Optimization apparatus, optimization method, and optimization program

Abstract

This process derives conditional information for executing the incineration process in an incinerator. [Solution] The optimization device has a storage unit that stores a grate combustion model, a gas phase combustion model, a boiler heat recovery and power generation model, which are physical models for performing simulations of grate combustion, gas phase combustion, boiler heat recovery and power generation in the incineration process of an incinerator, and whose parameters have been adjusted based on measurements taken in the incineration process; a prediction unit that performs a simulation of the incineration process and predicts process values ​​by operating the adjusted grate combustion model, the adjusted gas phase combustion model and the adjusted boiler heat recovery and power generation model; and a derivation unit that derives condition information for performing the incineration process of the incinerator based on the predicted process values.

Description

Technical Field

[0001] The present disclosure relates to an optimization device, an optimization method, and an optimization program.

Background Art

[0002] There is known a technique for calculating the state in an incinerator (for example, garbage retention amount, combustion heat amount, etc.) based on the energy balance of the entire incinerator.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, since the incineration process of the incinerator includes various processes such as grate combustion, gas-phase combustion, boiler heat collection, and power generation, it is difficult to accurately calculate the state in the incinerator by the method of analyzing the energy balance of the entire incinerator as described above. Therefore, in the case of the above method, for example, it cannot be applied to a scene where an optimal operating condition for maximizing a process value (target variable) in the incineration process is searched.

[0005] An object of the present disclosure is to provide an optimization device, an optimization method, and an optimization program for deriving condition information when executing an incineration process of an incinerator.

Means for Solving the Problems

[0006] The optimization device according to the first aspect of the present disclosure is A physical model for performing simulations of grate combustion, gas-phase combustion, boiler heat recovery, and power generation in the incineration process of an incinerator, comprising a grate combustion model, a gas-phase combustion model, a boiler heat recovery and power generation model, and a storage unit for storing the adjusted grate combustion model, adjusted gas-phase combustion model, adjusted boiler heat recovery and power generation model whose parameters have been adjusted based on measurements taken in the incineration process, A prediction unit that runs the adjusted grate combustion model, the adjusted gas-phase combustion model, and the adjusted boiler heat recovery and power generation model to perform a simulation of the incineration process and predict process values, It includes a derivation unit that derives condition information for executing the incineration process of the incinerator based on predicted process values.

[0007] A second aspect of this disclosure is an optimization apparatus according to the first aspect, The control system for controlling the incinerator further includes an instruction unit that instructs the system to execute the incineration process based on the condition information.

[0008] A third aspect of this disclosure is an optimization apparatus relating to the first or second aspect, The prediction unit, When constraints for executing the incineration process are input, the process values ​​for each operating condition are predicted by operating the adjusted grate combustion model, the adjusted gas phase combustion model, and the adjusted boiler heat recovery and power generation models while changing the operating conditions under the said constraints. The aforementioned derivation section is, The operating conditions for executing the incineration process of the incinerator are derived by searching for operating conditions that maximize or minimize the predicted process values.

[0009] A fourth aspect of this disclosure is an optimization apparatus relating to the third aspect, It has a visualization unit that visualizes each operating condition during the search and the corresponding process value.

[0010] A fifth aspect of this disclosure is an optimization apparatus relating to the first or second aspect, The prediction unit, When the operating conditions for executing the incineration process are input, the process values ​​under each constraint are predicted by operating the adjusted grate combustion model, the adjusted gas phase combustion model, and the adjusted boiler heat acquisition and power generation model while changing the constraints. The aforementioned derivation section is, The constraints for executing the incineration process of the incinerator are derived by searching for constraints that maximize or minimize the predicted process value.

[0011] A sixth aspect of this disclosure is an optimization apparatus relating to the fifth aspect, It has a visualization unit that visualizes each constraint condition during the search and the corresponding process value.

[0012] A seventh aspect of this disclosure is an optimization apparatus relating to any of the first to sixth aspects, The adjusted grate combustion model, the adjusted gas-phase combustion model, and the adjusted boiler heat recovery and power generation models are each parameter-adjusted using a Kalman filter so that the output values ​​calculated by inputting the measured values ​​measured in the incineration process approach the output values ​​measured in the incineration process.

[0013] The eighth aspect of this disclosure is an optimization apparatus relating to any of the first to seventh aspects, The aforementioned process value includes the amount of electricity generated or the amount of energy consumed.

[0014] A ninth aspect of this disclosure is an optimization apparatus relating to the third or fifth aspect, The aforementioned operating conditions include control scenarios for the operating end of the incinerator.

[0015] A tenth aspect of this disclosure is an optimization apparatus relating to the third or fifth aspect, The above constraints include conditions based on the equipment capacity of the incinerator and conditions based on the incineration target of the incinerator.

[0016] The 11th aspect of the present disclosure is an optimization device according to the 10th aspect, The conditions based on the equipment capacity of the incinerator include upper and lower limit values of the amount of air blown into the incinerator or upper and lower limit values of the refuse movement speed on the grate.

[0017] The 12th aspect of the present disclosure is an optimization device according to the 10th aspect, The conditions based on the incineration target of the incinerator include upper and lower limit values of the refuse throughput or upper and lower limit values of the calorific value of the refuse.

[0018] The optimization method according to the 13th aspect of the present disclosure is A computer of an optimization device that stores an adjusted grate combustion model, an adjusted gas-phase combustion model, an adjusted boiler heat absorption and power generation model, which are physical models for performing simulations on grate combustion, gas-phase combustion, boiler heat absorption, and power generation in the incineration process of an incinerator, and the parameters of which are adjusted by measurement values measured in the incineration process, Executing a simulation of the incineration process and predicting process values by operating the adjusted grate combustion model, the adjusted gas-phase combustion model, and the adjusted boiler heat absorption and power generation model; Based on the predicted process values, a step of deriving condition information when executing the incineration process of the incinerator is performed.

[0019] The optimization program according to the 14th aspect of the present disclosure is A grate combustion model, a gas-phase combustion model, a boiler heat absorption and power generation model, which are physical models for performing simulations on grate combustion, gas-phase combustion, boiler heat absorption, and power generation in the incineration process of an incinerator, and an optimized grate combustion model, an optimized gas-phase combustion model, and an optimized boiler heat absorption and power generation model whose parameters are adjusted according to the measured values measured in the incineration process are stored in a computer of an optimization device. By operating the adjusted grate combustion model, the adjusted gas-phase combustion model, and the adjusted boiler heat absorption and power generation model, a step of performing a simulation of the incineration process and predicting process values is carried out. Based on the predicted process values, a step of deriving condition information when executing the incineration process of the incinerator is carried out.

Advantages of the Invention

[0020] According to the present disclosure, an optimization device, an optimization method, and an optimization program for deriving condition information when executing the incineration process of an incinerator can be provided.

Brief Description of the Drawings

[0021] [Figure 1] It is a diagram showing an example of the system configuration in the parameter adjustment phase of the target plant. [Figure 2] It is a diagram showing an example of the hardware configuration of the learning device. [Figure 3A] It is a diagram showing an example of the configuration of the incinerator. [Figure 3B] It is a diagram showing an example of a plurality of physical models for performing a simulation on the behavior of the incineration process of the incinerator. [Figure 4] It is a diagram showing an example of the functional configuration of the learning device in the parameter adjustment phase. [Figure 5] It is a first diagram for explaining the outline of the parameter adjustment process. [Figure 6] It is a second diagram for explaining the outline of the parameter adjustment process. [Figure 7]This is the third diagram, illustrating the overview of the parameter adjustment process. [Figure 8] This is an example flowchart illustrating the processing flow during the parameter adjustment phase. [Figure 9] This figure shows an example of a system configuration during the optimization phase of the target plant. [Figure 10] This figure shows an example of the hardware configuration of an optimization device. [Figure 11] This figure shows an example of the functional configuration of the optimization device during the optimization phase. [Figure 12] This diagram illustrates the overview of the operating conditions search process. [Figure 13] This figure shows an example of a search screen. [Figure 14] This is an example of a flowchart illustrating the processing flow during the optimization phase. [Modes for carrying out the invention]

[0022] Each embodiment will be described below with reference to the attached drawings. In this specification and the drawings, components having substantially the same functional configuration are denoted by the same reference numerals, and redundant descriptions will be omitted.

[0023] [First Embodiment] <System configuration during the parameter adjustment phase of the target plant> First, we will describe the system configuration during the parameter adjustment phase of the target plant. Figure 1 shows an example of the system configuration during the parameter adjustment phase of the target plant.

[0024] As shown in Figure 1, the target plant 10 comprises the target equipment 100 and the plant control system 110. The target plant 10 is, for example, a general waste treatment plant intended for waste disposal.

[0025] The target equipment 100 is an incinerator, which transmits measured values ​​taken during operation to the plant control system 110 and performs the incineration process by operating based on control values ​​transmitted from the plant control system 110.

[0026] The plant control system 110 includes a monitoring and control unit 111. The monitoring and control unit 111 monitors the target equipment 100 in operation based on measurements transmitted from the target equipment 100. The monitoring and control unit 111 also calculates control values ​​to operate the target equipment 100 so that the process values ​​in the incineration process, among the measurements transmitted from the target equipment 100, approach target values. Furthermore, the monitoring and control unit 111 transmits the calculated control values ​​to the target equipment 100. The monitoring and control unit 111 is configured with operating conditions determined under predetermined constraints as target values.

[0027] The constraints refer to the upper and lower limits of various operating conditions when carrying out the incineration process. These constraints include, for example, conditions based on the incinerator's capacity and conditions based on the materials being incinerated. Conditions based on the incinerator's capacity include, for example, the upper and lower limits of the amount of air supplied into the incinerator and the upper and lower limits of the waste movement speed on the grate. Conditions based on the materials being incinerated include, for example, the upper and lower limits of the amount of waste processed or the upper and lower limits of the calorific value of the waste.

[0028] Furthermore, operating conditions include, for example, control scenarios for the operating ends of the incinerator.

[0029] The learning device 120 generates a physical model for simulating the behavior of the incineration process of the target equipment 100, which is an incinerator. The learning device 120 acquires measurement values ​​taken during the incineration process of the target equipment 100 from the plant control system 110, and generates an adjusted physical model by adjusting the parameters using the acquired measurement values. The phase in which the learning device 120 adjusts the parameters of the physical model and generates an adjusted physical model is called the "parameter adjustment phase".

[0030] The behavior of the incineration process of the incinerator, which is the target equipment 100, is simulated using multiple physical models (for example, a grate combustion model, a gas-phase combustion model, a boiler heat absorption / power generation model, etc. Details will be described later).

[0031] Thus, the learning device 120 is • To simulate the behavior of the incineration process in an incinerator, multiple physical models are generated. • The generated physical models are adjusted using measurements taken during the incineration process of the incinerator.

[0032] As a result, the learning device 120 can generate multiple adjusted physical models that can perform highly accurate simulations of the behavior of the incineration process of the incinerator.

[0033] <Hardware configuration of the learning device> Next, the hardware configuration of the learning device 120 will be described. Figure 2 shows an example of the hardware configuration of the learning device.

[0034] As shown in Figure 2, the learning device 120 includes a processor 201, memory 202, auxiliary storage device 203, connection device 204, communication device 205, and drive device 206. The processor 201, memory 202, auxiliary storage device 203, connection device 204, communication device 205, and drive device 206 of the learning device 120 are interconnected via a bus 207.

[0035] The processor 201 has various computing devices such as a CPU (Central Processing Unit) and a GPU (Graphics Processing Unit). The processor 201 reads various programs (for example, learning programs, etc.) into memory 202 and executes them.

[0036] Memory 202 has main memory devices such as ROM (Read Only Memory) and RAM (Random Access Memory). The processor 201 and memory 202 form a so-called computer, and the computer realizes various functions by having the processor 201 execute various programs read from memory 202.

[0037] The auxiliary storage device 203 stores various programs and various information used when those programs are executed by the processor 201.

[0038] The connection device 204 is a device for connecting the learning device 120 to an example of an external device, such as the operating device 211 and the display device 212.

[0039] The communication device 205 is a device for communicating with various devices (for example, the plant control system 110) via a network.

[0040] The drive device 206 is a device for setting the recording medium 213. The recording medium 213 here includes media that record information optically, electrically, or magnetically, such as CD-ROMs, flexible disks, and magneto-optical disks. The recording medium 213 may also include semiconductor memory that records information electrically, such as ROMs and flash memory.

[0041] The various programs to be installed on the auxiliary storage device 203 are installed, for example, when the distributed recording medium 213 is set in the drive device 206 and the various programs recorded on the recording medium 213 are read. Alternatively, the various programs to be installed on the auxiliary storage device 203 may be installed by downloading them from the network via the communication device 205.

[0042] <Outline configuration of the incinerator> Next, we will describe the general configuration of the incinerator, which is the target equipment 100. Figure 3A is a diagram showing an example of the incinerator configuration, and it shows an example of the configuration of an incinerator called a stoker-type incinerator.

[0043] As shown in Figure 3A, in a stoker-type incinerator, the waste that is fed in moves on a movable grate. Air is supplied to the movable grate, and the waste moving on the grate undergoes complete combustion by sequentially going through the processes of drying, thermal decomposition, and combustion.

[0044] The incinerated ash generated by combustion is sent to an ash pit (not shown). The exhaust gas generated by combustion (including exhaust gas generated from the combustion of combustible gases generated by combustion) is heated by a waste heat boiler and then sent to a filtration-type dust collector (not shown). The steam generated by the heat absorption in the waste heat boiler is sent to a steam turbine and used for power generation.

[0045] In this embodiment, the following three physical models are used to simulate the behavior of the incineration process of the stoker-type incinerator shown in Figure 3A. • Grate combustion model: A physical model that simulates the behavior of a completely combusted waste material in the region indicated by reference numeral 310, where the waste material moves along a movable grate and undergoes the processes of drying, thermal decomposition, and combustion in sequence. • Gas-phase combustion model: A physical model that simulates the behavior of combustible gas generated by combustion when it is combusted by supplied air in the region indicated by reference numeral 320. • Boiler heat recovery / power generation model: A physical model that simulates the behavior when a steam turbine rotates and generates electricity using steam generated by the absorption of heat from exhaust gas into a waste heat boiler in the region indicated by reference numeral 330.

[0046] Each physical model is represented by a predetermined relational expression, which calculates the desired information by performing the simulations described above. Specific examples include the relational expression disclosed in Japanese Patent Application No. 2023-182295, or the relational expression disclosed in Japanese Patent Application No. 2023-182691.

[0047] A grate combustion model is composed of one, two, or all of the following models: a water evaporation model, a volatile matter release model, and a fixed carbon combustion model, and is constructed according to the type of information to be calculated. This type of information includes, for example, the amount of water evaporation (amount of water vapor), the amount of combustible gas generated, and the amount of fixed carbon combustion. However, the models that constitute a grate combustion model are not limited to these.

[0048] The same applies to the gas-phase combustion model and the boiler heat recovery / power generation model, which are represented by predetermined relational equations that allow the desired information to be calculated by performing the simulations described above. These predetermined relational equations include, but are not limited to, those that calculate information such as the amount of combustible gas burned, the amount of heat recovered by the boiler, and the amount of power generated.

[0049] In this way, by generating a physical model for each process included in the incineration process of an incinerator (grille combustion, gas-phase combustion, boiler heat recovery, and power generation), it is possible to achieve highly accurate simulations of the behavior of the incineration process of an incinerator.

[0050] <Overview of the physical model> Next, we will describe the outlines of the three physical models mentioned above. Figure 3B shows an example of multiple physical models used to simulate the behavior of the incineration process in an incinerator.

[0051] As shown in Figure 3B, the grate combustion model 310M includes: Examples of measurements taken during the incineration process of an incinerator include the composition of the waste being fed in, the amount of waste being fed in, the amount of air supplied, the movement of the movable grate, etc. The amount of combustible gas burned, etc., was calculated by running a simulation using the gas-phase combustion model 320M. The following is entered.

[0052] The grate combustion model 310M performs the simulation based on this input information. This allows the grate combustion model 310M to calculate observable and unobservable state variables in the incinerator. Figure 3B shows an example where the unobservable state variables are: • Composition of waste put in, • Distribution of waste dropped into the furnace, • Weight of waste per grate, • Waste temperature per grate, • Radiant heat from the fire, • Amount of water vapor, • Amount of combustible gas generated, • Composition of flammable gases, • Fixed carbon combustion amount, • Amount of waste moved, This shows how the calculations were performed.

[0053] In this context, observable information refers to information for which both measured values ​​obtained during the incineration process of the incinerator and calculated values ​​obtained by the grate combustion model 310M exist. Observable information includes the composition of the waste being fed in, the amount of waste being fed in, the amount of air supplied, and the movement of the movable grate, as well as, although not shown in Figure 3B, the temperature of the combustible gas, for example. On the other hand, unobservable state variables (information) refer to information for which calculated values ​​obtained by the grate combustion model 310M exist, but for which no measured values ​​are obtained during the incineration process of the incinerator.

[0054] Some of these state variables (e.g., water vapor amount, combustible gas generation amount, fixed carbon combustion amount) calculated by the grate combustion model 310M are input into the gas-phase combustion model 320M.

[0055] The 320M gas-phase combustion model includes: • Some of the state variables input by the grate combustion model 310M (e.g., water vapor amount, combustible gas generation amount, fixed carbon combustion amount), Examples of measurements taken during the incineration process of an incinerator include the amount of air supplied to burn the combustible gas generated by combustion, The following is entered.

[0056] The gas-phase combustion model 320M performs the simulation based on this input information. This allows the gas-phase combustion model 320M to calculate observable information and unobservable state variables in the incinerator. Figure 3B shows an example where unobservable state variables are: • Amount of combustible gas burned, • Composition of post-combustion gas, • Amount of heat removed from the furnace body, This shows how the calculations were performed.

[0057] In this context, observable information refers to information for which both measured values ​​obtained during the incineration process of the incinerator and calculated values ​​obtained by the gas-phase combustion model 320M exist. Observable information includes the amount of air mentioned above, as well as, although not shown in Figure 3B, combustion control temperature, gas concentration, etc. On the other hand, unobservable state variables (information) refer to information for which calculated values ​​obtained by the gas-phase combustion model 320M exist, but for which measured values ​​obtained during the incineration process of the incinerator do not exist.

[0058] Some of these state variables (e.g., the amount of combustible gas burned) calculated by the gas-phase combustion model 320M are input into the grate combustion model 310M and the boiler heat recovery / power generation model 330M.

[0059] The boiler heat recovery / power generation model 330M includes: • Some of the state variables input by the gas-phase combustion model 320M (e.g., amount of combustible gas burned), Examples of measurements taken during the incineration process of an incinerator include exhaust gas flow rate, exhaust gas temperature, etc. (not shown in Figure 3B), The following is entered.

[0060] The boiler heat recovery / power generation model 330M performs the simulation based on this input information. This allows the boiler heat recovery / power generation model 330M to calculate observable information and unobservable state variables in the incinerator. In the example in Figure 3B, the unobservable state variables are: • Heat recovery efficiency, • Amount of heat removed from the furnace body, These are calculated and, as observable information, • Boiler heat output, • Amount of power generation, This shows how the calculations were performed.

[0061] In this context, observable information refers to information for which both measured values ​​obtained during the incineration process of the incinerator and calculated values ​​obtained by the boiler heat recovery / power generation model 330M exist. Observable information includes, in addition to the boiler heat recovery amount and power generation amount mentioned above, exhaust gas flow rate, exhaust gas temperature, etc. On the other hand, unobservable state variables (information) refer to information for which calculated values ​​obtained by the boiler heat recovery / power generation model 330M exist, but measured values ​​obtained during the incineration process of the incinerator do not exist.

[0062] <Functional Configuration of the Learning Device> Next, the functional configuration of the learning device 120 in the parameter adjustment phase will be described. Figure 4 is a diagram showing an example of the functional configuration of the learning device in the parameter adjustment phase. As described above, a learning program is installed in the learning device 120, and when this program is executed, the learning device 120 functions as a measurement value acquisition unit 400 and a parameter adjustment unit 410 in the parameter adjustment phase.

[0063] The measurement value acquisition unit 400 acquires measurement values ​​measured during the incineration process of the incinerator from the plant control system 110 and stores the acquired measurement values ​​in the measurement value storage unit 450.

[0064] The parameter adjustment unit 410 includes a grate combustion model 310M, an adjustment unit 420, a gas phase combustion model 320M, an adjustment unit 430, a boiler heat recovery / power generation model 330M, and an adjustment unit 440.

[0065] The adjustment unit 420 adjusts the parameters of the grate combustion model 310M based on the observable output values ​​calculated by the grate combustion model 310M running a simulation and the measured values ​​stored in the measured value storage unit 450.

[0066] The adjustment unit 430 adjusts the parameters of the gas-phase combustion model 320M based on the observable output values ​​calculated by the gas-phase combustion model 320M running a simulation and the measured values ​​stored in the measured value storage unit 450.

[0067] The adjustment unit 440 adjusts the parameters of the boiler heat recovery / power generation model 330M based on the observable output values ​​calculated by the boiler heat recovery / power generation model 330M running a simulation and the measured values ​​stored in the measured value storage unit 450.

[0068] <Details of parameter adjustment processing by the learning device's parameter adjustment unit> Next, we will explain in detail the parameter adjustment process performed by the parameter adjustment unit 410 of the learning device 120 during the parameter adjustment phase.

[0069] (1) Details of the grate combustion model and parameter adjustment process by the adjustment unit First, we will explain the details of the parameter adjustment process by the grate combustion model 310M and the adjustment unit 420, which are included in the parameter adjustment unit 410. Figure 5 is the first diagram illustrating the overview of the parameter adjustment process.

[0070] As shown in Figure 5, among the measurement values ​​stored in the measurement value storage unit 450, the measurement values ​​necessary for running the simulation by the grate combustion model 310M (for example, the composition of the waste to be fed in and the amount of waste to be fed in) are input to the grate combustion model 310M as input values. As a result, the grate combustion model 310M runs the simulation under default parameters. At this time, the grate combustion model 310M outputs to the adjustment unit 420 an observable output value (calculated value of water evaporation) calculated by running the simulation, which corresponds to the measurement values ​​other than those used as input values ​​among the measurement values ​​stored in the measurement value storage unit 450 (for example, the calculated value of water evaporation, which is an output value corresponding to measurement values ​​other than the composition of the waste to be fed in and the amount of waste to be fed in).

[0071] Furthermore, as shown in Figure 5, among the measurement values ​​stored in the measurement value storage unit 450, • Measurement values ​​other than those used as input values ​​(for example, measurement values ​​other than the composition of the waste being put in, the amount of waste being put in, such as the amount of water evaporation), • Measured values ​​(measured water evaporation amounts) corresponding to the observable output values ​​(calculated water evaporation amounts) calculated by the grate combustion model 310M, This value is input to the adjustment unit 420 as an output value. Note that measurement values ​​other than those used as input values ​​refer to measurement values ​​measured during the incineration process of the incinerator for which there are calculated values ​​calculated by the grate combustion model 310M.

[0072] The adjustment unit 420 includes a Kalman filter 421. The Kalman filter 421 is The observable output value (calculated value of water evaporation) calculated by the grate combustion model 310M is, • Of the measurement values ​​stored in the measurement value storage unit 450, the measurement values ​​other than the measurement value used as the input value (measurement value of water evaporation amount) The parameters of the grate combustion model 310M are updated to approximate the result. This generates the adjusted grate combustion model 310M'. In this way, by updating the parameters using a Kalman filter with measurements taken during the incineration process of the incinerator, it is possible to generate an adjusted grate combustion model 310M' that can perform highly accurate simulations.

[0073] Although not shown in Figure 5, when the grate combustion model 310M performs a simulation, state variables (e.g., amount of combustible gas burned) are input in addition to measured values. However, at the stage of adjusting the parameters of the grate combustion model 310M, the state variables calculated by the adjusted grate combustion model 310M' have not yet been calculated. Therefore, it is assumed here that predetermined values ​​for the state variables are input to the grate combustion model 310M.

[0074] (2) Details of the gas-phase combustion model and parameter adjustment process by the adjustment unit Next, the details of the parameter adjustment process by the gas-phase combustion model 320M and the adjustment unit 430, which are included in the parameter adjustment unit 410, will be explained. Figure 6 is a second diagram illustrating the overview of the parameter adjustment process.

[0075] As shown in Figure 6, among the measurement values ​​stored in the measurement value storage unit 450, the measurement values ​​necessary for running the simulation by the gas-phase combustion model 320M (for example, the measurement value of the amount of air) are input to the gas-phase combustion model 320M as input values. In addition, state variables (for example, the amount of water evaporation (amount of water vapor)) calculated by running the simulation with the adjusted grate combustion model 310M' are input to the gas-phase combustion model 320M. As a result, the gas-phase combustion model 320M runs the simulation under default parameters. At this time, the gas-phase combustion model 320M outputs to the adjustment unit 430 the observable output values ​​(calculated values ​​of combustion control temperature) calculated by running the simulation, which are the output values ​​corresponding to the measurement values ​​other than those used as input values ​​among the measurement values ​​stored in the measurement value storage unit 450 (for example, the calculated values ​​of combustion control temperature, which are output values ​​corresponding to measurement values ​​other than the amount of air).

[0076] Furthermore, as shown in Figure 6, among the measurement values ​​stored in the measurement value storage unit 450, • Measurement values ​​other than those used as input values ​​(for example, measurement values ​​of combustion control temperature other than air volume), • Measured values ​​(measured combustion control temperature) corresponding to the observable output value (calculated value of combustion control temperature) calculated by the gas-phase combustion model 320M, This value is input to the adjustment unit 430 as an output value. Note that the measurement values ​​other than those used as input values ​​refer to measurement values ​​measured during the incineration process of the incinerator for which there are calculated values ​​calculated by the gas-phase combustion model 320M.

[0077] The adjustment unit 430 includes a Kalman filter 431. The Kalman filter 431 is The observable output value (calculated value of combustion control temperature) calculated by the gas-phase combustion model 320M is, • Of the measurement values ​​stored in the measurement value storage unit 450, the measurement values ​​other than the measurement value used as the input value (measurement value of combustion control temperature) The parameters of the gas-phase combustion model 320M are updated to approximate the target. This generates the adjusted gas-phase combustion model 320M'. In this way, by updating the parameters using the Kalman filter with measurements taken during the incineration process of the incinerator, it is possible to generate an adjusted gas-phase combustion model 320M' capable of performing highly accurate simulations.

[0078] (3) Details of the boiler heat recovery / power generation model and parameter adjustment process by the adjustment unit Next, we will explain the details of the parameter adjustment process by the boiler heat recovery / power generation model 330M and the adjustment unit 440, which are included in the parameter adjustment unit 410. Figure 7 is a third diagram illustrating the overview of the parameter adjustment process.

[0079] As shown in Figure 7, among the measured values ​​stored in the measured value storage unit 450, the measured values ​​necessary for running the simulation by the boiler heat recovery / power generation model 330M (for example, measured values ​​of exhaust gas flow rate) are input to the boiler heat recovery / power generation model 330M as input values. In addition, state variables (for example, combustion rate of combustible gas) calculated by running the simulation with the adjusted gas-phase combustion model 320M' are input to the boiler heat recovery / power generation model 330M. As a result, the boiler heat recovery / power generation model 330M runs the simulation under default parameters. At this time, the boiler heat recovery / power generation model 330M outputs to the adjustment unit 440 the observable output values ​​(calculated values ​​of boiler heat recovery) calculated by running the simulation, which are the output values ​​corresponding to the measured values ​​stored in the measured value storage unit 450 other than those used as input values ​​(for example, calculated values ​​of boiler heat recovery, which are output values ​​corresponding to measured values ​​other than exhaust gas flow rate).

[0080] Furthermore, as shown in Figure 7, among the measurement values ​​stored in the measurement value storage unit 450, • Measurement values ​​other than those used as input values ​​(for example, measurement values ​​of boiler heat acquisition, which are measurement values ​​other than exhaust gas flow rate), • Measured values ​​(measured boiler heat acquisition values) corresponding to the observable output values ​​(calculated boiler heat acquisition values) calculated by the boiler heat acquisition / power generation model 330M. This value is input to the adjustment unit 440 as an output value. Note that the measurement values ​​other than those used as input values ​​refer to measurement values ​​measured during the incineration process of the incinerator for which there are calculated values ​​calculated by the boiler heat recovery / power generation model 330M.

[0081] The adjustment unit 440 includes a Kalman filter 441. The Kalman filter 441 is The observable output value (calculated boiler heat gain) calculated by the boiler heat recovery / power generation model 330M is, • Of the measurement values ​​stored in the measurement value storage unit 450, the measurement values ​​other than those used as input values ​​(measurement values ​​of boiler heat acquisition) The parameters of the boiler heat recovery / power generation model 330M are updated to approximate the target. This generates the adjusted boiler heat recovery / power generation model 330M'. This generates the adjusted gas-phase combustion model 320M'. In this way, by updating the parameters using the Kalman filter with the measured values ​​obtained during the incineration process of the incinerator, it is possible to generate the adjusted boiler heat recovery / power generation model 330M' which can perform highly accurate simulations.

[0082] <Processing flow during the parameter adjustment phase> Next, we will explain the processing flow by the learning device 120 during the parameter adjustment phase. Figure 8 is an example of a flowchart showing the processing flow during the parameter adjustment phase.

[0083] In step S801, the learning device 120 acquires measurement values ​​from the plant control system 110.

[0084] In step S802, the learning device 120 adjusts the parameters of the physical model using the measured values ​​and generates an adjusted physical model. The learning device 120 stores the generated adjusted physical model in the auxiliary storage device 203.

[0085] <System configuration during the optimization phase of the target plant> Next, we will describe the system configuration during the optimization phase of the target plant. Figure 9 shows an example of the system configuration during the optimization phase of the target plant.

[0086] As shown in Figure 9, the target plant 10 comprises the target equipment 100 and the plant control system 110. Note that the target equipment 100 and plant control system 110 shown in Figure 9 are the same as those shown in Figure 1, and therefore, their explanation is omitted here.

[0087] The optimization device 900 has a pre-tuned physical model and calculates a predicted process value (target variable) by inputting various operating conditions under predetermined constraints into the pre-tuned physical model. It then searches for the optimal operating conditions that maximize or minimize the calculated predicted process value. In this embodiment, for the sake of simplicity, the optimization device 900 searches for the operating conditions that maximize the calculated predicted process value as the optimal operating conditions. The optimization device 900 provides the optimal operating conditions, which it has found under predetermined constraints, to the plant control system 110. As a result, the plant control system 110 can control the target equipment 100 under the optimal operating conditions.

[0088] <Hardware configuration of the optimization device> Next, the hardware configuration of the optimization device 900 will be described. Figure 10 shows an example of the hardware configuration of the optimization device.

[0089] As shown in Figure 10, the optimization device 900 includes a processor 1001, memory 1002, auxiliary storage device 1003, connection device 1004, communication device 1005, and drive device 1006. The processor 1001, memory 1002, auxiliary storage device 1003, connection device 1004, communication device 1005, and drive device 1006 of the optimization device 900 are interconnected via a bus 1007.

[0090] Since the components included in the hardware configuration of the optimization device 900 are the same as those included in the hardware configuration of the learning device 120 shown in Figure 2, we will omit their explanation here.

[0091] <Functional Configuration of the Optimization Device> Next, the functional configuration of the optimization device 900 will be described. Figure 11 shows an example of the functional configuration of the optimization device during the optimization phase. The optimization device 900 has an optimization program installed, and when this program is executed, the optimization device 900 functions as a prediction unit 1110, an operating condition search unit 1120, a visualization unit 1130, and an operating condition instruction unit 1140.

[0092] The prediction unit 1110 includes a tuned grate combustion model 310M', a tuned gas-phase combustion model 320M', and a tuned boiler heat recovery / power generation model 330M'.

[0093] The adjusted grate combustion model 310M' is, The combination of operating conditions necessary for running the simulation is obtained from the operating condition search unit 1120. • The state variables from the previous cycle required for running the simulation are obtained from the adjusted gas-phase combustion model 320M'. The state variables are calculated by running the simulation. As a result, the tuned grate combustion model 310M' outputs the state variables necessary for running the simulation using the tuned gas-phase combustion model 320M'.

[0094] The tuned vapor phase combustion model 320M' is, The combination of operating conditions necessary for running the simulation is obtained from the operating condition search unit 1120. • Obtain the state variables from the previous cycle required for running the simulation from the adjusted grate combustion model 310M'. The state variables are calculated by running the simulation. As a result, the tuned gas-phase combustion model 320M' outputs the state variables necessary for running the simulation using the tuned boiler heat gain / power generation model 330M'.

[0095] The tuned boiler heat recovery / power generation model 330M' is, The combination of operating conditions necessary for running the simulation is obtained from the operating condition search unit 1120. • Obtain the state variables from the previous cycle required for running the simulation from the adjusted gas-phase combustion model 320M'. Predicted process values ​​are calculated by running a simulation.

[0096] In this embodiment, the prediction unit 1110 is equipped with multiple pre-tuned models capable of performing highly accurate simulations of the behavior of the incineration process of the incinerator, which is the target equipment 100. This allows the prediction unit 1110 to perform highly accurate simulations for various target variables (predicted process values) to search for optimal operating conditions. In other words, the prediction unit 1110 improves the degree of freedom of the target variables when searching for optimal operating conditions and improves the search accuracy when searching for optimal operating conditions (it can search for better operating conditions). The target variables here include, for example, power generation amount, energy consumption amount, or processing cost.

[0097] The operating condition search unit 1120 is an example of a derivation unit. The operating condition search unit 1120 identifies the range of operating conditions that the plant control system 110 can perform under the constraints transmitted from the plant control system 110. The operating condition search unit 1120 sequentially notifies the prediction unit 1110 of the combinations of operating conditions included in the identified range.

[0098] Each time the operating condition search unit 1120 notifies the prediction unit 1110 of a combination of operating conditions included in the identified range, it obtains a prediction process value from the prediction unit 1110 and notifies the visualization unit 1130.

[0099] When the operating condition search unit 1120 receives notification of the maximum predicted process value from the operating condition instruction unit 1140 in response to the visualization unit 1130 notifying it of the predicted process value, it identifies a combination of operating conditions corresponding to that maximum predicted process value. The operating condition search unit 1120 then notifies the operating condition instruction unit 1140 of the identified combination of operating conditions corresponding to the maximum predicted process value as the optimal combination of operating conditions.

[0100] The visualization unit 1130 visualizes the predicted process values ​​(i.e., the predicted process values ​​being searched) that are sequentially notified by the operating condition search unit 1120, identifies the maximum predicted process value, and notifies the operating condition instruction unit 1140 of it.

[0101] When the operating condition instruction unit 1140 receives notification of the maximum predicted process value from the visualization unit 1130, it instructs the operating condition search unit 1120 to identify the corresponding combination of operating conditions and notify the optimal combination of operating conditions. As a result, the operating condition instruction unit 1140 obtains the optimal combination of operating conditions from the operating condition search unit 1120. The operating condition instruction unit 1140 then instructs the plant control system 110 to set the optimal combination of operating conditions as the target value.

[0102] The plant control system 110 controls the incinerator, which is the target equipment 100, according to the optimal combination of operating conditions instructed by the operating condition instruction unit 1140. However, the method of controlling the incinerator is not limited to this. For example, the plant control system 110 may control the incinerator, which is the target equipment 100, according to the optimal combination of operating conditions instructed by the operating condition instruction unit 1140, which has been fine-tuned by an operator or the like.

[0103] <Overview of the operating conditions search process> Next, an overview of the operating condition search process by the operating condition search unit 1120 will be described. Figure 12 is a diagram illustrating the overview of the operating condition search process. As shown in Figure 12, the operating condition search unit 1120 further includes an operating condition range identification unit 1201 and an input / output unit 1202.

[0104] The operating condition range identification unit 1201 acquires constraint conditions from the plant control system 110 and identifies the range of operating conditions that the plant control system 110 can execute under the acquired constraint conditions. The operating condition range identification unit 1201 notifies the input / output unit 1202 of the identified range of operating conditions.

[0105] The input / output unit 1202 sequentially notifies the prediction unit 1110 of combinations of operating conditions that fall within the range of operating conditions notified by the operating condition range identification unit 1201, and obtains predicted process values ​​from the prediction unit 1110. The input / output unit 1202 maintains a correspondence between the combinations of operating conditions notified to the prediction unit 1110 and the predicted process values ​​notified by the prediction unit 1110.

[0106] Furthermore, the input / output unit 1202 sequentially notifies the visualization unit 1130 of the predicted process values ​​obtained from the prediction unit 1110. When the maximum predicted process value is notified by the operating condition instruction unit 1140, the input / output unit 1202 identifies the combination of operating conditions held in association with that maximum predicted process value. The input / output unit 1202 notifies the operating condition instruction unit 1140 of the identified combination of operating conditions as the optimal combination of operating conditions.

[0107] <Example of a search screen> Next, we will describe the search screen where the predicted process values ​​are visualized by the visualization unit 1130. Figure 13 shows an example of the search screen.

[0108] As shown in Figure 13, the search screen 1500 displays a graph 1310 showing the time change of the predicted process value, and graphs 1320, 1330, ... etc. showing the time change of each operating condition, in correspondence with each other.

[0109] As a result, the visualization unit 1130 can visualize the maximum predicted process value that maximizes the predicted process value, and can also visualize the optimal combination of operating conditions.

[0110] <Processing flow in the optimization phase> Next, we will explain the processing flow by the optimization device 900 during the optimization phase. Figure 14 is an example of a flowchart showing the processing flow during the optimization phase.

[0111] In step S1401, the optimization device 900 obtains constraint conditions from the plant control system 110.

[0112] In step S1402, the optimization device 900 identifies the range of operating conditions that the plant control system 110 can perform under the acquired constraints.

[0113] In step S1403, the optimization device 900 performs a simulation using combinations of operating conditions that fall within the range of specified operating conditions and calculates predicted process values.

[0114] In step S1404, the optimization device 900 determines whether the calculated predicted process value is the maximum value. If it is determined in step S1404 that it is not the maximum value (i.e., the answer is NO in step S1404), the device proceeds to step S1405.

[0115] In step S1405, the optimization device 900 changes the combination of operating conditions that falls within the range of specified operating conditions to one that has not been used to run the simulation, and then returns to step S1403.

[0116] On the other hand, if it is determined in step S1404 that the value is the maximum value (if the answer is YES in step S1404), the process proceeds to step S1406.

[0117] In step S1406, the optimization device 900 acquires the combination of operating conditions corresponding to the maximum predicted process value as the optimal combination of operating conditions. The optimization device 900 transmits the acquired optimal combination of operating conditions as a target value to the plant control system 110.

[0118] <Summary> As is clear from the above description, the optimization apparatus 900 according to the first embodiment is This system stores physical models that simulate grate combustion, gas-phase combustion, boiler heat recovery, and power generation during the incineration process of an incinerator. These physical models are adjusted grate combustion models, adjusted gas-phase combustion models, and adjusted boiler heat recovery and power generation models, with parameters adjusted based on measurements taken during the incineration process. • By running the tuned grate combustion model, tuned gas-phase combustion model, and tuned boiler heat recovery and power generation models, the incineration process is simulated and process values ​​are predicted. Based on predicted process values, explore the operating conditions for executing the incineration process of the incinerator.

[0119] As a result, according to the first embodiment, when executing the incineration process of the incinerator, for example, it is possible to search for operating conditions that maximize the predicted process value.

[0120] [Second Embodiment] In the first embodiment described above, a case was described in which the operating conditions for executing the incineration process of the incinerator are searched under predetermined constraints so as to maximize the predicted process value. However, the conditions for maximizing the predicted process value when executing the incineration process of the incinerator are not limited to operating conditions.

[0121] For example, the optimal constraints may be searched under predetermined operating conditions to maximize the predicted process value. Specifically, the upper limit of the amount of waste processed may be searched for when the upper limit of the amount of air that can be blown in is reduced due to a malfunction at the operating end of the incinerator, etc., so that the amount of power generated is maximized.

[0122] Thus, the optimization device 900, • To search for operating conditions (condition information) that maximize (or minimize) the objective variable under constraints, and, • To search for constraints (condition information) that maximize (or minimize) the target variable under operating conditions. This is possible.

[0123] [Third Embodiment] In the first embodiment described above, the details of the optimization algorithm in the operating condition search unit 1120 were not mentioned, but any algorithm can be applied to the operating condition search unit 1120 as long as it is a constraint-based optimization algorithm.

[0124] Specifically, the operating condition search unit 1120 may be subjected to a constrained optimization algorithm such as the Lagrange multiplier method or the subgradient method. Alternatively, the operating condition search unit 1120 may be subjected to a genetic algorithm or the like.

[0125] Furthermore, optimization algorithms are not limited to those that search for optimal operating conditions; they may also be algorithms that derive optimal operating conditions without involving a search process, such as linear programming.

[0126] [Fourth Embodiment] In the embodiments described above, the learning device and the optimization device were described as separate devices, but the learning device and the optimization device may be the same device. Also, in the embodiments described above, the learning device and the optimization device were described as being provided separately from the plant control system, but some or all of the functions of the learning device and some or all of the functions of the optimization device may be implemented in the plant control system.

[0127] Furthermore, although the learning device and the optimization device were described in each of the above embodiments as being implemented by a single device, they may also be implemented by multiple devices. For example, each functional unit implemented by the learning device and the optimization device may be implemented in a distributed manner across multiple devices.

[0128] It should be noted that the present invention is not limited to the configurations shown in the above embodiments, including combinations with other elements. These aspects can be modified without departing from the spirit of the present invention and can be appropriately determined according to their application. [Explanation of Symbols]

[0129] 10: Target Plant 100: Target equipment 110: Plant control system 120: Learning device 310M: Grate combustion model 310M': Adjusted grate combustion model 320M: Gas-phase combustion model 320M': Adjusted gas-phase combustion model 330M: Boiler heat recovery / power generation model 330M': Adjusted boiler heat recovery / power generation model 400: Measurement value acquisition unit 410: Parameter adjustment unit 420~440: Adjustment section 900: Optimization device 1110: Prediction Department 1120: Operational Condition Search Department 1130 :Visualization part 1140: Operating Conditions Instruction Unit 1201: Operation Conditions Range Identification Section 1202: Input / output section

Claims

1. A physical model for performing simulations of grate combustion, gas-phase combustion, boiler heat recovery, and power generation in the incineration process of an incinerator, comprising a grate combustion model, a gas-phase combustion model, a boiler heat recovery and power generation model, and a storage unit for storing the adjusted grate combustion model, adjusted gas-phase combustion model, adjusted boiler heat recovery and power generation model whose parameters have been adjusted based on measurements taken in the incineration process, A prediction unit that runs the adjusted grate combustion model, the adjusted gas-phase combustion model, and the adjusted boiler heat recovery and power generation model to perform a simulation of the incineration process and predict process values, Based on predicted process values, a derivation unit derives condition information for executing the incineration process of the incinerator. An optimization device having the following features.

2. The control system for controlling the incinerator further includes an instruction unit that instructs the system to execute the incineration process based on the condition information. The optimization apparatus according to claim 1.

3. The prediction unit, When constraints for executing the incineration process are input, the process values ​​for each operating condition are predicted by operating the adjusted grate combustion model, the adjusted gas phase combustion model, and the adjusted boiler heat recovery and power generation models while changing the operating conditions under the said constraints. The aforementioned derivation section is, The operating conditions for executing the incineration process of the incinerator are derived by searching for operating conditions that maximize or minimize the predicted process values. The optimization apparatus according to claim 1.

4. It has a visualization unit that visualizes each operating condition during the search and the corresponding process value. The optimization apparatus according to claim 3.

5. The prediction unit, When the operating conditions for executing the incineration process are input, the process values ​​under each constraint are predicted by operating the adjusted grate combustion model, the adjusted gas phase combustion model, and the adjusted boiler heat acquisition and power generation model while changing the constraints. The aforementioned derivation section is, By searching for constraints that maximize or minimize the predicted process values, the constraints for executing the incineration process of the incinerator are derived. The optimization apparatus according to claim 1.

6. It has a visualization unit that visualizes each constraint condition during the search and the corresponding process value. The optimization apparatus according to claim 5.

7. The adjusted grate combustion model, the adjusted gas-phase combustion model, and the adjusted boiler heat recovery and power generation models are each parameter-adjusted using a Kalman filter so that the output values ​​calculated by inputting the measured values ​​measured in the incineration process approach the output values ​​measured in the incineration process. The optimization apparatus according to claim 1.

8. The aforementioned process value includes the amount of electricity generated or the amount of heat recovered by the boiler. The optimization apparatus according to claim 1.

9. The aforementioned operating conditions include a control scenario for the operating end of the incinerator. The optimization apparatus according to claim 3 or 5.

10. The aforementioned constraints include conditions based on the equipment capacity of the incinerator and conditions based on the materials to be incinerated by the incinerator. The optimization apparatus according to claim 3 or 5.

11. The conditions based on the equipment capacity of the incinerator include upper and lower limits on the amount of air supplied into the incinerator, or upper and lower limits on the speed at which waste moves on the grate. The optimization apparatus according to claim 10.

12. The conditions based on the materials to be incinerated in the aforementioned incinerator include upper and lower limits on the amount of waste to be processed, or upper and lower limits on the calorific value of the waste. The optimization apparatus according to claim 10.

13. A computer in an optimization device that stores a grate combustion model, a gas phase combustion model, a boiler heat recovery and power generation model, which is a physical model for performing simulations of grate combustion, gas phase combustion, boiler heat recovery and power generation in the incineration process of an incinerator, wherein the adjusted grate combustion model, adjusted gas phase combustion model, adjusted boiler heat recovery and power generation model have been parameterized based on measurements taken in the incineration process, The process involves running the adjusted grate combustion model, the adjusted gas-phase combustion model, and the adjusted boiler heat recovery and power generation model to simulate the incineration process and predict process values. A step of deriving condition information for executing the incineration process of the incinerator based on predicted process values. An optimization method to perform.

14. A physical model for performing simulations of grate combustion, gas-phase combustion, boiler heat recovery, and power generation in the incineration process of an incinerator, comprising a grate combustion model, a gas-phase combustion model, a boiler heat recovery, and a power generation model, wherein the computer of an optimization device stores the adjusted grate combustion model, adjusted gas-phase combustion model, adjusted boiler heat recovery, and power generation models whose parameters have been adjusted based on measurements taken in the incineration process, The process involves running the adjusted grate combustion model, the adjusted gas-phase combustion model, and the adjusted boiler heat recovery and power generation model to simulate the incineration process and predict process values. A step of deriving condition information for executing the incineration process of the incinerator based on predicted process values. An optimization program to run.

Images