JP2010194445A - Plant operation controller - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a plant operation controller for calculating the quantity of operation control input at a short control cycle. <P>SOLUTION: The plant operation controller includes a constraint condition setting means of estimating a current plant state amount from the inflow amount, history of inflow water quality, and history of the quantity of operation control input by using a nonlinear model simulating a process of a plant, and then linearizing the nonlinear model in the vicinity of the estimated state amount to set an input constraint condition, an output constraint condition, and a minimization constraint condition, a quantity-of-operation-control-input calculating means of calculating the quantity of operation control input of operation instruments by using a linear model based on the inflow amount of inflow water, inflow water quality, and the constraint conditions, and a linear model verification means of making a linear model by a plant state estimating means and a linear model making means when a linear error, which is an error between treated water quality calculated by the linear model and measured treated water quality, exceeds a allowable linear error. <P>COPYRIGHT: (C)2010,JPO&INPIT

Description

  The present invention relates to a plant operation control device that uses a linear model to control, in particular, the quality of treated water and greenhouse gas emissions at a sewage treatment plant.

  In the sewage treatment process, from the standpoint of environmental conservation, in addition to conventional organic matter removal, operation that satisfies a plurality of water quality standards such as nitrogen and phosphorus has been required. In order to realize such operation, attempts have been made to represent the sewage treatment process with a mathematical model and use it for control.

  As a model of a sewage treatment process, for example, as described in [Patent Document 1], there is a model constituted by a nonlinear hydraulic model and a biological reaction model for a plurality of biological reaction tanks and final sedimentation basins. The biological reaction model represents fluctuations in the concentration of microorganisms, pollutants, nitrogen, phosphorus, and other components due to processes such as microbial growth, autolysis, denitrification, and nitrification. [Non-Patent Document 1] published by the Water Environment Association (IWAQ).

  Using this sewage treatment process model, the flow rate and quality of the influent water, which is the inflow of the plant, and the flow rate of the blower and circulation pump, which are the operation volume, are input to the water quality of each tank, which is the plant state quantity, The quality of treated water, which is the output amount, is calculated.

  When a mathematical model is applied to control, the nonlinear model increases the computational load, and thus a linearized linear model is generally used. The linear model is an approximate representation of a nonlinear model in a state quantity, for example, a Taylor expansion in the vicinity of the water quality value in each tank used for calculation, and a matrix having primary partial differential coefficients as elements. In the vicinity of the state quantity used for linearization, the nonlinear model is approximated well.

  One of the control methods using a linear model is model predictive control as described in [Non-Patent Document 2]. This is a method of predicting the future of a process using a linear model of a process to be controlled and determining the operation amount of a blower, a pump, or the like based on the prediction result. There is a feature that optimum control is possible under constraint conditions, and it is possible to perform control for determining an operation amount that minimizes costs and greenhouse gas emissions, for example, while satisfying water quality standards.

  In [Patent Document 2], a nonlinear model representing a sewage treatment process is linearly approximated to create a linear model, and this linear model is applied to model predictive control.

  In order to improve the control accuracy of model predictive control, it is important to handle a linear model. If the state quantity of the sewage treatment plant is close to the state quantity at the time of creating the linear model, high-precision control is possible, but the control accuracy decreases as the two deviate from each other. As a countermeasure against this, in [Patent Document 2], for each control cycle (observation cycle), the state quantity of the actual sewage treatment plant is estimated by calculation of a nonlinear model, and a linear model is created again with the state quantity. Applies to model predictive control. As a result, control accuracy is maintained.

Japanese Patent Laid-Open No. 2001-137881 JP 2002-373002 A

"Activated sludge model No. 2" (IWAQ: IWAQ Scientific and Technical Report No. 3, Activated Sludge Model No. 2, 1995) J. M. Maciejowski, Shuichi Adachi, Masaaki Kanno, Model Predictive Control Optimal control under constraints, Tokyo Denki University Press (2005)

  As described above, in the method described in [Patent Document 2], (1) state quantity estimation by calculation using a nonlinear model, (2) linear model creation near the estimated state quantity, and (3) the created linear model is modeled. Applying to the predictive control, deriving the optimum operation amount of the plant, (4) controlling the sewage treatment plant with the processes (1) to (4) as one cycle of inputting the derived operation amount to the sewage treatment plant. . However, since the processes (1) and (2) have a large calculation load, there is a problem that one cycle of control becomes long and the accuracy of the control decreases.

  An object of the present invention is to provide a plant control device that calculates an operation amount in a short control cycle.

  In order to achieve the above object, the present invention estimates a current plant state quantity from an inflow of inflow water, a history of inflow water quality, and a history of operation amount of operation equipment by a non-linear model simulating a plant process. A plant state quantity estimating means, a linear model creating means for linearizing the nonlinear model in the vicinity of the state quantity estimated by the plant state quantity estimating means, an input constraint condition that is a constraint condition of the operation amount of the operating equipment, and treated water quality The constraint condition setting means for setting the output constraint condition and the minimization constraint condition for minimizing the objective function with the operation amount as a variable, the inflow amount of the influent water calculated by the inflow amount calculation means, and Driving operation amount calculation means for calculating the driving operation amount of the driving equipment using the linear model based on the inflow water quality and the restriction conditions set by the restriction condition setting means; and a linear model When the linear error that is the error between the treated water quality calculated by the above and the treated water quality calculated by the outflow amount calculating means exceeds a preset linear tolerance, the plant state quantity estimating means and the linear model creating means And linear model verification means for creating a linear model.

  In addition, a linear model creation means for linearizing a nonlinear model simulating a plant process, an input constraint condition that is a constraint condition of an operation amount of an operating device, an output constraint condition that is a constraint condition of treated water, and an operation operation amount Restriction condition setting means for setting a minimization restriction condition for minimizing the objective function as a variable, inflow amount and quality of inflow water calculated by the inflow amount calculation means, and restriction conditions set by the restriction condition setting means An error between the driving operation amount calculation means for calculating the driving operation amount of the driving equipment using the linear model, the treated water quality calculated by the linear model, and the upper limit value of the output condition set by the constraint condition setting means And a second output condition calculation means for changing the upper limit value of the output condition to a second output condition smaller than that when the control error exceeds a preset control allowable error. Those were.

  If the control error, which is the error between the treated water quality calculated by the linear model and the upper limit value of the output condition set by the constraint condition setting means, exceeds the preset control tolerance, And a second operating condition calculating means for correcting the operation amount.

  According to the present invention, for example, in order to optimize greenhouse gas emissions and a plurality of treated water qualities, it is possible to accurately determine a plurality of operation amounts such as a blower, a circulation pump, and a return pump.

The block diagram of the sewage treatment plant which is Example 1 of this invention. The figure which shows the flowchart of the plant operation control apparatus of a present Example. The figure which shows the example of a screen which displays the graph of the measured value of the plant operation control apparatus of a present Example, and the calculation result of a linear model. The figure which shows the example of a screen which displays the graph of the linear error and linear allowable error of the plant operation control apparatus of a present Example. The figure explaining the control frequency of a present Example. The block diagram of the sewage treatment plant which is Example 2 of this invention. The figure which shows the flowchart of the plant operation control apparatus of a present Example. The figure which shows the example of a screen when the control error of the plant operation control apparatus of a present Example exceeds an allowable error. The figure which shows the example of a screen which displays the graph of the control error and control allowable error of the plant operation control apparatus of a present Example. The figure which shows the example of a screen which displays the graph of the measured value of the treated water quality of the plant operation control apparatus of a present Example, and the upper limit of an output constraint condition. The figure showing the fluctuation | variation of the upper limit of the output constraint conditions of the plant operation control apparatus of a present Example, the quality of treated water, and the amount of operation. The block diagram of the sewage treatment plant which is Example 3 of this invention. The figure which shows the flowchart of the plant operation control apparatus of a present Example. The figure showing the fluctuation | variation of the treated water quality of the plant operation control apparatus of a present Example, and the operation amount of operation.

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

  A first embodiment of the present invention will be described with reference to FIGS. FIG. 1 is a configuration diagram of a sewage treatment plant including the plant operation control device according to the first embodiment.

  The influent water 2 flowing into the sewage treatment plant 1 is treated by the sewage treatment plant 1 and flows out as treated water 3. The sewage treatment plant 1 includes at least a biological reaction tank and a final sedimentation basin, and includes a blower for aeration in the biological reaction tank, a return pump that returns sludge settled in the final sedimentation basin to the biological reaction tank, It is operated with the set operation amount.

  In order to optimally control this operation amount, the plant operation control apparatus of the present embodiment has a plant inflow amount calculation means 10, a plant outflow amount calculation means 11, an operation amount measurement means 12, and a plant state quantity estimation means. 21, a linear model creation unit 22, an optimum driving operation amount calculation unit 23, a constraint condition setting unit 24, a linear model verification unit 30, and an output unit 40.

  The plant operation control apparatus configured as described above operates as follows according to the flowchart shown in FIG. FIG. 2 shows processing in one control cycle after a certain time has elapsed from the start of control.

  In step S <b> 1, the influent water quality of the influent water 2 and the influent water quality such as the total nitrogen concentration and the total phosphorus concentration are calculated by the plant inflow amount calculating means 10. The treated water quality of the treated water 3 is calculated by the plant outflow amount calculating means 11.

  Since both influent water quality and effluent water quality are used for control, it is desirable to calculate in real time. As a calculation method, the water quality required for control may be directly measured by an online measuring device, or the water quality required for control may be indirectly estimated from the water quality measured by the online measuring device.

  In step S2, input constraint conditions and output constraint conditions, which are constraint conditions set by the constraint condition setting means 24, are read.

  The input constraint condition is a constraint condition of the driving operation amount, such as an upper limit value and a lower limit value of the blower air flow rate. Output constraints include, for example, biochemical oxygen demand (BOD) 20 mg / L, total nitrogen 30 mg / L, total phosphorus 3 mg / L, upper limit values of treated water quality, and greenhouse gas emissions from the entire sewage treatment plant For example, minimizing the amount.

  In step S3, the operation amount of the operation equipment such as the blower air flow rate and the return pump flow rate in the sewage treatment plant is measured.

  In step S4, the linear model used for the control before one control cycle is read.

  In step S5, the value of the treated water quality by the linear model is calculated by the linear model verification means 30 based on the linear model read in step S4 and the measured values of the influent water flow rate and the influent water quality calculated in step S1. Next, the calculated treated water quality value is compared with the measured treated water quality value.

  At that time, the values of the treated water quality at the time of creating the linear model are matched, and the subsequent error is measured as a linear error. In addition, since there is a time delay of output with respect to the input in the linear model, the past data from the comparison time is also used for the input data to the linear model. For example, the period is longer than the hydraulic residence time of the sewage treatment plant.

The linear error Δy _{1 at} time T is defined by, for example, Equation ₁ .

Here, the measured value of the treated water quality at time t is y _p , the treated water quality by the linear model is y _l , τ is the integration time, and K ₁ , K ₂ , K ₃ are 0 or a positive constant. Δy _l , y _p , y _l are vector quantities having the order of the number of water quality items. Equation 1 has the same format as the change in the operation amount in the PID control. The time average value on the right side of Equation ₁ may be used as the linear error Δy _l .

As a result of the comparison, if the linearity error [Delta] y _l exceeds the linear tolerances E _l which is set in advance, it is determined that the linear model does not reproduce the actual phenomena correctly, the process proceeds to step S5-1. If the linear error Δy ₁ is less than or equal to the linear tolerance E ₁ , the process proceeds to step S6.

The linear tolerance E ₁ may be a vector quantity having the order of the number of water quality items. In this case, for example, when the linear error Δy ₁ and the linear allowable error E ₁ are compared for each component of the water quality item, and the linear error Δy ₁ becomes larger than the linear allowable error E _{1 for} one or more components, the linear error Δy _It is determined that _l has exceeded the linear tolerance E ₁ .

The linear tolerance E ₁ may be a scalar quantity. In this case, comparing the scalar quantity created from the weight coefficient and the corresponding components of the water quality of the linearity error [Delta] y _l, the linear tolerances E _l. If the scalar quantity created is greater than the linear tolerances E _l, linearity error [Delta] y _l is determined that exceeds the linear tolerances E _l.

  In step S5-1, a non-linear model simulating a sewage treatment process as a control target is read.

  In step S5-2, a history of inflow water quantity and inflow water quality before one control cycle is read, and a history of operation amount before one control period is read in step S5-3.

  In step S5-4, the plant state quantity estimating means 21 adds the inflow water flow rate and inflow water quality history acquired in step S1 and step S5-2 to the nonlinear model read in step S5-1, and step S3, step S5-4. The history of the operation amount acquired in S5-3 is input, and the current state amount of the sewage treatment plant 1 is estimated.

  In step S5-5, the linear model creation means 22 linearizes the nonlinear model read in step S5-1 near the state quantity calculated in step S5-4 to create a linear model.

  In step S6, the optimum operation amount for satisfying the constraint condition read in step S2 based on the inflow water flow rate, the influent water quality, and the treated water quality calculated in step S1 by the optimum operation amount calculation means 23. Is calculated by model predictive control using a linear model, for example, the blower air flow rate and the return pump flow rate. A vector representing the calculated driving operation amount is represented by u and expressed by Equation 2.

Here, the vector representing the inflow flow rate and the influent water quality is x, the vector representing the measured treated water quality is y _p, and the predictive control using the linear model is represented by the function F. The constraint conditions of the function F are as shown in Equation 3, Equation 4, and Equation 5.

  Here, the subscripts min and max of each variable represent a lower limit value and an upper limit value.

Equation 3 is an input constraint, Equation 4 is an output constraint, and Equation 5 is a minimum constraint. Equation 5 is a constraint condition for minimizing greenhouse gas emissions, and the function V is a vector u for operation amount and y _g which is a vector representing a plurality of greenhouse gases emitted in the sewage treatment process. This is a variable evaluation function. Note that the minimization target of Equation 5 may be the cost of the entire processing plant.

In step S5, if the linearity error [Delta] y _l is determined to linear tolerances E _l greater than the linear model used in the calculation are those created in step S5-5. If it is determined that the linear error Δy ₁ is equal to or _smaller than the linear allowable error E ₁ , the linear model read in step S4 is used.

  In step S7, the operation amount calculated in step S6 is input to an operating device such as a blower or a return pump to control the operation of the sewage treatment plant.

In step S8, the measured value of the treated water quality calculated in step S1 and the time history of the treated water quality based on the linear model calculated in step S5 are displayed on the same graph by the output means 40. FIG. 3 shows an example of this display. In addition, the time history of the linear error Δy ₁ and the linear allowable error E ₁ calculated in step S5 may be displayed on the same graph. FIG. 4 is a diagram showing an example of this display. The output means 40 for displaying may be a monitor.

  As described above, based on the verification result of the linear model in step S5, it is possible to reduce the calculation frequency of the state quantity estimation in step S5-4 and the linear model creation in step S5-5, which have a large calculation load. Compared with the case of the conventional method that calculates every control cycle, the control frequency within a certain period can be increased, and the control accuracy can be improved.

  The effect of the increased control frequency will be described with reference to FIG. Processes (1) to (5) shown in FIG. 5 correspond to the steps in the flowchart shown in FIG. 2, but S1 to S4 and S8 are omitted because they are processed in a short time.

  (A) In the conventional method, (1) a state quantity is estimated by a non-linear model, (2) a linear model is created, (3) an optimum operation amount is derived, and (4) an operation amount is input to the plant. The processes (1) to (4) are repeated. That is, the process moves from step S4 in FIG. 2 to step S5-1 each time. In (1) and (2), the computation time is longer than in other processes. In the example shown in FIG. 5, (1) to (4) are repeated every control cycle, and the runt control is performed twice.

  On the other hand, in the method of this embodiment (b), the linear model is verified in the process (5), and if the linear error is smaller than the linear tolerance, a new linear model is not created and the process (3) The optimum operation amount is derived, and the operation amount is input to the plant of process (4).

  As a case where the linear error in the process (5) is larger than the linear tolerance, for example, the state quantity (water quality) of each treatment tank fluctuates during heavy rain when the quality of sewage flowing into the sewage treatment plant varies greatly. . This is an unsteady phenomenon and is rare. Therefore, in the steady state, in the method of this embodiment, the process (5) → (3) → (4) is one cycle, which is compared with the conventional method (1) → (2) → (3) → (4). Thus, the control frequency can be increased and the control accuracy can be improved.

  A second embodiment of the present invention will be described with reference to FIGS. FIG. 6 is a configuration diagram of a sewage treatment plant including the plant operation control device according to the second embodiment. Instead of the linear model verification unit 30 of the first embodiment, the optimum output operation amount calculation unit 23 is provided with a second output constraint condition calculation unit 25.

  The plant operation control apparatus configured as described above operates as follows according to the flowchart shown in FIG. FIG. 7 also shows processing in one control cycle after a certain time has elapsed from the start of control.

  The processing in steps S1 to S4 is the same as that in the first embodiment. In step S5, the optimal operation amount calculation means 23 verifies the validity of the control model based on the treated water quality calculated in step S1 and the upper limit value of the output constraint read in step S2.

The control error Δy _c, which is an error between the treated water quality and the output constraint condition, is expressed by, for example, _Equation 6, where the treated water quality is y _p and the upper limit value of the output constraint condition is y _pmax .

Here, K ₁ , K ₂ , and K ₃ are 0 or a positive constant. Equation 6 has the same format as the change in the operation amount in the PID control. The time average value on the right side of Equation 6 may be used for the control error Δy _c .

If the control error Δy _c exceeds the preset control allowable error E _c as a result of the comparison, it is determined that the control model is not valid, and the process proceeds to step S5-1. If it is equal to or _smaller than the control allowable error Ec, the process proceeds to step S6.

The control allowable error E _c may be a vector quantity having the order of the number of water quality items. In this case, for example, when the control error Δy _c and the control allowable error E _c are compared for each component of the water quality item, and the control error Δy _c becomes larger than the control allowable error E _{c for} one or a plurality of components, the control error Δy _It is determined that _c exceeds the control allowable error E _c .

The control allowable error E _c may be a scalar quantity. In this case, the control allowable error E _c is compared with the scalar amount created from the component amount of each water quality item of the control error Δy _c and the corresponding weight coefficient. If the scalar quantity created is greater than the control tolerance E _c, it is determined that the control error [Delta] y _c exceeds the control tolerances E _c.

  In step S5-1, the fact that the control error exceeds the control allowable error is displayed on the screen of the monitor which is the output means 40. FIG. 8 shows an example of this. In this example, the process when exceeding is called a sub-mode. The sub mode is a state in which the control accuracy is lowered. In the example shown in FIG. 8, the sub-mode is displayed on the upper right of the screen to alert the operator of the sewage treatment plant. In addition, you may convey that it exceeded with the audio | voice.

In step S5-2, the second output constraint condition calculating unit 25 calculates a second output constraint y _p ^*. The second output constraint condition has the upper limit y ^* _pmax of the treated water quality in the same manner as the output constraint condition. If it is determined that there is an error in step S5, y ^* _pmax is smaller than the upper limit value y _pmax output constraints. The difference y _pmax −y ^* _pmax between the upper limit value of the output constraint condition and the second output constraint condition may be, for example, a constant multiple of the control error Δy _{c in} step S5.

  In step S6, the optimum operation manipulated variable calculation means 23 performs the optimum operation satisfying the constraints based on the influent water flow rate, the influent water quality, the treated water quality calculated in step S1, and the linear model read in step S4. An operation amount, that is, a blower air flow rate, a return pump flow rate, and the like are calculated by model predictive control.

When the process of step S5-2 is performed on the upper limit value of the output constraint condition, the upper limit value y ^* _pmax of the second output constraint condition is used. That is, the equation used for the calculation is the number 2, the number 3, and the number 5 shown in the first embodiment, and the number 7 obtained by replacing the upper limit value y _pmax of the output constraint condition of the formula 4 with the second output constraint condition y ^* _pmax. Become.

  When step S5-2 is not performed, Equations 2 to 5 are used as in the first embodiment.

  In step S7, the operation amount calculated in step S6 is input to an operating device such as a blower or a return pump installed in the plant to control the operation of the sewage treatment plant.

  In step S8, the time history of the control error and the control allowable error calculated in step S5 is displayed on the same graph by the output means 40. FIG. 9 shows this example.

  In addition, the measured value of the treated water quality calculated in step S1 and the upper limit value of the output constraint condition used for calculating the optimum operation amount in step S6 are displayed on the same graph. FIG. 10 shows this example. In this example, since the control error related to the total nitrogen concentration exceeds the control allowable error during the control, the upper limit value of the second output constraint condition smaller than that is used as the upper limit value of the output constraint condition. The output means 40 for displaying may be a monitor.

  If the output constraint condition cannot be satisfied with the driving operation amount calculated based on the output constraint condition, it is considered that the non-linear model does not accurately reproduce the actual phenomenon. In the method of the second embodiment, even in this case, the operation of the sewage treatment plant can be controlled with the optimum operation amount that satisfies the output constraint condition.

  FIG. 11 shows an example of fluctuations in the upper limit value of the output constraint condition, the treated water quality, and the operation amount when the process of step S5-2 is performed. Here, the quality of the treated water was the total nitrogen concentration, and the operation amount was the blower blast amount.

  Before it was determined that there was an error, the operation was performed with the operation amount calculated by the optimum operation amount calculation means 23 in order to keep the total nitrogen concentration below the upper limit value of the output restriction condition. However, the total nitrogen concentration did not fall below the upper limit value of the constraint conditions, and it was determined that there was an error in both in step S5. As a result, in the optimum driving operation amount calculation means 23, the calculation based on the upper limit value of the second output restriction condition calculated in Step S5-2 was performed, the blower blowing amount was improved, and the total nitrogen concentration was reduced. As a result, it was below the upper limit of the original output constraint.

  As described above, in the second embodiment, even when the linear error is larger than the linear allowable error, it is not necessary to perform the process of creating a linear model that requires calculation time, and appropriate control can be realized. Therefore, compared with the conventional method of creating a linear model for each control, the control frequency can be increased and the control accuracy can be improved.

  In addition, the linear model verification means 30 shown in the first embodiment may be added to the present embodiment, and the linear model may be updated in a timely manner as in the first embodiment.

  A third embodiment of the present invention will be described with reference to FIGS. FIG. 12 is a configuration diagram of a sewage treatment plant including the plant operation control device according to the third embodiment. In this embodiment, instead of the second output constraint condition calculating means 25 of the second embodiment, a second driving operation condition calculating means 26 is provided, and the output means 40 is removed.

  The plant operation control apparatus configured as described above operates as follows according to the flowchart shown in FIG. FIG. 13 also shows processing in one control cycle after a certain time has elapsed from the start of control.

  Steps S1 to S5 are the same as those in the second embodiment.

In step S5-1, the second driving operation condition calculation unit 26 calculates the second driving operation condition. For the calculation of the second operating condition, first, a combination of operating equipment necessary for improving the quality of the treated water in which an error has occurred in step S5 to the input constraint condition is selected. Next, a correction function f for correcting the driving operation amount of the driving equipment is obtained according to the amount of error. The correction function f _j related to the driving operation amount u _j of the driving device j is expressed by, for example, Formula 8.

Here, a _j and b _j are variables determined according to the amount of error.

  In step S6, the optimum operation manipulated variable calculation means 23 performs the optimum operation that satisfies the constraint conditions based on the influent water flow rate, the influent water quality, the treated water quality calculated in step S1, and the linear model read in step S4. An operation amount, that is, a blower air flow rate, a return pump flow rate, and the like are calculated by model predictive control. When step S5-1 is performed, the minimum constraint condition of Formula 9 is used in addition to Formulas 2 to 4 shown in the first embodiment.

Equation 9 represents a case where the driving operation amount of the driving device j is corrected by a correction function as an example. Although the correction function f _j is used for the driving operation amount under the minimum constraint condition, the driving operation amount calculated by Equation 2 is u _j . When step S5-1 is not performed, Equations 2 to 5 are used as in the first embodiment.

  In step S7, the operation amount calculated in step S6 is input to an operating device such as a blower or a return pump installed in the plant to control the operation of the sewage treatment plant.

When step S5-1 is performed, the driving operation amount to be input is a corrected driving operation amount obtained by applying the correction function f _j of the second driving operation condition to the driving operation amount u _j calculated in step S6.

  If the output constraint condition cannot be satisfied with the driving operation amount calculated based on the output constraint condition, it is considered that one of the causes is that the nonlinear model does not accurately reproduce the actual phenomenon. In the method of the third embodiment, even in this case, the operation of the sewage treatment plant can be controlled with the optimum operation amount that satisfies the output constraint condition.

  In FIG. 14, the example of the fluctuation | variation of the treated water quality at the time of implementing the process of step S5-1, and the driving | operation operation amount is shown. In this example, the quality of the treated water was set as the BOD concentration, and the operation amount was set as the blower blast amount. Before it was determined that there was an error, driving was performed with the driving operation amount calculated by the optimum driving operation amount calculation means 23 in order to set the BOD concentration below the upper limit value of the output constraint condition. However, the BOD concentration did not fall below the upper limit value of the constraint conditions, and it was determined in step S5 that there was an error in both.

  In step S5-1, in order to reduce the BOD concentration, an operation method for increasing the blower air flow rate was selected, and a correction function for adding a constant value based on the magnitude of the error to the blower air flow rate was created. As a result, the blower blowing amount increased by a certain amount, and the BOD concentration of the treated water was improved. It was judged that there was no error again, and the correction of the blower air volume was released.

  In the method of this embodiment, not only the driving operation amount calculated by the optimum driving operation amount calculating means 23 is corrected, but also the objective function related to minimization is corrected at the same time. Thereby, the operation amount reflecting the optimum calculation result by the model predictive control can be input to the operating equipment installed in the plant.

  Note that the output means 40 added in the second embodiment may be added to the present embodiment, and the fact that the control error exceeds the control allowable error may be displayed on the screen and transmitted by voice as in the second embodiment.

Note that the second output constraint condition calculating means 25 added in the second embodiment is added to the present embodiment, and the second output constraint condition is calculated based on the control error Δy _{c as} in the second embodiment, and the output constraint condition is set. It may be variable.

  As described above, in the method of the present embodiment, even when the linear error is larger than the linear allowable error, it is not necessary to perform the process of creating a linear model that requires calculation time, and appropriate control can be realized. Therefore, compared with the conventional method of creating a linear model for each control, the control frequency can be increased and the control accuracy can be improved.

  In addition, the linear model verification means 30 shown in the first embodiment may be added to the present embodiment, and the linear model may be updated in a timely manner as in the first embodiment.

DESCRIPTION OF SYMBOLS 1 Sewage treatment plant 2 Influent water 3 Treated water 10 Plant inflow amount calculation means 11 Plant outflow amount calculation means 12 Operation amount measurement means 21 Plant state quantity estimation means 22 Linear model creation means 23 Optimal operation amount calculation means 24 Restriction condition setting Means 25 Second output constraint condition calculation means 30 Linear model verification means 40 Output means

Claims (12)

  An inflow amount calculating means for calculating an inflow amount and an influent water quality of the plant inflow water from the measured values, an outflow amount calculating means for calculating the treated water quality of the treated water from the measured values, and a nonlinear that simulates the process of the plant According to the model, a state estimated by the plant state quantity estimating means for estimating the current plant state quantity from the history of the inflow quantity of the influent water, the history of the influent water quality, and the history of the operation quantity of the operating equipment, and the state estimated by the plant state quantity estimation means A linear model creating means for linearizing the nonlinear model in the vicinity of the quantity; an input constraint condition that is a constraint condition of the operation quantity of the operating equipment; an output constraint condition that is a constraint condition of the treated water quality; and the operation manipulated variable A restriction condition setting means for setting a minimization restriction condition for minimizing the objective function, and an inflow amount and an inflow water amount calculated by the inflow amount calculation means. Driving amount calculation means for calculating the driving operation amount of the driving equipment using the linear model created by the linear model creation means based on the constraint condition set by the constraint condition setting means, and the linear model When a linear error that is an error between the calculated treated water quality and the treated water quality calculated by the outflow amount calculating means exceeds a preset linear allowable error, the plant state quantity estimating means and the linear model creating means A plant operation control device comprising linear model verification means for creating a linear model by   An inflow amount calculating means for calculating an inflow amount and an influent water quality of the plant inflow water from the measured values, an outflow amount calculating means for calculating the treated water quality of the treated water from the measured values, and a nonlinear that simulates the process of the plant A linear model creating means for linearizing the model, an input constraint condition that is a constraint condition of the operation amount of the operating device, an output constraint condition that is a constraint condition of the treated water quality, and an objective function having the operation operation variable as variables Based on the constraint condition setting means for setting the minimization constraint condition to be minimized, the inflow amount and quality of the influent water calculated by the inflow amount calculation means, and the constraint conditions set by the constraint condition setting means Driving operation amount calculation means for calculating the driving operation amount of the driving equipment using the linear model created by the linear model creation means, and processing calculated by the linear model. When a control error, which is an error between the water quality and the upper limit value of the output condition set by the constraint condition setting means, exceeds a preset control allowable error, the upper limit value of the output condition is set smaller than that. The plant operation control apparatus provided with the 2nd output condition calculation means changed to 2nd output conditions.   An inflow amount calculating means for calculating an inflow amount and an influent water quality of the plant inflow water from the measured values, an outflow amount calculating means for calculating the treated water quality of the treated water from the measured values, and a nonlinear that simulates the process of the plant A linear model creating means for linearizing the model, an input constraint condition that is a constraint condition of the operation amount of the operating device, an output constraint condition that is a constraint condition of the treated water quality, and an objective function having the operation operation variable as variables Based on the constraint condition setting means for setting the minimization constraint condition to be minimized, the inflow amount and quality of the influent water calculated by the inflow amount calculation means, and the constraint conditions set by the constraint condition setting means Driving operation amount calculation means for calculating the driving operation amount of the driving equipment using the linear model created by the linear model creation means, and processing calculated by the linear model. When the control error, which is an error between the water quality and the upper limit value of the output condition set by the constraint condition setting means, exceeds a preset control allowable error, the second operation amount of the driving device is corrected. A plant operation control device comprising operation condition calculation means.   If the control error, which is the error between the treated water quality calculated by the linear model and the upper limit value of the output condition set by the constraint condition setting means, exceeds a preset control tolerance, the output condition The plant operation control device according to claim 1 or 3, further comprising second output condition calculation means for changing the upper limit value to a second output condition smaller than the upper limit value.   The linear model creation means uses a nonlinear model that simulates the process of the plant to estimate a current plant state quantity from the history of the inflow quantity of the influent water, the history of the influent water quality, and the history of the operation quantity of the operating equipment. The plant operation control apparatus according to claim 2 or 3, wherein the nonlinear model is linearized in the vicinity of the state quantity estimated by the estimation means and the plant state quantity estimation means.   A second driving operation amount condition calculating means for calculating a correction function using the driving operation amount as a variable when a control error that is an error between the output constraint condition and the treated water exceeds a preset control allowable error; The plant operation control device according to claim 1, wherein the optimum operation amount calculation unit uses the correction function as a substitute for the operation amount that is a variable of the objective function.   The plant operation control according to any one of claims 1 to 3, further comprising output means for displaying a time history of the treated water quality calculated by the linear model and the treated water quality calculated by the outflow amount calculating means as a graph on a screen. apparatus.   The plant operation control apparatus according to any one of claims 1 to 3, further comprising output means for displaying a time history of the linear error and the linear allowable error as a graph on a screen.   The plant operation control apparatus according to any one of claims 1 to 3, further comprising output means for displaying that the control error exceeds the control allowable error.   The plant operation control apparatus according to any one of claims 1 to 3, further comprising output means for transmitting by voice that the control error exceeds the control allowable error.   The plant operation control apparatus according to any one of claims 1 to 3, further comprising an output unit that displays a time history of the treated water quality calculated by the output restriction condition and the outflow amount calculating unit as a graph on a screen.   The plant operation control apparatus according to any one of claims 1 to 11, comprising at least minimizing greenhouse gas emissions as the minimum constraint condition.

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