that these have a predetermined relationship.

In other words, regarding fuel and air among the input amounts, air and air are adjusted to meet the boiler load request through two proportional-integral regulators P1 and PI3 with different proportional gains so that the excess air ratio has a predetermined relationship. Fuel is controlled.

A portion 12 indicated by a dotted line is an optimization control device according to the present invention, which inputs the search signal command (1) and the optimization signal x(t) to the output of the regulator P2 to the adders 2, . 22, the excess air ratio is changed by adding and inputting, and the maximum point of boiler efficiency is determined.

Next, considering the characteristics of the boiler 7, the efficiency of the boiler can be divided into a nonlinear element 3 and a linear element 4.

Here, if we assume that the boiler characteristics that are elemental in these elements are first-order lag □+T8, the former 3 corresponds to gain K, and the latter corresponds to □+0. corresponds to

However, T is a boiler time constant and S is a plus operator.

Since the gain changes with time, it is necessary to always find the minimum point of gain and perform optimization control.

Figure 2a is a diagram showing the boiler efficiency of the main steam pressure with respect to the nonlinear element 3 of the boiler, that is, the excess air ratio, where the horizontal axis is the optimization signal x(t) and the vertical axis is the boiler efficiency. It shows.

Hereinafter, this characteristic will be referred to as the efficiency mountain.

Note that the horizontal axis f(t) may be considered to be the excess air ratio.

As is clear from the figure, the relationship between excess air ratio and boiler efficiency under normal operating conditions is that the excess air ratio is small (A
) and incomplete combustion, combustion efficiency decreases.

On the other hand, if the temperature is too thick (point C'), the temperature of the furnace decreases and the amount of radiant heat transfer decreases, and the amount of combustion gas increases and the gas velocity passing through the heat transfer surface increases, resulting in an increase in the amount of heat absorption. If this is not followed, there is a tendency for the gas temperature to rise and heat loss to increase.

Therefore, the excess air ratio (B
/ point) exists.

However, this efficiency peak constantly fluctuates over time due to various disturbances such as load fluctuations, fuel properties, calorific value, and dirt on the heat transfer surface.

The optimization control device seeks this extreme value (maximum efficiency point), and in order to search for the extreme value, it must not have an adverse effect on the boiler.

In other words, to search for the operating point where the boiler efficiency is maximum,
This is done by decreasing the excess air ratio, and in reality, the air flow rate taken into the boiler is controlled by the artificial vane 1.
This is done by slightly varying the opening degree of the 4th etc.

However, this variation in the opening of the artificial vane must not have an adverse effect on the boiler, and since the boiler receives a special input during continuous operation, the signal must be small enough so as not to disturb the operating state.

Here, the search signal x(t
) and □, it is called a sequence signal.

This is 2 with whiteness (including all frequency components).
It is a value signal, and is a pseudo-random signal (Pseudo Ra
Also called ndom Binary Signal).

If the M-sequence signal is sufficiently small, the efficiency peak can be regarded as linear in a minute range, so the present invention superimposes the M-sequence signal on the operating parameter related to efficiency and inputs it, extracts the corresponding plant output, This input/output correlation function is calculated to determine the manipulated variable at the maximum efficiency point.

Although the M-sequence signal is described here as a search signal, this is not limited to the M-sequence signal, and the same applies to other search signals that have no correlation with the vibration of the plant output signal.Here, the M-sequence signal and is as shown in Figure 1, and is a periodic signal whose minimum pulse width is A1, amplitude is al, and period is expressed by NA, and includes all frequency components, -
When integrated over a period of 1M, the average value is almost zero, so if the absolute value 2a is sufficiently small with respect to the operation signal, there will be no adverse effect on the plant during the search.

Using this M-sequence signal as a search signal has the following advantages.

(1) It has whiteness, so there is little variation when calculating the impulse response, (2) The signal pattern at periodic NA has reproducibility, (3) It is easy to create the signal because it is a binary signal, ( 4) Calculation of the correlation function is easy.

In FIG. 1, 1 is an M-sequence signal generator, 2 (2-1,
2-2) is an adder, 11 is an optimization signal regulator, 14 is an inlet vane, 15 is a forced draft fan, 16 is an outlet damper, and the operation signal is the optimization signal j(t) in addition to the output of PI3. and all M-sequence signals (1).

The excess air ratio of the boiler 17 is changed by an amount corresponding to the optimization signal=(t) and the M-sequence signal (1) with respect to the output signal of the PI3.

(In addition, in the boiler for thermal power generation shown in Fig. 1, the search signal "2 (t
) and the optimization signal X(t) are both added to the air flow rate, but as described later with reference to Table 1, depending on the plant, X(t) and It may also be added to a separate plant input.

) FIG. 1 is an example in which X(t) and X(t) are applied to the same plant input.

Next, if we divide the characteristics of the boiler 17 into linear elements 4 and nonlinear elements 3 as described above, the change in the output signal corresponding to the M-sequence signal X(1) is nonlinear as shown in Figure 2a. Element 3 (Efficiency - Y(x, u) where U indicates disturbance.

), its polarity and magnitude change.

In other words, the polarity and magnitude change between the right (point C) and the left (point A) of efficiency -Y (x, u) due to the magnitude of the optimization signal x(t) (which is equivalent to the excess air ratio). It is.

At point B (peak of efficiency -), where the efficiency is maximum, the output is almost zero.

As described above, if the M-sequence signal '>(t) is made sufficiently small, the efficiency -Y(x, u) can be regarded as linear within that small range.

Therefore, the slope of efficiency - Y (x, u) is the value of the change '5t) in the output signal when x (t) is input stepwise,
That is, it can be determined with high precision from the value r(αL) of the indicative response r(α) at time α□.

On the other hand, the M-sequence signal (1) is affected not only by the nonlinear element 3 but also by the linear element 4.

Since 4 is considered to be a first-order delay □+Ts, its output is expressed as y(t) in the figure.

After the above process, the main steam pressure is sent to the optimization control device 1 via the steam pressure detector 18 as a signal for determining efficiency.
Incorporated into 2.

Here, 19 is a strain meter, 20 is a reduction filter, and 4
1 is an amplifier, and the signal Y(
t) is a signal from which whisker-like disturbances have been removed.

This Y(1) is the DC component y(,) and the M-sequence signal y(,)
The signal Y(t) consists of a component ◆(1) corresponding to y(t) and a signal Y(t) corresponding to a variation of the optimized signal Ma(t).

Next, a mathematical formula will be shown and a detailed explanation will be given regarding the optimization control performed using the M-sequence signal (Qt).

It is clear from FIG. 1 that equations (1) and (2) hold true.

Here, the second term φxy(α) in equation (10) is a cross-correlation function between the M-sequence signal (1) and the DC component 7(t).

In addition, the first term φ total y(α) is the cross-correlation function between the M-sequence signal (1) and y(t), and y(t) is the fluctuation component due to the influence of the M-sequence signal (1). Since it consists of a DC component and a DC component, if α is made large enough to eliminate the influence of the value cd(t) of φy7(α), it will match the value of φyy(α).

Therefore, φxy (α) is defined as φxy (interval of ψ (α1.α
Approximate by the average value in 2).

However, the second term of equation (10)' is an approximation of φxy(α), and α1. α2 is the value of α at the time when the impulse response g(α) is sufficiently stabilized.

The initial response value r(αL) obtained by integrating the impulse response g(α) determined by the equation aO)' over the interval (αS to αL) is given by the equation (11).

However, αS is the integration start time (close to zero) that takes into account the shift in the start and end of the impulse response due to the pseudo-whiteness of the M-sequence signal.

α is the end time of the integration interval when integrating the impulse response.

αL is determined in advance from the dynamic characteristics of the plant.

Since r(αL) is normalized by Z shown in equation (9), it corresponds to an initial response when a unit input is given.

Therefore, r(αL) in the aυ equation (generally, αL is the value when α is set to a sufficiently large value) represents the slope of the efficiency.

In other words, it represents the change in efficiency with respect to C(t) in operating force X.

Here, M(t) in Equation 09 is expressed by Equation (2).

This M(t) is obtained by replacing the part surrounded by () in the second item formula of formula 09.

The output signal M(t) of the correlation signal generator 7 is a signal unrelated to the plant response signal y(t) and is determined from the signal Q(t).

This initial response value r (αL) and efficiency −Yo (
There is the following relationship between x and u).

In other words, the efficiency -Y within the range of change when the value t) is small.

The transfer function when (x, u) can be considered linear is G
When (s, x, u), equations (14) and (151) hold true.

However, U is a disturbance. However, Y.

is the value of Y when s = 0. (S is Laplace operator) Here, in equations (14) and (15), r(αL) determined by equation 00 is the efficiency -Y in FIG.

This indicates that the slope of (x, u) is expressed, and the slope r(αL) of efficiency − is determined from the correlation signal M(t).

Figure 2 shows the static characteristics of g(τ) shown in Figure 1,
When x(t) changes from A' to B' to C', the slope of the static characteristic, that is, the initial response r(αL) changes.

Efficiency control becomes possible using this value r(αL).

For example, if the optimization signal MA (1) is changed in proportion to r(αL) by integrating with the optimization signal adjuster 11 according to equation (16), the efficiency can be moved to the highest value.

Here, in equation (13), the second term on the right side is an error term.

This is because r(αL) is originally calculated using equation (17), and r(αL) in equation (13) is obtained from the cross-correlation function between 0(t) and y(t). This is because.

Here, the correlation signal M(t) is 'x'(
t), and is a signal that becomes zero when integrated from (0 to NJ), so if the DC component y(t) is constant, the second term disappears by adding an appropriate predetermined value, r(
The correct value of αL) is determined.

An overview of the configuration and operation of optimization control according to the present invention will be explained with reference to FIG.

The M-sequence signal generator 1 generates an M-sequence signal (Qt) which is a search signal.
)), and adder 2 generates the optimized signal (X(t)')
The operation signal (X(1)) is input to the plant as a specific operation signal.

Here, we will treat the signal as a change due to control.

A specific output signal (first output signal) of the plant corresponding to the operation signal
In the figure, the main steam pressure signal) is measured by the steam pressure detector 18, the strain make 19, the low-pass filter 20, and the amplifier 41.
A detection signal Y(t) is obtained via

The optimization control device 12 calculates a signal Y( corresponding to the DC component of the operation signal X(t), that is, the vibration component of the optimization signal x(1) using the DC component estimator 6 described later from this detection signal Y(t).
t).

Since it is expressed as Y(t)=Y(t)+y(t), the subtracter 29 subtracts Y(t) from Y(t) to obtain the signal y(t).

On the other hand, the search signal %t output from the M-sequence signal generator 1
) is transmitted to a correlation signal generator 7 to generate a correlation signal M(t).

The magnitude r(αL
) is calculated.

Using this r(αL), 1(t) is calculated via the optimization signal adjuster 11 and transmitted to the adder 2, thereby constructing an optimization control system.

The optimization signal adjuster 11 integrates r(αL) according to equation (16), for example, to form a so-called integral ";:*J".

FIG. 3 is a diagram showing a circuit for generating the M-sequence t) and a specific circuit for obtaining the correlation signal M(t) from the M-sequence signal.

However, the same reference numerals as in FIG. 1 indicate the same parts.

2-3.2-4 indicates an adder.

In the figure, 1 is an M-sequence signal generator, and a signal image t) is generated from an n-bit dynamic shift register 32a (2n-1
) is generated at a period of

In the M-sequence signal generator in FIG. 3, (2n-
(n+1)) bit shift register 32b is added to the series so that one cycle of NJ signals can be taken out, and x(1) to this M sequence signal is the optimization signal 7(t) and the output of PI3. It is superimposed and manually input to the plant.

On the other hand, a correlation signal M(t) is obtained from the M-sequence signal t).

24.24 is a shift register 32a. 32b, and the sum of 24 and 25 is the correlation signal M(t
) corresponds to the dynamic characteristic measurement term (first term) in equation (12) for calculating.

26 and 27 are also connected to appropriate positions of the shift registers 32a and 32b, and their sum is the correlation signal M(t).
This corresponds to the DC component correction term (second term) in equation (12) for determining .

In other words, 24 and 25 add the M-sequence signal Q(t-a) within the interval of (αS-αL), and 26 and 27 add the M-sequence signal ζ(t-α) within the interval of (α-α2). It is something to do.

The coefficient unit 28 is set to CIL-a8 in the second term of equation (12).

33 sets the initial value M(o) of the correlation signal α2-α1 signal M(t), and the product obtained by multiplying the output of the adder 2-4 by a predetermined coefficient (coefficient unit 24) is the formula 02). The correlation signal M(t) is expressed as

In FIG. 1, 8 is a multiplier, and the product of the thus obtained correlation signal M(t) and t) and the boiler output 5t) corresponding to 〒 is obtained.

The output of the multiplier 8 is integrated by the integrator 9.
This corresponds to the boiler's individual response (the slope of efficiency - r(αL)) shown in equation 01).

FIG. 4a shows an example of the DC component estimating circuit 6, which is a type of filter that estimates the DC component Y(t) from the boiler output Y(t).

The DC component Y(t) handled here is the optimized control signal X(1)
This is the variation of the output signal due to the addition of an increasing or decreasing correction to y, and is different from the DC component y corresponding to the second term on the right side of the above-mentioned equation aO)'.

In FIG. 4a, 21 is an integrator, 22 is a storage device, and 23
is an adder, 30 is a switch, and 28 (28-r
~28, ) is a coefficient unit.

First, y(t) is integrated by the integrating circuit 21 as shown in the figure, and then taken into the storage device 22 at a predetermined period Jt by closing the switch 30.

In other words, if y(t) is a digital quantity, the information y'(t) taken into 22 is the sum of y(t) in a predetermined cycle At.

The storage device 22 sequentially stores the captured information y'(t), and for example, always stores five pieces of information y'(t).

In other words, the current capture time is t.

Then, five pieces of information from y' (0) to y' (-4) are stored in 22, and at the next import time t1, information from y' (1) to yl (-3) is stored. is memorized.

Incidentally, after the switch 30 is closed and the integrator 21 takes in y'(t), its contents are reset (Re5et).
) and then starts integrating y(t) again.

Here, regarding the DC component estimation circuit, as explained above, five pieces of information (y' (0) to y' (-4)) acquired in a time series at a predetermined period are linearly approximated. By doing so, the DC component Y(t) included in Y(t) is estimated.

In other words, as shown in the figure, at the time of acquisition to, the five pieces of information (y' (0) to y'
(-4)) is linearly approximated by the straight line y'(t), y
' Deviation ε between (0)~y'(-4) and straight line y'(t)
.

Initial value AO of the straight line (time t) that minimizes the sum of squares of ~ε4 (th+εf+εX+εi).

This is to find the value at ) and the slope A1.

And this initial value A. and slope A1 at time t.

Predict the value of y'Ot from tl to tl.

y'(t) predicted between tl and tl is used as the DC component Y(t).

As explained above, the initial value A of the straight line y'(t) that minimizes the sum of the squares of the deviations of the outputs of 22 is set to 5 points.

and slope A. is generally expressed by the following formula.

The coefficient multiplier 28 in FIG. 4a is the above 04) and 05)
04) and (15) in the adders 23-2 and 23-1.
O (value at the current acquisition time) and slope A1 are derived.

23-3 also indicates an adder. A here.

The values of and A1 continue to be output until the next information y' (t) is input by closing the switch 30.

Therefore, the output Y(t) of the DC component estimator 6 is A1(t)+A
n, and this output y(t) is the past five pieces of information y
Regarding the straight line y'(t) obtained by linear approximation of '(0) to y'(-4), time t.

This is a prediction of the state of change from tl to tl.

The DC component estimator 6 calculates the time t obtained in this way.

Let the value of y'(t) from to tl be the DC component Y(t) included in Y(t), and by subtracting Y(t) from Y(t), the slope of efficiency - due to negative t) This is to find r(αL).

Note that at the capture time t1, y'(1) to y'(-3)
The initial value A at tl is determined by the five pieces of information.

The slope A1 is newly determined. Accordingly, y'(t) within the range from tl to t2 is predicted. Y(t) is determined.

In Fig. 1, 29 is a subtracter, which calculates only the boiler output y(t) corresponding to the M-sequence signal t) from the y(t) obtained as described above and the boiler output y(t). is derived.

However, in FIG. 4, the acquisition period Jt and the number of memory points in the storage device 22 (here, 5 points) are necessary to optimally approximate the actual DC component Y(t). be.

Therefore, in order to determine Jt and the number of memory points, they must be determined optimally by referring to the results of sufficient simulations using an electronic computer or the like.

The DC component estimator 6 can be started by setting all the contents of 22 to zero when the plant is in steady operation, then inputting y(t) sequentially, and once all memories have been erased. It is done.

Next, the order t) obtained in this way is obtained from the M sequence signal (1). ooh,.

From (1), the slope r(αL) of efficiency is determined.

At this time, r(αL) that is not affected by y(t) as shown in equation (17) is obtained.

Here, r (αL) indicates the initial response of the boiler characteristics, and as shown in equation a7), all 1) and 9
It is obtained by integrating the cross-correlation function φ minus α) of '(t).

Therefore, the search using the M-sequence signal '4t) as described in detail above is equivalent to inputting a step signal to the boiler and finding its initial response r(αL).

Figure 2 is intended to explain how the initial response r(αL) changes with respect to the magnitude of the optimization signal X(t). The polarity of r(αL) has changed in the case of Jidai).

Further, the value r(αL) at the time αL at which the response becomes sufficiently stable varies depending on the magnitude of the optimization signal x(t).

Therefore, if the magnitude of the optimization signal 7(t) is changed in accordance with the magnitude and polarity of r(αL) determined in this manner, maximum efficiency plant operation can always be expected.

As shown in the figure, the initial response r(αL) at point B where the efficiency is maximum is zero.

Therefore, to maximize the efficiency of the plant, r(αL)=
It is sufficient to control the optimization signal x(t) until it becomes 0.

This is fine in a steady state, but in a transient state, r(αL) suddenly changes and becomes large.

Therefore, it is determined whether r(αL) obtained in the above manner changes rapidly or not, and only when it is recognized that the change is gradual, the optimization signal x( It is better to adjust t).

In other words, adjusting the optimization signal x(1) according to r(αL) in a plant transient state only disturbs the stability of the plant, and cannot be expected to have the effect of improving plant efficiency.

FIG. 5 shows an embodiment of the r(αL) determination circuit 10 according to the present invention.

This circuit determines whether r(αL) has changed normally by calculating the variance (from partiality) of multiple r(αL) taken in time series by (1) at a predetermined period. This is a circuit that does this.

In other words, in the same figure, r(αL) is y(t) in Figure 3.
Similarly, after being integrated by the integrator for a predetermined period Jt, the signal is stored inside the storage device 22 by closing the switch 30.
/(αL).

22 stores, for example, five pieces of data rn to r'n-4.
The average convergence of data 10 to r'n-4 is determined.

29 (29-1 to 29-3) are subtraction circuits that calculate the respective deviations ε(n) to ε'(n
-4).

45 is a squaring circuit that calculates the square value of the output of 47.

23 is an adder circuit whose output is 5 pieces of information r/,...
It represents the variance (from the bias) of /, r/n-4 with respect to the average value rm of r/, -4.

The comparison circuit 48 is a circuit that outputs when this dispersion value is within a predetermined set value.

46 is a hold circuit, which outputs an initial response value r(αL) when there is an output from 48;

When there is no output of 48, that is, when the variance of r(αL) is large, r(αL) is not output.

Therefore, when the variance is within a predetermined value, r(αL) is output as is from the judgment circuit, but when the variance becomes large, it is not output.

Now, if we consider the case where the variance of r(αL) increases, this means that the currently captured /, is larger than the previously captured r'n-1 to r'n-4. This occurs due to a sudden increase or decrease, and such a phenomenon occurs, for example, during a plant transition.

Therefore, even if the judgment circuit according to the present invention as described above is added, during steady operation, the optimization signal X(1
), it is expected that the plant will operate at maximum efficiency.

Furthermore, when r(αL) abnormally increases or decreases during plant transients, etc., the optimization signal is not adjusted.

At this time, since =<1> is not adjusted unnecessarily, the stability of the plant will not be disturbed by an increase or decrease in ma(t) during a transient state.

In FIG. 1, reference numeral 11 denotes an optimization signal adjuster, which adjusts the optimization signal x(t) according to the magnitude and polarity of r(αL), as described above.

Note that the judgment circuit 10 may be one that looks at variations, insulation values, etc. in addition to dispersion.

The optimization signal x(t) is sufficiently small compared to the output of the adjustment device (the output of the adjustment device is set close to the peak of the efficiency).

), the variance will not be extremely large.

Therefore, it is considered abnormal for the variance to be larger than a predetermined value.

** Above, we have explained in detail the case where the optimization control device of the present invention is applied to a thermal power generation boiler, but the same effect can be obtained even when applied to other general plants. Can be done.

In other words, this optimization control device is suitable for optimizing continuous processes.

The objective function can be applied to various cases such as boiler and other efficiency, yield, production cost, profit, loss, quality, etc.

Table 1 shows the correspondence between the case where the present invention is applied to other plants and the above-mentioned embodiment.

In the figure, Example 1 shows an example of the thermal power generation boiler explained in detail with reference to Figure 1, and Examples 2, 4, and 5 are searched in the same way as in the case of the thermal power generation boiler in Example 1. The plant input to which the signal is injected and the plant input controlled by the optimization signal match.

In other words, in the case of the boiler shown in FIG. 1, the slope of the efficiency is determined by adding a search signal to the air flow rate, and the efficiency is moved to the maximum value by adding an optimization signal to the air flow rate.

On the other hand, Example 3 is an example in which the plant input to which the search signal is injected and the plant input to be controlled by the optimization signal do not match.

In other words, this optimization control is to maintain the plant at its optimum level by changing the values of the parameters (time constant, gain) of the steam temperature control system at the boiler outlet. This method searches for parameter values that can optimize the plant, changes the parameters using optimization signals, and controls the plant to optimize the plant.

As explained above, in the optimization control method according to the present invention, similar effects can be expected even if the plant input into which the search signal is injected and the plant input controlled by the optimization signal are separate.

Although the present invention has been described with respect to the case where the efficiency of the boiler is optimally controlled, here the search signal is independent of the M-sequence signal,
It goes without saying that any other signal having whiteness may be used.

Also, according to the correspondence in Table 1, the search signal and the optimization signal are added to the set value, and in FIG. 1 they are added to the output of the regulator PI.

Of course, the only difference is the gain, and in any case, they are the same.

〔Effect of the invention〕

According to the present invention, even if a transient state occurs in a plant to be controlled, stable optimization control can be achieved.

[Brief explanation of drawings]

Fig. 1 is an overall circuit diagram of the optimization control device according to the present invention, Fig. 2 is a diagram for explaining the search for the peak of efficiency, and Fig. 3 is a correlation signal from the M-sequence signal t. A specific circuit configuration diagram for determining M(t), FIG. 4 is an example circuit diagram of a DC component estimation circuit and its operating waveform diagram, and FIG. 5 is an efficiency diagram.
This figure shows an example of a circuit for determining whether the slope r(αL) has changed normally. 13... Search signal adder, 14... Artificial vane, 15... Forced ventilation fan, 16...
...Exit damper, 19...Strain meter, 2
0...Low pass filter, 41...Amplifier, 48...Comparison circuit, 29 (29. to 29.)...Subtractor, 1... ... Search signal generator, 2 ... Adder, 3 ... Boiler efficiency static characteristics, 4 ... Boiler efficiency dynamic characteristics, 5.
...Disturbance characteristics, 6...DC component estimator, 7
... Correlation signal generator, 8 ... Multiplier,
9...Time averager, 10...Judgment device,
11... Optimization signal conditioner, 17...
Boiler, 21...Integrator, 22...Storage device, 23...Adder, 28...Coefficient multiplier, 31... Pulse generator, 32...
Shift register, 33 Initial value of correlation function, 44 Average circuit, 45 Square circuit, 46 Gate circuit, 18・・・・・・
・Steam pressure detector.

Claims (1)

[Scope of Claims] 1. A plant in which there is an operation signal that optimizes a specific output signal in response to a change in a specific operation signal of a controlled plant, in which the operation signal is sequentially corrected by the optimization signal and the specific output signal is In a plant optimization control method that aims at optimization of a plant, a correction signal for correcting the optimization signal is obtained from the specific output signal, and when the correction signal is smaller than a predetermined value, the optimum value at that time is determined. An optimization control method for a plant, comprising: modifying a modified signal using the modified signal, and optimizing a specific output signal of the plant.