Process model construction system and process model construction method

Abstract

To construct a process model which requires less construction load or operational load than a conventional process model even in the case of a control object having a plurality of characteristics.SOLUTION: A process model construction system for constructing a process model obtained by modeling characteristics of a control object by a computer, includes: a first processing unit which acquires characteristics from past operational result data showing operation results corresponding to the characteristics of the control object; and a second processing unit which calculates, for each characteristic, a characteristic parameter for correcting the process model so that a result value of a process of the control object for the characteristic included in the past operational result data and an estimated value of the process of the control object estimated by the process model constructed in accordance with a prescribed theoretical formula satisfy a prescribed condition.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a process model construction system and a process model construction method. [Background technology]

[0002] In process operation, there are times when the optimal operating conditions are known from past operating records, but the operating method to reproduce those operating conditions is unknown. This is because the operating method to achieve the same operating conditions varies each time due to various disturbances that vary with each operation, such as reaction heat generated within controlled objects including control devices such as valves, the characteristics of deteriorated control devices, and external environmental conditions that cannot be obtained as inputs.

[0003] One existing technology addressing this issue is model predictive control (MPC). MPC models the characteristics of a controlled object, enabling prediction of a controlled variable relative to the controlled object's manipulated variable, thereby enabling back-calculation of a manipulated variable that satisfies a desired control waveform. Patent Document 1, for example, describes such an existing technology. In Patent Document 1, a first-principles model is used to simulate a batch process, and this first-principles model can be used to configure a multiple-input / multiple-output control routine to control the batch process. The first-principles model can generate estimates of batch variables that cannot be measured or are not measured during the operation of the actual batch process. An example of such a variable may be the rate of change of a component of the batch process (e.g., production rate, cell growth rate, etc.). It is described that the first-principles model and its configured multiple-input / multiple-output control routine can be used to facilitate control of the batch process. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2012-64245 Summary of the Invention [Problem to be solved by the invention]

[0005] In the aforementioned Patent Document 1, a mechanism (software sensor) is added that uses a first-principles model to estimate difficult-to-measure factors present in a batch process. This mechanism elucidates an appropriate operating method based on the estimated values ​​of the first-principles model, and then uses the estimated values ​​as the control object to shorten the batch cycle and improve yield. However, controlling a control object with different characteristics requires manually constructing different first-principles models, making practical application difficult in multi-product production sites. For example, when there are multiple combinations of the characteristics of the product manufactured by the control object and the characteristics of the control equipment to be controlled, a process model must be constructed for each combination. In particular, in multi-product production sites such as batch processes, the number of combinations of product types and equipment, etc., is large, resulting in a huge number of process models to be constructed. Building a large number of process models is difficult, and post-construction maintenance becomes cumbersome, resulting in a heavy operational burden. Therefore, there is a need for a technology for building process models that does not impose construction and operational burdens compared to conventional methods, even when the control object has multiple characteristics.

[0006] An object of one aspect of the present invention is to provide a technique for constructing a process model that imposes less construction and operation load than conventional techniques, even when a control target has multiple characteristics. [Means for solving the problem]

[0007] A process model construction system according to the present invention is a process model construction system that constructs, by a computer, a process model that models characteristics of a controlled object, and is characterized by having: a first processing unit that acquires the characteristics from past operation performance data that indicates operation performance according to the characteristics of the controlled object; and a second processing unit that calculates, for each characteristic, a characteristic parameter for correcting the process model such that an actual value of the process of the controlled object for the characteristic included in the past operation performance data and an estimated value of the process of the controlled object estimated by the process model constructed using a predetermined theoretical formula satisfy a predetermined condition. [Effects of the Invention]

[0008] According to one aspect of the present invention, even when a control target has a plurality of characteristics, it is possible to construct a process model that imposes less construction load and less operational load than conventional models. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 illustrates an example of a configuration of a process model building system according to a first embodiment. [Figure 2] FIG. 1 is a diagram illustrating an outline of a computer. [Figure 3] FIG. 10 is a diagram showing an example of past operation performance data. [Figure 4] FIG. 2 is a diagram illustrating an example of a process model and an example of a relationship between the process model and characteristics. [Figure 5] FIG. 10 is a diagram illustrating an example of a characteristic parameter group. [Figure 6] FIG. 10 is a diagram illustrating an example of characteristic information. [Figure 7] FIG. 10 is a diagram illustrating an example of a flowchart showing a processing procedure of a model construction process performed by a model construction unit in the first embodiment. [Figure 8A] FIG. 8 is a diagram showing an example of a flowchart illustrating a processing procedure for calculating characteristic parameters performed in S706 shown in FIG. 7. [Figure 8B]FIG. 8 is a diagram showing an example of a flowchart illustrating a processing procedure for calculating characteristic parameters performed in S706 shown in FIG. 7. [Figure 9A] FIG. 8C is a diagram showing an example of a flowchart illustrating the processing procedure for model identification processing of a process model recorded in a model identification data group in the characteristic parameter calculation processing shown in FIGS. 8A and 8B. [Figure 9B] FIG. 8C is a diagram showing an example of a flowchart illustrating the processing procedure for model identification processing of a process model recorded in a model identification data group in the characteristic parameter calculation processing shown in FIGS. 8A and 8B. [Figure 10] FIG. 10 is a diagram illustrating an example of a flowchart showing a processing procedure of a simulation process performed by a simulation unit in the first embodiment. [Figure 11] FIG. 10 is a diagram illustrating an example of a configuration of a process model building system according to a second embodiment. [Figure 12A] FIG. 10 is a diagram illustrating an example of a configuration of a process model building system according to a third embodiment. [Figure 12B] FIG. 10 is a diagram illustrating an example of additional characteristic information. [Figure 13A] FIG. 10 is a diagram illustrating an example of the configuration of a process model building system according to a fourth embodiment. [Figure 13B] FIG. 10 is a diagram illustrating an example of model identification information. [Figure 14] FIG. 13 is a diagram illustrating an example of the configuration of a process model building system according to a fifth embodiment. [Figure 15] FIG. 13 is a diagram illustrating an example of a flowchart showing a processing procedure of a simulation process performed by a simulation unit in the fifth embodiment. [Figure 16] FIG. 20 is a diagram illustrating an example of the configuration of a process model building system according to a sixth embodiment. [Figure 17] FIG. 20 is a diagram illustrating an example of a flowchart showing the processing procedure of a model construction process performed by a model construction unit in the sixth embodiment. [Figure 18A] FIG. 18 is a diagram illustrating an example of a flowchart showing the processing procedure of the characteristic model calculation processing performed in S1706 shown in FIG. [Figure 18B]FIG. 18 is a diagram illustrating an example of a flowchart showing the processing procedure of the characteristic model calculation processing performed in S1706 shown in FIG. [Figure 19] FIG. 2 is a diagram showing an example of a screen (model construction screen) displayed on a display of a computer constituting the process model construction system when a model construction unit executes a model construction process. [Figure 20] FIG. 2 is a diagram showing an example of a screen (simulation execution screen) displayed on a display of a computer constituting the process model building system when a simulation unit executes a simulation process. [Figure 21] FIG. 10 is a diagram showing an example of a screen (feedback execution screen) displayed on the display of a computer constituting the process model construction system when a control device input calculation unit back-calculates an operation method of a controlled object that satisfies optimal operating conditions. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. The following description and drawings are examples for explaining the present invention, and some omissions and simplifications have been made as appropriate for clarity of explanation. The present invention can be implemented in various other forms. Unless otherwise specified, each component may be singular or plural.

[0011] In order to facilitate understanding of the invention, the position, size, shape, range, etc. of each component shown in the drawings may not represent the actual position, size, shape, range, etc. Therefore, the present invention is not necessarily limited to the position, size, shape, range, etc. disclosed in the drawings.

[0012] In the following explanation, various types of information may be described using expressions such as "database," "table," and "list," but the various types of information may also be expressed in data structures other than these. To indicate that the information is not dependent on the data structure, "XX table," "XX list," etc. may be referred to as "XX information." When describing identification information, expressions such as "identification information," "identifier," "name," "ID," and "number" are used, and these are interchangeable.

[0013] When there are multiple components with the same or similar functions, they may be described using the same reference numeral with different subscripts. However, when there is no need to distinguish between these multiple components, the subscripts may be omitted.

[0014] Furthermore, in the following description, processing performed by executing a program may be described, but the program is executed by a processor (e.g., a CPU (Central Processing Unit) or a GPU (Graphics Processing Unit)) to perform the specified processing while appropriately using storage resources (e.g., memory) and / or interface devices (e.g., communication ports), and therefore the processor may be the subject of the processing. Similarly, the subject of the processing performed by executing a program may be a controller, device, system, computer, or node having a processor. The subject of the processing performed by executing a program may be any computing unit, and may include a dedicated circuit (e.g., an FPGA (Field-Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit)) that performs specific processing.

[0015] A program may be installed on a device such as a computer from a program source. The program source may be, for example, a program distribution server or a computer-readable storage medium. If the program source is a program distribution server, the program distribution server may include a processor and storage resources for storing the program to be distributed, and the processor of the program distribution server may distribute the program to be distributed to other computers. Also, in the following description, two or more programs may be realized as one program, and one program may be realized as two or more programs. [Example]

[0016] The configuration of an embodiment of a process model construction system and a process model construction method according to this embodiment will be described below.

[0017] Fig. 1 is a diagram illustrating an example of the configuration of a process model construction system according to Example 1. As illustrated in Fig. 1, a process model construction system 1000 according to this example includes a model construction unit 1001, a simulation unit 1002, a characteristic parameter group 1003, and a process model 1004.

[0018] The process model construction system 1000 constructs a process model suitable for a control object with many combinations of characteristics by setting the parameters of the process model, constructed using a theoretical formula, according to the characteristics of the process model based on past operational performance data of the control object. This enables process simulation using a small number of process models without creating a process model for each combination of characteristics. The control object is an object controlled by this system, such as a control device that performs process operation. In the following description, the control object is assumed to be equipment such as a reactor used in a plant, but may also be applied to various equipment and devices other than plants that perform process operation. Furthermore, the characteristics of the control object are assumed to be the type of equipment and the type of product manufactured by the control object, but other information that represents the characteristics of the control object may also be defined as characteristics.

[0019] Characteristics are elements that affect the behavior of a process model, such as the type of product manufactured by the controlled object through process operation or the equipment that operates the process. A characteristic type, for example, represents the type of elements included in a certain type of characteristic. A characteristic combination, for example, represents a combination of multiple types of characteristics.

[0020] The model construction unit 1001 calculates characteristic parameters (described later) for correcting the parameters of the process model for controlling the control object that performs the process operation based on the past operation performance data 101 and the prepared process model 1004, and stores the calculated characteristic parameters in the characteristic parameter group 1003.

[0021] The simulation unit 1002 corrects the parameters of the process model 1004 with the correction parameters stored in the characteristic parameter group 1003 based on the input characteristic information 102. The simulation unit 1002 simulates the behavior of the process performed by the controlled object based on the simulation input information 103, and stores the results in the simulation result 104. The simulation input information 103 is information that defines the method and conditions for performing the simulation (e.g., operating conditions desired by the user), and includes, for example, reference values ​​that represent the setting values ​​set for the controlled object at each time.

[0022] The characteristic parameter group 1003 stores one or more characteristic parameters. The characteristic parameter group 1003 is a table that stores a set of correction parameters for correcting a process model that indicates the characteristics of a controlled object. Here, an example is shown in which two characteristic parameters, a characteristic parameter that indicates the type of product manufactured by the controlled object and a characteristic parameter that indicates the equipment that is the controlled object, are stored in the characteristic parameter group 1003 as the characteristics of the controlled object. As described above, the number and types of characteristic parameters may be determined appropriately depending on the environment in which this system is applied.

[0023] The process model 1004 is a model of the characteristics of the controlled object, and is constructed using a predetermined theoretical formula. The process model 1004 can be expressed using various mathematical formulas. In the following, as an example, y=ax 2 The following description will be given using a model expressed as +bx+c. In this case, a, b, and c are parameters of the model, x is an input value of the model, and y is an output value of the model. Specific functions of the process model construction system 1000 will be described later using flowcharts and the like. In addition, although this example illustrates a case where the process model 1004 is expressed by one logical formula related to a mathematical model, it may be composed of multiple logical formulas, and further, there may be multiple inputs to or outputs from one or more of these logical formulas.

[0024] The process model construction system 1000 shown in FIG. 1 can be realized by, for example, a general computer 1600, as shown in FIG. 2 (schematic diagram of a computer), which includes a CPU 1601, a memory 1602, an external storage device 1603 such as an HDD (Hard Disk Drive), a reading / writing device 1607 for reading and writing information from / to a portable storage medium 1608 such as a CD (Compact Disk) or USB memory, an input device 1606 such as a scanner, keyboard, or mouse, an output device 1605 such as a display, a communication device 1604 such as an NIC (Network Interface Card) for connecting to a communication network, and an internal communication line (referred to as a system bus) 1609 such as a system bus that connects these devices together.

[0025] Various data (e.g., characteristic parameter group 1003, process model 1004) stored in the process model construction system 1000 or used for processing can be realized by the CPU 1601 reading and using the data from the memory 1602 or external storage device 1603. Furthermore, each functional unit (e.g., model construction unit 1001, simulation unit 1020) of each system or device can be realized by the CPU 1601 loading a predetermined program stored in the external storage device 1603 into the memory 1602 and executing it.

[0026] The above-mentioned predetermined program may be stored (downloaded) into the external storage device 1603 from the storage medium 1608 via the reading / writing device 1607 or from a network via the communication device 1604, and then loaded onto the memory 1602 and executed by the CPU 1601. Alternatively, the program may be directly loaded onto the memory 1602 from the storage medium 1608 via the reading / writing device 1607 or from a network via the communication device 1604, and then executed by the CPU 1601.

[0027] In the following, an example is given in which the process model construction system 1000 is configured by one computer, but all or part of these functions may be distributed across one or more computers, such as a cloud, and the same functions may be realized by communicating with each other via a network. Specific processing performed by each unit constituting the process model construction system 1000 will be described later using flowcharts.

[0028] FIG. 3 is a diagram illustrating an example of past operation performance data 101. The past operation performance data 101 is data indicating past operation performance obtained from a controlled object performing process operation, and is input to the model construction unit 1001. As illustrated in FIG. 3, the past operation performance data 101 stores a data ID for identifying the data, a product type and equipment indicating characteristics, and operation performance data, in association with each other. The operation performance data is, for example, operation and measurement information of a controlled object constituting a plant, and stores, for example, time-series data including a reference value, a manipulated variable, and a controlled variable. The reference value is information indicating a set value (in this example, a temperature set value) set for the controlled object (e.g., a reactor) at each time. The manipulated variable is information indicating an operation method of the controlled object calculated by the controlled object equipment based on the reference value to realize control by the controlled variable. In this example, it is assumed that hot water flows on the wall of the controlled reactor, and the temperature is changed by changing the opening / closing degree of a valve through which the hot water flows. The controlled variable is an operating condition of the control that is actually performed by the controlled object, and is, for example, information indicating the actual temperature inside the reactor that is the controlled object.

[0029] In FIG. 3, for example, the past operation performance data identified by data ID "0" includes operation performance data 301 for a controlled object having characteristics represented by product type "A" and equipment "A." The reference value, manipulated variable, and controlled variable are stored in chronological order at predetermined intervals (e.g., every minute) as reference value α0 [°C]: [20, 20, 50, 50, ..., 20], manipulated variable β0 [%]: [15, 18, 80, 70, ..., 0], and controlled variable γ0 [°C]: [15, 18, 37, 40, ..., 30], respectively. For example, the reference value α0 of equipment "A" that produces a product of product type "A" stores temperature values ​​such as 20°C, 20°C, 50°C, 50°C, ..., 20°C for every minute.

[0030] 4 is a diagram showing an example of the process model 1004 and an example of the relationship between the process model and characteristics. The process model is expressed by a model constructed using a predetermined theoretical formula as described above. In FIG. 4, y=ax 2A model represented by +bx+c is shown as an example of a process model. In this example, the initial values ​​of parameters a, b, and c of the process model are given as 2, 1, and 0, respectively. Furthermore, by processing described below, the correction amounts of the process model parameters are calculated as +0.2, +0, and +0 for the product type, respectively, and as +0, -0.1, and +0 for the equipment, respectively. As a result, the corrected parameters a, b, and c of the process model are calculated as 1.2, -0.1, and 0, respectively.

[0031] FIG. 5 is a diagram showing an example of the characteristic parameter group 1003. The characteristic parameter group 1003 stores characteristic parameters corresponding to the characteristics. In this example, characteristic parameters are stored for each characteristic and for each characteristic type. As shown in FIG. 5, the characteristic parameter group 1003 stores the type of characteristic of the control target (product type in this example) and characteristic parameters that are correction parameters for the parameters of the process model that indicate the characteristic, in association with each other. In FIG. 5, it can be seen that the characteristic parameters for correcting the parameters of the process model indicated by the product type "I" are stored as a: 1.2, b: -0.1, and c: 0, respectively.

[0032] 5 shows an example of a table in which characteristic parameters for each product type are stored, and the table stores as many characteristic parameters as there are characteristics in the characteristic parameter group 1003. Also, in FIG. 5, the process model is 2 Since the model is expressed as +bx+c, the explanation is based on the assumption that the parameters consist of three: a, b, and c. However, as mentioned above, when the process model is expressed in a different way, the number of characteristic parameters stored depends on the format of the expression.

[0033] FIG. 6 is a diagram showing an example of the characteristic information 102. The characteristic information 102 is information indicating the characteristic of the controlled object and the characteristic types included in the characteristic. The characteristic information 102 is input when the simulation unit 1020 executes a simulation. As shown in FIG. 6, the characteristic information 102 stores characteristics (e.g., product type, equipment, etc.) and characteristic types (A, B, A, B, etc.) included in each characteristic in association with each other. FIG. 6 shows, for example, that A, B, etc. are defined as characteristic types for the product type.

[0034] 7 is a diagram showing an example of a flowchart illustrating the processing procedure of the model construction processing performed by the model construction unit 1001 in the first embodiment. The processing is performed when a user issues an instruction or operates to start the processing. The model construction processing is a processing for calculating characteristic parameters based on the past operation performance data 101, and is mainly a processing for preparing for the calculation of the characteristic parameters.

[0035] In S701, the model construction unit 1001 acquires the process model 1004 and the past operation performance data 101 (S701).

[0036] In S702, the model construction unit 1001 creates a process model P using an arbitrary set initial value of the process model (S702). For example, the model construction unit 1001 creates a process model P using the initial value of the process model, y=ax 2 A process model P is created by setting the initial values ​​a=2, b=1, and c=0 to the parameters of the process model expressed as +bx+c.

[0037] In S703, the model construction unit 1001 acquires a characteristic list L from the past operation performance data 101 (S703). For example, the model construction unit 1001 acquires, as the characteristic list L, a list [product type, equipment] in which the product types and equipment defined in the past operation performance data 101 are associated with each other.

[0038] In S704, the model construction unit 1001 extracts characteristic l without restoring it from the characteristic list L acquired in S703, and acquires a characteristic type list K for characteristic l (S704). For example, the model construction unit 1001 acquires l=variety from the characteristic list L described above, and acquires a characteristic type list K=[A, B] for the variety.

[0039] In S705, the model construction unit 1001 extracts, without restoration, the characteristic type k from the characteristic type list K acquired in S704 (S705). For example, the model construction unit 1001 acquires the characteristic type k=I from the characteristic type list K=[I, II] described above.

[0040] In S706, the model construction unit 1001 calculates the characteristic parameters of the characteristic type k acquired in S705 and stores them in the characteristic parameter group 1003 (S706). For example, the model construction unit 1001 calculates the characteristic parameters for the type I. A specific method for calculating the characteristic parameters will be described later with reference to FIGS. 8A and 8B.

[0041] In S707, the model construction unit 1001 determines whether the characteristic type list K is an empty set (S707). For example, if the model construction unit 1001 is calculating characteristic parameters for the characteristic type A of the variety, it determines that the characteristic type list K = [B] exists and is therefore not an empty set (S707; No), and returns to S705. Then, the model construction unit 1001 executes S705 and S706 for the characteristic type list K = [B]. At this time, processing has been executed for each of the characteristic type lists K = [A, B]. Therefore, the model construction unit 1001 determines that the characteristic type list K is an empty set (S707; Yes), and proceeds to S708.

[0042] In S708, the model construction unit 1001 determines whether the characteristic list L is an empty set (S708). For example, if the model construction unit 1001 is processing the characteristic list L=[product type], it determines that the characteristic list L=[equipment] exists and therefore determines that it is not an empty set (S708; No), and returns to S704. Then, the model construction unit 1001 executes S704, S705, and S706 for l=[equipment]. At this time, processing has been executed for each of the characteristic lists L=[product type, equipment]. Therefore, the model construction unit 1001 determines that the characteristic list L is an empty set (S708; Yes), and ends processing.

[0043] 8A and 8B are diagrams showing an example of a flowchart illustrating the processing procedure of the characteristic parameter calculation processing performed in S706 shown in FIG. 7. The characteristic parameter calculation processing is processing for calculating characteristic parameters. A specific description will be given below using the past operation performance data 101 shown in FIG. 3. In the description using FIGS. 8A and 8B, a controlled object of two characteristics including the above-mentioned two types of characteristic types will be described as an example, and therefore, a specific description will be given in the order of loop 1 to loop 4.

[0044] In loop 1, the case where the characteristic list L=[equipment] and the characteristic type list K=[ii] are unprocessed, and the characteristic l=product type and the characteristic type k=ii are processed will be described.

[0045] In S801, the model construction unit 1001 extracts partial data D of characteristic l = characteristic type k from the past operation performance data 101 (S801). For example, the model construction unit 1001 extracts data of product type I from the past operation performance data 101, and creates partial data D consisting of data IDs (#) of 0 and 1. At this time, from the past operation performance data 101 shown in FIG. 3, records "0, I, A, [α0, β0, γ0...]" and "1, I, B, [α1, β1, γ1...]" whose data IDs (#) are identified by 0 and 1, respectively, are extracted as partial data D.

[0046] In S802, the model construction unit 1001 sets the loop variable i=0 (S802).

[0047] In S803, the model construction unit 1001 creates an empty list M of a model identification data group (S803). For example, the model construction unit 1001 creates a model identification data group M=[ ]. The model identification data group is a collection of data for identifying characteristic parameters of a process model that are corrected according to the past operation performance data 101 for each characteristic and characteristic type. The model identification data group will be described later with reference to FIGS. 9A and 9B.

[0048] In S804, the model construction unit 1001 selects the i-th data D[i] from the partial data D (S804). Here, since i=0, the model construction unit 1001 selects the record “0, I, A, [α0, β0, γ0...]” with a data ID (#) of 0 from the partial data D.

[0049] In S805, the model construction unit 1001 determines whether or not characteristic parameters other than characteristic l of characteristic type k have been calculated for the data D[i] (S805). At this point, characteristic parameters other than those of the product type, i.e., characteristics of the equipment, have not been calculated. Therefore, the model construction unit 1001 determines that characteristic parameters other than characteristic l of characteristic type k have not been calculated for the data D[i] (S805; No), and proceeds to S809.

[0050] In S806, the model construction unit 1001 uses each calculated characteristic parameter to create a process model m by correcting the process model P (S806). Here, since the determination in S805 is No as described above, the model construction unit 1001 skips this process.

[0051] In S807, [process model m, data d[i]] is added to the model identification data group M (S807). Here, since the determination in S805 is No as described above, the model construction unit 1001 skips this process.

[0052] In S808, the model constructing unit 1001 executes i=i+1 (S808).

[0053] In S809, the model construction unit 1001 determines whether i>=the number of data D (S809). Here, i=1 and the number of data D is 2, so the model construction unit 1001 determines that i>=the number of data is not true (S809; No) and returns to S804. Thereafter, the model construction unit 1001 skips the processes of S807 and S808. In the next loop, i=2, i>=2, so the model construction unit 1001 determines that i>=the number of data is true (S809; Yes) and proceeds to S810.

[0054] In S810, the model construction unit 1001 determines whether or not the list of the model identification data group M is empty (S810). Here, the model construction unit 1001 determines that the list of the model identification data group M is still empty (S810; Yes), and proceeds to S811.

[0055] In S811, the model construction unit 1001 adds [process model P, data D] to the model identification data group M. For example, the model construction unit 1001 creates a model identification data group M=[[process model P, data D]] in which the records “0, A, A, [α0, β0, γ0...]” and “1, A, B, [α1, β1, γ1...]” selected in the loop from S804 to S809 are associated with the process model P using the initial values ​​created in S702.

[0056] In S812, the model construction unit 1001 calculates correction parameters C that minimize the objective function for each model and data pair in the model identification data group M (S812). For example, the model construction unit 1001 calculates correction parameters C of the process model P that minimize the difference between the measured control amounts γ0 and γ1 of each data D and the estimated control amount of the process model P. The correction parameters C are parameters for correcting the parameters a, b, and c of the process model 1004 shown in FIG. 4, such as C={a:+1, b=-1, c=+3}.

[0057] In S813, the model construction unit 1001 stores the correction parameters calculated in S812 as characteristic parameters for characteristic l and characteristic type k (S813). For example, the model construction unit 1001 records characteristic parameters = {a: +1, b = -1, c = +3} for characteristic type "I" of characteristic "Variety". When the processing up to this point is completed, the process returns to S705, and characteristic parameters are calculated for a different characteristic type "II" for the same characteristic "Variety" and stored in the characteristic parameter group 1003 (S706, FIGS. 8A and 8B).

[0058] In the following loop 2, the characteristic list L=[device] and characteristic type list K=[] are unprocessed, and the characteristic l=product type and characteristic type k=2 are processed.

[0059] In S801, the model construction unit 1001 extracts data D of characteristic l = characteristic type k from the past operation performance data 101 (S801). For example, the model construction unit 1001 extracts data of product type B from the past operation performance data 101, and creates partial data D having a data ID (#) of 2. At this time, from the past operation performance data 101 shown in FIG. 3, a record "2, B, B, [α2, β2, γ2...]" identified by a data ID (#) of 2 is extracted as partial data D. At this time, the model construction unit 1001 reassigns an ID to the data ID (#) in order to identify the record of partial data D, and creates partial data D as a record "0, B, B, [α2, β2, γ2...]".

[0060] In S802 and S803, the model construction unit 1001 sets the loop variable i=0, as in the case of the product type I, and creates an empty list M of the model identification data group.

[0061] In S804, the model construction unit 1001 selects the i-th data D[i] from the partial data D (S804). Here, since i=0, the model construction unit 1001 selects the record “0, B, B, [α2, β2, γ2…]” with a data ID (#) of 0 from the partial data D.

[0062] In S805, the model construction unit 1001 determines whether characteristic parameters other than characteristic l of characteristic type k have been calculated for data D[i] (S805). Here, as in the case of type I, characteristic parameters for characteristics other than the type, that is, characteristics of equipment, have not yet been calculated, so the process proceeds to S809.

[0063] In S806 and S807, as in the case of type A, the determination in S805 is No as described above, so the model construction unit 1001 skips the process.

[0064] In S808, the model constructing unit 1001 executes i=i+1 (S808).

[0065] In S809, the model construction unit 1001 determines whether i>=the number of data D (S809). In this case, i=1 and the number of data D is 1, so the model construction unit 1001 determines that i>=the number of data (S809; Yes) and proceeds to S810.

[0066] In S810, the model construction unit 1001 determines whether or not the list of the model identification data group M is empty (S810). Here, the model construction unit 1001 determines that the list of the model identification data group M is still empty (S810; Yes), and proceeds to S811.

[0067] In S811, the model construction unit 1001 adds [process model P, data D] to the model identification data group M. For example, the model construction unit 1001 creates a model identification data group M=[[process model P, data D]] in which the records “0, B, B, [α2, β2, γ2...]” selected in the loop from S804 to S809 are associated with the process model P using the initial values ​​created in S702.

[0068] In S812, the model construction unit 1001 calculates correction parameters C that minimize the objective function for each model and data pair in the model identification data group M (S812). For example, the model construction unit 1001 calculates correction parameters C of the process model P that minimize the difference between the measured controlled variable γ2 of the data D and the estimated controlled variable of the process model P. The correction parameters C are parameters for correcting the parameters a, b, and c of the process model 1004 shown in FIG. 4, such as C={a:+2, b=+1, c=+10}.

[0069] In S813, the model construction unit 1001 stores the correction parameters calculated in S812 as characteristic parameters for characteristic l and characteristic type k (S813). For example, the model construction unit 1001 records characteristic parameters = {a: +2, b = +1, c = +10} for characteristic type "B" of characteristic "product type." When the processing up to this point is completed, the process returns to S704, and characteristic parameters are calculated for characteristic type "A" of a different characteristic "equipment" and stored in the characteristic parameter group 1003 (S706, FIGS. 8A and 8B).

[0070] In the following loop 3, the characteristic list L=[] and the characteristic type list K=[B] are unprocessed, and the characteristic l=device and characteristic type k=A are processed.

[0071] In S801, the model construction unit 1001 extracts data D of characteristic l = characteristic type k from the past operation performance data 101 (S801). For example, the model construction unit 1001 extracts data of equipment A from the past operation performance data 101 and creates partial data D consisting of a data ID (#) of 0. At this time, from the past operation performance data 101 shown in FIG. 3, the record "0, I, A, [α0, β0, γ0...]" identified by a data ID (#) of 0 is extracted as partial data D.

[0072] In S802 and S803, the model construction unit 1001 sets the loop variable i=0, as in the cases of types A and B, and creates an empty list M of the model identification data group.

[0073] In S804, the model construction unit 1001 selects the i-th data D[i] from the partial data D (S804). Here, since i=0, the model construction unit 1001 selects the record “0, I, A, [α0, β0, γ0...]” with a data ID (#) of 0 from the partial data D.

[0074] In S805, the model construction unit 1001 determines whether characteristic parameters other than characteristic l of characteristic type k have been calculated for the data D[i] (S805). In this case, characteristic parameters other than those for the equipment, that is, the characteristic for the type, have been calculated, so the process proceeds to S806.

[0075] In S806, the model construction unit 1001 uses each calculated characteristic parameter to create a process model m by correcting the process model P (S806). For example, the model construction unit 1001 corrects the process model P using the characteristic parameters whose characteristic is product type and whose characteristic type is I, which were stored in S813 of loop 1, to create the process model m.

[0076] In S807, the model construction unit 1001 adds [process model m, data d[i]] to the model identification data group M (S807). For example, the model construction unit 1001 creates a model identification data group M=[[process model m, data d[0](#0)]] in which the record "0, I, A, [α0, β0, γ0...]" selected in S804 is associated with the process model m created in S806. By the processing of S807, the process model m corrected with the calculated characteristic parameters is added to the list of the model identification data group M in association with the data D[i] (record "0, I, A, [α0, β0, γ0...]") being processed currently.

[0077] In S808, the model constructing unit 1001 executes i=i+1 (S808).

[0078] In S809, the model construction unit 1001 determines whether i>=the number of data D (S809). In this case, i=1 and the number of data D is 1, so the model construction unit 1001 determines that i>=the number of data (S809; Yes) and proceeds to S810.

[0079] In S810, the model construction unit 1001 determines whether the list of the model identification data group M is empty (S810). Here, the model construction unit 1001 created model identification data for the process model m in S807 and added the model identification data to the list of the model identification data group M. Therefore, the model construction unit 1001 determines that the list of the model identification data group M is not empty (S810; No), and proceeds to S812.

[0080] In S811, since the determination in S810 is No as described above, the model construction unit 1001 skips this process.

[0081] In S812, the model construction unit 1001 calculates correction parameters C that minimize the objective function for each model and data pair in the model identification data group M (S812). For example, the model construction unit 1001 calculates correction parameters C of the process model m that minimizes the difference between the measured controlled variable γ0 of the data d[0] and the estimated controlled variable of the process model m. The correction parameters C are parameters for correcting the parameters a, b, and c of the process model 1004 shown in FIG. 4, such as C={a:-1, b=+1, c=+1}.

[0082] In S813, the model construction unit 1001 stores the correction parameters calculated in S812 as characteristic parameters for characteristic l and characteristic type k (S813). For example, the model construction unit 1001 records characteristic parameters = {a:-1, b=+1, c=+1} for characteristic type "A" of characteristic "equipment". When the processing up to this point is completed, the process returns to S704, and characteristic parameters are calculated for a different characteristic type "B" for the same characteristic "equipment" and stored in the characteristic parameter group 1003 (S706, FIGS. 8A and 8B).

[0083] In the following loop 4, the case where the characteristic list L=[] and the characteristic type list K=[] are unprocessed and the characteristic l=device and characteristic type k=B are processed will be described.

[0084] In S801, the model construction unit 1001 extracts data D of characteristic l = characteristic type k from the past operation performance data 101 (S801). For example, the model construction unit 1001 extracts data of equipment B from the past operation performance data 101 and creates partial data D consisting of data IDs (#) of 1 and 2. At this time, from the past operation performance data 101 shown in FIG. 3, records “1, A, B, [α1, β1, γ1 …]” and “2, B, B, [α2, β2, γ2 …]” identified by data IDs (#) of 1 and 2 are extracted as partial data D. At this time, the model construction unit 1001 reassigns IDs for the data IDs (#) to identify the records of partial data D, and creates partial data D as records “0, A, B, [α1, β1, γ1 …]” and “1, B, B, [α2, β2, γ2 …].”

[0085] In S802 and S803, the model construction unit 1001 sets the loop variable i=0, as in the cases of product type A, B, and device A, and creates an empty list M of the model identification data group.

[0086] In S804, the model construction unit 1001 selects the i-th data D[i] from the partial data D (S804). Here, since i=0, the model construction unit 1001 selects the record “0, I, B, [α1, β1, γ1...]” with a data ID (#) of 0 from the partial data D.

[0087] In S805, the model construction unit 1001 determines whether characteristic parameters other than characteristic l of characteristic type k have been calculated for the data D[i] (S805). In this case, characteristic parameters other than those for the equipment, that is, the characteristic for the type, have been calculated, so the process proceeds to S806.

[0088] In S806, the model construction unit 1001 uses each calculated characteristic parameter to create a process model m by correcting the process model P (S806). For example, the model construction unit 1001 corrects the process model P using the characteristic parameters whose characteristic is product type and whose characteristic type is I, which were stored in S813 of loop 1, to create the process model m.

[0089] In S807, the model construction unit 1001 adds [process model m, data d[i]] to the model identification data group M (S807). For example, the model construction unit 1001 creates a model identification data group M=[[process model m, data d[0](#0)]] in which the record "0, I, B, [α1, β1, γ1...]" selected in S804 is associated with the process model m created in S806. By the processing in S807, the process model m corrected with the calculated characteristic parameters is added to the list of the model identification data group M in association with the data D[i] (record "0, I, B, [α1, β1, γ1...]") being processed currently.

[0090] In S808, the model constructing unit 1001 executes i=i+1 (S808).

[0091] In S809, the model construction unit 1001 determines whether i >= the number of data D (S809). Here, i = 1 and the number of data D is 2, so the model construction unit 1001 determines that i >= the number of data is not true (S809; No) and returns to S804. Thereafter, the model construction unit 1001 skips the processes of S806 and S807. In the next loop, i = 2 and i >= 2, so the model construction unit 1001 determines that i >= the number of data is true (S809; Yes) and proceeds to S810. When the next loop ends, the model construction unit 1001 creates a model identification data group M = [[process model m, data d[1] (#1)]] in which the record "2, B, B, [α2, β2, γ2 ...]" selected in S804 of the next loop is associated with the process model m created in S806. Finally, the model identification data group M[[process model m(i), data d[0](#0)]], [[process model m(ii), data d[1](#1)]] are created.

[0092] In S810, the model construction unit 1001 determines whether the list of the model identification data group M is empty (S810). Here, the model construction unit 1001 created model identification data for the process model m in S807 and added the model identification data to the list of the model identification data group M. Therefore, the model construction unit 1001 determines that the list of the model identification data group M is not empty (S810; No), and proceeds to S812.

[0093] In S811, since the determination in S810 is No as described above, the model construction unit 1001 skips this process.

[0094] In S812, the model construction unit 1001 calculates correction parameters C that minimize the objective function for each model and data pair in the model identification data group M (S812). For example, the model construction unit 1001 calculates correction parameters C for the process model m that minimize the difference between the measured control amount γ1 of the data d[0] and the estimated control amount of the process model m(a) and the difference between the measured control amount γ2 of the data d[1] and the estimated control amount of the process model m(b). The correction parameters C are parameters for correcting the parameters a, b, and c of the process model 1004 shown in FIG. 4, such as C={a:+0, b=+0, c=+1}.

[0095] In S813, the model construction unit 1001 stores the correction parameters calculated in S812 as characteristic parameters for characteristic l and characteristic type k (S813). For example, the model construction unit 1001 records characteristic parameters = {a:+0, b=+0, c=+1} for characteristic type "B" of characteristic "equipment." When the processing up to this point is completed, the characteristic parameter calculation processing shown in FIGS. 8A and 8B has been performed for all characteristics and all characteristic types. Therefore, the process proceeds to S707 of the model construction processing shown in FIG. 7.

[0096] In S707, since processing has already been executed for each of the characteristic type list K=[A, B], the model construction unit 1001 determines that the characteristic type list K is an empty set (S707; Yes), and proceeds to S708.

[0097] In S708, since the processing has already been executed for each of the characteristic list L=[product type, equipment], the model construction unit 1001 determines that the characteristic list L is an empty set (S708; Yes), and ends the processing.

[0098] 9A and 9B are diagrams showing an example of a flowchart illustrating the processing procedure of the model identification processing of the process model recorded in the model identification data group in the characteristic parameter calculation processing shown in FIGS. 8A and 8B. This processing is performed when called from the characteristic parameter calculation processing. For example, it is called in the processing of S812 of the characteristic parameter calculation processing. The model identification processing is processing for performing identification on the pair of the process model and data recorded in the model identification data group in the characteristic parameter calculation processing.

[0099] 8A and 8B, the model identification data group M stores a plurality of pairs of data in which a process model and data are associated with each other (e.g., [process model P, data D], [process model m, data d[i]]). In this example, the model identification data group M stores [[process model m(a), data d[0](#0)]], and [[process model m(b), data d[1](#1)]]. The model identification data group M stores a process model corrected using characteristic parameters for each calculated characteristic associated with actual values ​​of the characteristics of the controlled object included in the past operation performance data 101 used when the uncorrected process model was constructed. The data D and data d[i] are partial data of the past operation performance data 101, and as described above, actual values ​​of the reference value α, the manipulated variable β, and the controlled variable γ are recorded.

[0100] In the process described below, for each data pair, the model construction unit 1001 first corrects the process model with characteristic parameters. Next, the model construction unit 1001 calculates the difference between data (e.g., controlled variable) corresponding to the corrected process model and an output estimate of the process model (e.g., an estimate of the controlled variable obtained by inputting a reference value / manipulated variable) using an arbitrary objective function. As the arbitrary objective function, for example, RMSE (Root Mean Squared Error) can be used.

[0101] The model construction unit 1001 then finally calculates the difference between the process model and each actual value for each pair of data, and calculates the average value of the differences calculated for each pair of data. If the calculated average value satisfies a predetermined criterion, such as being sufficiently small, the model construction unit 1001 terminates the process and returns it as the final characteristic parameter. On the other hand, if the arbitrary criterion is not met, the model construction unit 1001 starts over from determining the characteristic parameter. A specific description will be given below with reference to a flowchart.

[0102] In S901, the model constructing unit 1001 selects the candidate characteristic parameters and the characteristic parameters calculated in S812 based on an arbitrary optimization algorithm (S901).

[0103] In S902, the model constructing unit 1001 sets the loop variable j=0 (S902).

[0104] In S903, the model construction unit 1001 creates an empty list V that stores the results of the objective function (S903). For example, the model construction unit 1001 creates an objective function result list V=[].

[0105] In S904, the model construction unit 1001 acquires the process model m and the data d from the j-th list M[j] recorded in the model identification data group M (S904).

[0106] In S905, the model construction unit 1001 corrects the process model m acquired in S904 with the characteristic parameters selected in S901 to acquire a process model m' (S905).

[0107] In S906, the model construction unit 1001 calculates a value v of an arbitrary objective function using the process model m' corrected in S905 and the data d corresponding to the process model m acquired in S904, and adds the value v to the objective function result list V (S906).

[0108] In S907, the model constructing unit 1001 executes j=j+1 (S907).

[0109] In S908, the model construction unit 1001 determines whether the length of the model identification data group M is greater than or equal to j (S908). That is, the model construction unit 1001 determines whether j has reached the number of records stored as the model identification data group M.

[0110] If the model construction unit 1001 determines that j has not reached the number of records stored as the model identification data group M (S908; No), the process returns to S904 and repeats the subsequent processes. On the other hand, if the model construction unit 1001 determines that j has reached the number of records stored as the model identification data group M (S908; Yes), the process proceeds to S909.

[0111] In S909, the model construction unit 1001 calculates the average value a of the values ​​v of any objective function recorded in the objective function result list V in S906, and evaluates the calculated average value a by comparing it with a predetermined standard (S909).

[0112] In S910, the model construction unit 1001 determines whether the calculated average value a satisfies a predetermined standard (S910). If the model construction unit 1001 determines that the calculated average value a does not satisfy the predetermined standard (S910; No), the process returns to S901 and the characteristic parameters are selected again.

[0113] In S911, if the model construction unit 1001 determines that the calculated average value a satisfies a predetermined standard (S910; Yes), it determines that the characteristic parameter selected in S901 is the final characteristic parameter and outputs it (S911).

[0114] 7 to 9B, a new process model can be constructed by correcting the parameters of the process model constructed by the theoretical formula for each characteristic based on the past operation performance data, and the dynamic characteristics of the controlled object can be expressed without creating a process model for each combination of characteristics. Therefore, even for a controlled object with a huge number of characteristic combinations, it becomes possible to perform a process simulation using a small number of process models.

[0115] 10 is a diagram showing an example of a flowchart illustrating the procedure of the simulation processing performed by the simulation unit 1002 in the first embodiment. The processing is performed when a user issues an instruction or operates to start the processing. The simulation processing is a processing for performing a process simulation using the process model constructed in the model construction processing performed by the model construction unit 1001.

[0116] In S1001, the simulation unit 1002 acquires characteristic parameters that match the input characteristic information 102 from the characteristic parameter group 1003 stored in FIGS. 7 to 9B (S1001).

[0117] In S1002, the simulation unit 1002 corrects the process model P using the acquired characteristic parameters to create a process model P' (S1002).

[0118] In S1003, the simulation unit 1002 inputs the simulation input information 103 into the process model P' created in S1002, and executes a process simulation of the controlled object using the process model P' (S1003).

[0119] In S1004, the simulation unit 1002 returns the results of the process simulation (S1004).

[0120] As described above, in this embodiment, a process model construction system for constructing a process model in which characteristics of a control object are modeled by a computer includes: a first processing unit (e.g., model construction unit 1001, FIG. 7) that acquires the characteristics (e.g., product type, equipment, etc.) from past operation result data (e.g., past operation result data 101) that indicates operation results according to the characteristics of the control object; and a second processing unit (e.g., model construction unit 1001, FIG. 8) that calculates, for each characteristic, characteristic parameters (e.g., characteristic parameters stored in the characteristic parameter group 1003 shown in FIG. 5) for correcting the process model such that an actual value of the process of the control object for the characteristics included in the past operation result data (e.g., controlled variable of the control object) and an estimated value of the process of the control object estimated by the process model constructed using a predetermined theoretical formula (e.g., the theoretical formula shown in FIG. 4) satisfy a predetermined condition (e.g., a condition that a difference between an actually measured controlled variable and an estimated controlled variable of the process model is minimized). This makes it possible to construct a process model that imposes less construction and operational burden than conventional methods, even when the controlled object has multiple characteristics, and also reduces the number of process models that need to be constructed to perform process simulation.

[0121] The system also includes a third processing unit (e.g., simulation unit 1002) that creates a corrected process model using the characteristic parameters of the same characteristics as characteristic information (e.g., characteristic information 102) that indicates the characteristics of the control object and the characteristic types included in the characteristics and is input to the process model creation system, and simulates the behavior of the process of the control object using the created process model.As a result, even for a control object with an enormous number of combinations of characteristics and characteristic types, process simulation can be performed using a small number of process models created by the model creation unit 1001.

[0122] Although the first embodiment has been specifically described above, the following modifications are possible depending on the environment in which the system is used.

[0123] For example, the model constructor 1001 may correct the parameters of the process model in various ways.

[0124] In the above-described embodiment, the model construction unit 1001 calculates characteristic parameters for correcting the parameters of the process model constructed using the theoretical formula for each characteristic of the controlled object based on past operational performance data. However, the model construction unit 1001 may calculate statistical values ​​based on the calculated characteristic parameters and correct the parameters of the process model constructed using the theoretical formula using statistical characteristic parameters based on the calculated statistical values. Examples of the statistical values ​​include statistical values ​​obtained by the model construction unit 1001 performing processes such as correcting the characteristic parameters stored in the characteristic parameter group 1003 using a weighted linear sum, correcting the characteristic parameters using a weighted multiplication, or correcting the characteristic parameters using a correction factor for the initial parameters of the process model. The weights and correction factors may be determined in advance by the user.

[0125] In this way, the second processing unit (e.g., model construction unit 1001, FIG. 8) calculates statistical values ​​based on the characteristic parameters calculated for each of the characteristics, and corrects the process model constructed by the predetermined theoretical formula using the statistical characteristic parameters based on the statistical values. This makes it possible to correct the parameters of the process model using the characteristic parameters corrected by each of the above-mentioned methods, which are specified in advance by the user.

[0126] Alternatively, the initial values ​​of the parameters of the process model may be identified and determined from past operational performance data.

[0127] In the above-described embodiment, the initial values ​​of the parameters of the process model are assumed to be given in advance. However, for example, the model construction unit 1001 may set the initial values ​​of the parameters of the process model that have been corrected using characteristic parameters identified from past operation performance data and obtained by the model identification process shown in Figures 9A and 9B.

[0128] In this way, the second processing unit (e.g., model construction unit 1001, FIG. 8) creates model identification data that associates the process model corrected with the characteristic parameters with actual values ​​included in the past operation performance data used to construct the uncorrected process model, and performs model identification processing (e.g., the model identification processing shown in FIGS. 9A and 9B) that performs identification on a pair of the process model corrected with the characteristic parameters included in the created model identification data and the actual values, and the first processing unit sets the parameters of the process model corrected with the characteristic parameters obtained by the model identification processing as initial values ​​of the parameters of the process model. This makes it possible to automatically set initial values ​​of the parameters of the process model based on the past operation performance data without the user having to specify them in advance.

[0129] Alternatively, the characteristic parameters may be manually specified by the user.

[0130] In the above-described embodiment, the model construction unit 1001 calculates characteristic parameters for correcting the parameters of the process model constructed using the theoretical formula for each characteristic of the controlled object based on past operational performance data. However, the model construction unit 1001 may also store characteristic parameters specified by the user in the characteristic parameter group. Alternatively, the model construction unit 1001 may store some of the calculated characteristic parameters (e.g., {a:+0, b=+0} of characteristic parameters={a:+0, b=+0, c=+1}) and characteristic parameters specified by the user (e.g., {c=+1} of characteristic parameters={a:+0, b=+0, c=+1}) in the characteristic parameter group.

[0131] In this way, the process model construction system 1000 has an input unit (e.g., input device 1606) that accepts input of the characteristic parameters, and the second processing unit (e.g., model construction unit 1001, FIG. 8) sets at least some of the characteristic parameters to the parameters input from the input unit and performs the calculations for parameters other than the set parameters. This makes it possible to directly store characteristic parameters that reflect the user's intention.

[0132] As described above, this system can be applied to various plants that perform process operations. In this case, the first processing unit (e.g., the model construction unit 1001, FIG. 7) acquires a first device and a first product type that indicate the characteristics of the controlled object (e.g., the characteristics of a reactor) from the past operational performance data. The second processing unit (e.g., the model construction unit 1001, FIG. 8) calculates, for each of the first device and the first product type, characteristic parameters that make the actual value of the controlled object process (e.g., the controlled variable of the reactor) and the estimated value of the controlled object process for the first device and the first product type satisfy a predetermined condition (e.g., the condition that the difference between the measured controlled variable of the reactor and the estimated controlled variable of the process model is minimized). The system corrects the process model when generating the first product type in the first device based on at least the calculated characteristic parameters. This allows various plants that perform process operations to build process models that require less construction and operation load than conventional systems, and also reduces the number of process models built for process simulation. [Example]

[0133] In the second embodiment, a case will be described in which the results of a simulation by the simulation unit 1002 are used to back-calculate an operation method of the controlled object that satisfies the optimum operating conditions, and the results are fed back to the controlled object.

[0134] FIG. 11 is a diagram showing an example of the configuration of a process model construction system according to the second embodiment. As shown in FIG. 11, a process model construction system 2000 according to this embodiment further includes a control device input calculation unit 2001 in addition to the configuration of the process model construction system 1000 according to the first embodiment. The process model construction system 2000 also receives input of desired operating condition data 106 for a controlled object designated by a user. As in the first embodiment, the process model construction system 2000 shown in FIG. 11 can be realized by, for example, a general computer 1600 as hardware, as shown in FIG. 2 (schematic diagram of a computer). In the following, the same components as those in the process model construction system 1000 according to the first embodiment are denoted by the same reference numerals, and their description will be omitted.

[0135] The control device input calculation unit 2001 is a processing unit that calculates, by inverse calculation, input values ​​to the control object 105 that satisfy the desired operating condition data 106 for a given control object. The control device input calculation unit 2001 uses an arbitrary optimization algorithm to calculate input values ​​to the control object 105 through trial and error, performing an optimization calculation to find input values ​​that match the desired operating conditions. For example, the control device input calculation unit 2001 sequentially re-executes the optimization calculation using feedback results of control variables obtained from the control object 105 to calculate optimal input values ​​in accordance with the operating status of the control object 105. Examples of input values ​​to the control object 105 include time-series data of manipulated variables, reference values, and controlled variables (or a data set combining some or all of these). Furthermore, examples of the desired operating condition data 106 include exemplary operational performance data selected by a user from the operational performance data 301 of the past operational performance data 101 shown in FIG. 3 .

[0136] As described above, this embodiment includes a fourth processing unit (e.g., a control device input calculation unit 2001) that calculates input values ​​for the control object (e.g., time-series data of manipulated variables, reference values, and controlled variables (or a data set combining some or all of these)) using a predetermined algorithm (e.g., an arbitrary optimization algorithm) and feedback results of controlled variables for the control object obtained from the control object, and performs optimization calculations to find input values ​​that match desired operating conditions for the control object (e.g., operating condition data 106). That is, the control device input calculation unit 2001 recalculates the input values ​​for the control object that satisfy the desired operating conditions each time based on the feedback results obtained from the control object, so that it is possible to calculate input values ​​that satisfy optimal operating conditions for the control object at the current time. Therefore, as in the first embodiment, even when there are many types and combinations of characteristics, it is possible to back-calculate manipulated variables that satisfy optimal operating conditions. [Example]

[0137] In the third embodiment, a case will be described in which characteristic parameters are estimated even for unknown characteristics (for example, product type or equipment) and a process simulation is performed.

[0138] FIG. 12A is a diagram showing an example of the configuration of a process model construction system according to the third embodiment. As shown in FIG. 12A, a process model construction system 3000 according to this embodiment includes, in addition to the configuration of the process model construction system 1000 according to the first embodiment, a characteristic parameter model construction unit 3001, a simulation unit 3002 different from those in the first and second embodiments, and a characteristic parameter model 3003. The process model construction system 3000 also accepts input of characteristic additional information 107. As in the first and second embodiments, the process model construction system 3000 shown in FIG. 12A can be realized by, for example, a general computer 1600 as hardware, as shown in FIG. 2 (schematic diagram of a computer). In the following, the same components as those in the process model construction system 1000 and the process model construction system 2000 according to the first and second embodiments are denoted by the same reference numerals, and their description will be omitted.

[0139] The characteristic parameter model construction unit 3001 is a processing unit that constructs a machine learning model that estimates characteristic parameters from the characteristic additional information 107, using the characteristic parameter group 1003 and the characteristic additional information 107. For example, when the characteristic parameter model construction unit 3001 knows factors (e.g., the ratio of raw materials) that affect a certain characteristic (e.g., the variety manufactured by the controlled object), it acquires the variety and the ratio of raw materials as the characteristic additional information 107. The characteristic parameter model construction unit 3001 constructs a characteristic parameter model that serves as a parameter estimator using any machine learning method or the like, with the acquired characteristic additional information 107 as an explanatory variable and the characteristic parameters as a target variable. This makes it possible to estimate characteristic parameters even for unknown characteristic types and estimate the dynamic characteristics of a process model.

[0140] The characteristic additional information 107 is data that is referenced when constructing the above-mentioned characteristic parameter model or when estimating characteristic parameters of unknown characteristics.

[0141] Fig. 12B is a diagram showing an example of characteristic additional information 107. As shown in Fig. 12B, characteristic additional information 107 stores unknown characteristics (e.g., an unknown product type manufactured by the controlled object) in association with feature information corresponding to the characteristic (e.g., raw materials of the product type manufactured by the controlled object). Fig. 12B shows, for example, that the breakdown of raw materials for product type I manufactured by the controlled object is 30% raw material 1, ..., 20% raw material k. Here, raw materials of the product type are exemplified, but various information representing the characteristics of the characteristics, such as setting values ​​of equipment, may be defined as characteristic additional information.

[0142] When correcting the process model with characteristic parameters that match the characteristics of the characteristic information 102, the simulation unit 3002 estimates characteristic parameters for unknown characteristic types (such as a product type with no past operational history) using the characteristic additional information 107 and the characteristic parameter model described above. For example, in S1001 shown in Fig. 10, the simulation unit 3002 inputs the input characteristic information 102 into the characteristic parameter model constructed using the characteristic additional information 107, and acquires estimated values ​​of characteristic parameters that match the characteristic information 102. Furthermore, in S1002, the simulation unit 3002 corrects the process model using the acquired estimated values ​​of the characteristic parameters, and thereafter performs simulation in the same manner as in S1003 and S1004.

[0143] As described above, this embodiment includes a fifth processing unit (e.g., characteristic parameter model construction unit 3001) that constructs a characteristic parameter model that estimates the characteristic parameters for the characteristic by machine learning using characteristic information (e.g., characteristic additional information 107) that associates the characteristic of the controlled object (e.g., an unknown product type manufactured by the controlled object) with an element that influences the characteristic (e.g., characteristic information of raw materials of the product type manufactured by the controlled object) as an explanatory variable and the characteristic parameters that correct the process model of the controlled object for the characteristic as an objective variable, and the third processing unit (e.g., simulation unit 3002) simulates the behavior of the process of the controlled object using the process model corrected using the estimated value of the characteristic parameter obtained by inputting the characteristic information into the characteristic parameter model. That is, the characteristic parameter model construction unit 3001 constructs a characteristic parameter model using the characteristic additional information 107 (for example, if the characteristic is a variety, the raw material ratio of that variety, etc.) and the characteristic parameter group 1003, and the simulation unit 3002 corrects the process model using estimated values ​​of the characteristic parameters obtained by inputting the characteristic information 102 into the constructed characteristic parameter model, and the simulation unit 3002 performs a simulation using the corrected process model. Therefore, it is possible to estimate characteristic parameters and perform process simulation even for unknown varieties or equipment. In other words, it becomes possible to estimate dynamic characteristics and perform process simulation even for unknown types of characteristics or combinations of characteristics. [Example]

[0144] In the fourth embodiment, for a control target having a small amount of past operational performance data, the number of combinations of characteristics and characteristic types is small, and a contradiction may occur in the characteristic parameters in a specific combination. In the following, a method for resolving the contradiction when a contradiction occurs in the characteristic parameters will be described.

[0145] Fig. 13A is a diagram showing an example of the configuration of a process model construction system according to a fourth embodiment. As shown in Fig. 13A, a process model construction system 4000 according to this embodiment includes, in addition to the configuration of the process model construction system 1000 according to the first embodiment, model identification information 4001 and a model construction unit 4002 that is different from those of the first to third embodiments. As in the first to third embodiments, the process model construction system 4000 shown in Fig. 13A can be realized by, for example, a general computer 1600 as hardware, as shown in Fig. 2 (schematic diagram of a computer). In the following, the same components as those of the process model construction system 1000, the process model construction system 2000, and the process model construction system 3000 according to the first to third embodiments are denoted by the same reference numerals, and their description will be omitted.

[0146] 9A and 9B, and is a set of data for identifying a characteristic model of a process model to be corrected according to the past operation performance data 101 for each characteristic or characteristic type. In the first embodiment, characteristic parameters were calculated for all parameters of the process model as the characteristic parameter group 1003 shown in Fig. 5, but in this embodiment, only the parameters of the process model corresponding to the characteristic or characteristic type are stored as characteristic parameters for that characteristic or characteristic type.

[0147] 13B is a diagram showing an example of model identification information 4001. As shown in FIG. 13B, the model identification information 4001 stores one or more characteristic parameters of a process model corresponding to a characteristic or characteristic type. In FIG. 13B, for example, correction parameters {a: +1.2, b=-0.1}, which are characteristic parameters for type A, and correction parameters {c=0}, which are characteristic parameters for type B, are stored in association with the respective characteristic types. Here, of the parameters a, b, and c that constitute the characteristic parameters, the characteristic parameter for type A is associated with a and b, and the characteristic parameter for type B is associated with c and stored.

[0148] Next, the model identification process in this embodiment will be described. In the fourth embodiment, only the step corresponding to S901 differs from the model identification process shown in the first embodiment (FIGS. 9A and 9B). Therefore, the flowchart will be omitted and only the parts that differ from S901 will be described.

[0149] 9A and 9B, the model construction unit 4002 selects, from the characteristic parameters stored in S813, characteristic parameters that correspond to the characteristics and characteristic types determined in the model identification information 4001, based on an arbitrary optimization algorithm. The characteristic parameters that correspond to the characteristics and characteristic types can be selected by referring to the model identification information 4001. The model identification information 4001 may be set in advance by the user by referring to the characteristic parameters stored in S813.

[0150] After selecting the characteristic parameters determined according to the characteristic and characteristic type as described above, the model construction unit 4002 executes the processes from S902 onward. At this time, in S905, the model construction unit 4002 acquires a process model m' by correcting the process model m acquired in S904 with the characteristic parameters according to the characteristic and characteristic type selected in the step corresponding to S901 in the fourth embodiment described above.

[0151] As described above, in this embodiment, the process model construction system 4000 has model identification information (e.g., model identification information 4001) storing the characteristic parameters of the process model corresponding to one or more characteristics, and the second processing unit (e.g., model construction unit 4002) creates model identification data in which the process model corrected using the characteristic parameters calculated for each of the characteristics corresponds to an actual value included in the past operation performance data 101 used to construct the uncorrected process model, and executes a model identification process (e.g., the model identification process shown in FIGS. 9A and 9B ) in which identification is performed on a pair of the process model corrected using the characteristic parameters included in the created model identification data and the actual value, and in the model identification process, the characteristic parameters defined in the model identification information are selected based on a predetermined algorithm (e.g., an arbitrary optimization algorithm), and the process model is corrected using the selected characteristic parameters.

[0152] That is, in this embodiment, in addition to the configuration of the first embodiment, only the parameters of the process model corresponding to the characteristic parameters are corrected using characteristic parameters according to the characteristics and characteristic types defined in the model identification information 4001. As a result, even when there is little data on combinations of characteristics and characteristic types and a contradiction occurs in the characteristic parameters for a specific combination, it is possible to obtain an output value of the process model with high accuracy and to suppress an increase in prediction error of the process model.

[0153] In the above example, characteristic parameters according to the characteristics and characteristic types are defined as model identification information 4001. However, the model construction unit 4002 may set a target value for the objective function value v calculated during model identification in S906, for example, by adopting a percentage error for the objective function, and setting the target to identify up to a percentage error of N% during model identification of product characteristics and to identify with a percentage error of 0% during model identification of equipment characteristics. This makes it possible to obtain the output value of the process model with high accuracy, as above, without defining model identification information 4001. [Example]

[0154] In the fifth embodiment, a method for correcting a process model when a simulation is performed and a deviation occurs between the behavior of the actual process and the behavior of the process model will be described.

[0155] Fig. 14 is a diagram showing an example of the configuration of a process model construction system according to Example 5. As shown in Fig. 14, a process model construction system 5000 according to this example has a simulation unit 5001 that is different from the configuration of the process model construction system 1000 according to Example 1 and that differs from those of Examples 1 to 4. Furthermore, the process model construction system 5000 receives as input a feedback result of a controlled variable obtained from a controlled object 1401.

[0156] As in the first to fourth embodiments, the process model construction system 5000 shown in Fig. 14 can be realized by, for example, a general computer 1600 as hardware, as shown in Fig. 2 (schematic diagram of a computer). In the following, the same components as those in the process model construction system 1000, process model construction system 2000, process model construction system 3000, and process model construction system 4000 in the first to fourth embodiments are assigned the same reference numerals, and their description will be omitted.

[0157] The simulation unit 5001 compares the simulation result with the control result of the controlled object 1401 (for example, the control amount obtained from the controlled object 1401) and determines whether the difference therebetween is equal to or greater than a certain value temporarily (for example, for 10 seconds) or cumulatively (for example, for one month). If the simulation unit 5001 determines that the difference therebetween is equal to or greater than a certain value temporarily or cumulatively, the simulation unit 5001 corrects the simulation result and recalculates it, and stores the result in the simulation result 104. As a method for correcting the simulation result, for example, the simulation unit 5001 may identify parameters of a process model that minimize the difference between the control result of the controlled object 1401 and the simulation result, or may reselect characteristic parameters that satisfy such a condition from a group of characteristic parameters.

[0158] 15 is a diagram showing an example of a flowchart illustrating the procedure of the simulation processing performed by the simulation unit 5001 in the fifth embodiment. The processing is performed when a user issues an instruction or operates to start the processing. The simulation processing is a processing for performing a process simulation using the process model constructed in the model construction processing performed by the model construction unit 1001, as in the first embodiment.

[0159] Since the processes in S1501 and S1502 are the same as those in S1001 and S1002 shown in FIG. 10, only S1503 and subsequent steps will be explained.

[0160] In S1503, the simulation unit 5001 inputs the simulation input information 103 into the process model P' created in S1502, executes a simulation of the control target using the process model P', and obtains the result S of the executed simulation (S1503).

[0161] In S1504, the simulation unit 5001 acquires the actual measurement value sequence T from the control object 1401 (S1504). The actual measurement value sequence T is time-series data included in the operation performance data, such as actual measurement control amounts γ0, γ1, γ2, etc.

[0162] In S1505, the simulation unit 5001 determines whether the difference between the simulation result S and the actual measurement value sequence T is equal to or greater than an arbitrary standard that serves as a predetermined threshold (S1505). If the simulation unit 5001 determines in S1505 that the difference between the simulation result S and the actual measurement value sequence T is not equal to or greater than the arbitrary standard that serves as a predetermined threshold (S1505; No), the process proceeds to S1507. On the other hand, if the simulation unit 5001 determines in S1505 that the difference between the simulation result S and the actual measurement value sequence T is equal to or greater than an arbitrary standard that serves as a predetermined threshold (S1505; Yes), the process proceeds to S1506.

[0163] In S1506, the simulation unit 5001 corrects the characteristic parameters of the process model used in S1502 so that the difference between the results of the simulation of the control object 1401 using the process model and the actual measurement value sequence T of the control object 1401 is minimized, and then performs the simulation again (S1506).

[0164] In S1507, the simulation unit 5001 returns the result of the simulation or the result of the re-executed simulation (S1507).

[0165] Thus, in this embodiment, when the difference between the result of the simulation and the control result of the controlled object exceeds a predetermined threshold, the third processing unit (e.g., simulation unit 5001) corrects the characteristic parameters of the process model so as to reduce the difference, and re-executes the simulation.

[0166] That is, the simulation unit 5001 checks the difference between the result of the process simulation and the actual controlled variable output by the controlled object, and if the difference exceeds a certain amount, corrects the characteristic parameters of the process model so that the waveforms of the simulation results up to that point and the actual controlled variable become closer. This makes it possible to perform process simulation using a small number of process models even for a controlled object with an enormous number of combinations of characteristics and characteristic types, and also makes it possible to re-identify the characteristic parameters of the process model based on the actual measured values ​​obtained from the controlled object if a deviation occurs between the simulated results and the actual measured values ​​of the controlled object after a certain period of simulation.

[0167] In the above example, the simulation unit 5001 corrects and optimizes the characteristic parameters so that the difference between the result of the simulation of the controlled object 1401 using the process model and the actual measurement value sequence T of the controlled object 1401 is minimized. However, the simulation may be performed again by correcting any characteristic parameter selected by the user from the group of characteristic parameters. [Example]

[0168] In the sixth embodiment, a case where a machine learning model is used for a process model and characteristic parameters will be described.

[0169] Fig. 16 is a diagram showing an example of the configuration of a process model construction system according to Example 6. As shown in Fig. 16, a process model construction system 6000 according to this example has a model construction unit 6001 and a simulation unit 6002 that are different from those in Examples 1 to 5 in comparison with the configuration of the process model construction system 1000 according to Example 1, and has a characteristic model group 6003 instead of the characteristic parameter group 1003.

[0170] Similar to the first to fifth embodiments, the process model construction system 6000 shown in Fig. 16 can be realized by, for example, a general computer 1600 as hardware, as shown in Fig. 2 (schematic diagram of a computer). In the following, the same components as those in the process model construction system 1000, process model construction system 2000, process model construction system 3000, process model construction system 4000, and process model construction system 5000 in the first to fifth embodiments are assigned the same reference numerals, and their description will be omitted.

[0171] The model construction unit 6001 is a processing unit that constructs the process model 1004 and the characteristic model group 6003 based on the past operation performance data 101 using machine learning.

[0172] The simulation unit 6002 is a processing unit that calls the process model 1004 and the characteristic model group 6003 and executes a simulation based on the input characteristic information 102 and simulation input information 103. The characteristic model group 6003 is a collection of characteristic models that are constructed for each characteristic based on the difference between the process model constructed by machine learning and past operational performance data.

[0173] 17 is a diagram showing an example of a flowchart illustrating the processing procedure of the model construction processing performed by the model construction unit 6001 in the sixth embodiment. This processing is performed when a user issues an instruction or operates to start the processing. The model construction processing is processing for calculating a characteristic model based on the past operation performance data 101, and is mainly processing for preparing to construct the characteristic model. In the following description, the process model will be described as being average value data of the past operation performance data 101.

[0174] In S1701, the model construction unit 6001 acquires the process model 1004 and the past operation performance data 101 (S1701).

[0175] In S1702, the model construction unit 6001 constructs a process model P from the average values ​​of the past operation performance data 101 (S1702). For example, the model construction unit 6001 calculates average values ​​of the reference values, manipulated variables, and controlled variables, which are time-series data included in the operation performance data accumulated as the past operation performance data 101, and creates the calculated average value data as the process model P. The process model P is an initial machine learning model for constructing a machine learning model, which will be described later with reference to FIGS. 18A and 18B.

[0176] In S1703, the model building unit 6001 acquires the characteristic list L from the past operation performance data 101, as in the first embodiment.

[0177] In S1704, the model constructing unit 6001 extracts the characteristic l without restoration from the characteristic list L acquired in S1703, and acquires the characteristic type list K of the characteristic l (S1703, S1704), as in the first embodiment.

[0178] In S1705, the model construction unit 6001 extracts, without restoration, the characteristic type k from the characteristic type list K acquired in S1704, as in the first embodiment (S1705).

[0179] In S1706, the model construction unit 6001 calculates a characteristic model for the characteristic type k acquired in S1705 and stores it in the characteristic model group 6003 (S1706). For example, the model construction unit 6001 calculates a characteristic model for the product type I. A specific method for calculating the characteristic model will be described later with reference to FIGS. 18A and 18B.

[0180] In S1707, the model construction unit 6001 determines whether the characteristic type list K is an empty set (S1707). For example, if the model construction unit 6001 is calculating a characteristic model for the characteristic type I of the variety, it determines that the characteristic type list K = [II] exists and is therefore not an empty set (S1707; No), and returns to S1705. The model construction unit 6001 then executes S1705 and S1706 for the characteristic type list K = [II]. At this time, as in the first embodiment, processing has been executed for each of the characteristic type lists K = [I, II]. Therefore, the model construction unit 6001 determines that the characteristic type list K is an empty set (S1707; Yes), and proceeds to S1708.

[0181] In S1708, the model construction unit 6001 determines whether the characteristic list L is an empty set (S1708). For example, if the model construction unit 6001 is processing the characteristic list L=[product type], it determines that the characteristic list L=[equipment] exists and therefore determines that it is not an empty set (S1708; No), and returns to S1704. Then, the model construction unit 6001 executes S1704, S1705, and S1706 for l=[equipment]. At this time, as in the first embodiment, processing has been executed for each of the characteristic lists L=[product type, equipment]. Therefore, the model construction unit 6001 determines that the characteristic list L is an empty set (S1708; Yes), and ends processing.

[0182] Figures 18A and 18B are diagrams showing an example of a flowchart illustrating the processing procedure of the characteristic model calculation processing performed in S1706 shown in Figure 17. The characteristic model calculation processing is processing for constructing a characteristic model.

[0183] In the characteristic model calculation process, the model construction unit 6001 extracts data D whose characteristic type matches the characteristic of the characteristic model to be constructed from the past operation performance data 101. As will be described below, the data D and data d[i] are partial data of the past operation performance data 101, as in the first embodiment, and as has been described above, the actual values ​​of the reference value α, the manipulated variable β, and the controlled variable γ are recorded.

[0184] If a characteristic model for another characteristic has not been constructed, the model construction unit 6001 creates differential data between the data D and the output estimate of the process model P (e.g., an estimated value of a controlled variable obtained by inputting a reference value / manipulated variable), and constructs a machine learning model using this as the objective variable. As an arbitrary objective function, for example, RMSE can be used. If the operation performance data included in the past operation performance data 101 is time series data of a controlled variable, the process model P is average time series data of the controlled variable. The model construction unit 6001 creates differential data d between the time series data of each controlled variable in the data D and the average time series data, and constructs a time series prediction model for the set of data d. Regarding explanatory variables, the actual controlled variable may be obtained from the controlled object, and data for the first window frame may be provided, or data D may be expanded and dummy data for one window frame may be inserted. Alternatively, additional information may be provided to the past operation performance data 101.

[0185] On the other hand, if another characteristic model has already been constructed, the model construction unit 6001 subtracts the output of the constructed model when calculating the difference between each data D and the process model P, constructs a characteristic model using the data D, and stores the constructed characteristic model in the characteristic model group 6003.

[0186] A specific description will be given below using the past operation performance data 101 shown in Fig. 3. In the description using Figs. 18A and 18B, as in the first embodiment, a controlled object having two characteristics including two types of characteristic types will be described as an example, and therefore, a specific description will be given in the order of loop 1 to loop 4. As described above, the description will be given on the premise that the process model is a model identified using all of the past operation performance data 101, or time-series data of the average value of the controlled variable constructed from the average value of the controlled variable at each time.

[0187] In loop 1, the case where the characteristic list L=[equipment] and the characteristic type list K=[ii] are unprocessed, and the characteristic l=product type and the characteristic type k=ii are processed will be described.

[0188] In S1801, similarly to the first embodiment, the model construction unit 6001 extracts partial data D of characteristic l = characteristic type k from the past operation performance data 101 (S801). For example, from the past operation performance data 101 shown in Fig. 3, records "0, I, A, [α0, β0, γ0...]" and "1, I, B, [α1, β1, γ1...]" whose data IDs (#) are identified by 0 and 1, respectively, are extracted as partial data D.

[0189] In S1802 and S1803, the model construction unit 6001 sets the loop variable i=0 and creates an empty list M of the model identification data group (S1802, S1803), as in the first embodiment. The model identification data group is subjected to the same processing as the model identification processing shown in Figures 9A and 9B.

[0190] In S1804, the model construction unit 6001 selects the i-th data D[i] from the partial data D (S1804), as in the first embodiment. Here, since i=0, the model construction unit 1001 selects the record “0, I, A, [α0, β0, γ0...]” with a data ID (#) of 0 from the partial data D.

[0191] In S1805, the model construction unit 6001 determines whether or not a characteristic model other than the characteristic l of the characteristic type k has been constructed for the data D[i] (S1805). At this point, the model construction unit 1001 determines that a characteristic model other than the characteristic l of the characteristic type k has not been constructed for the data D[i] (S1805; No), and proceeds to S1809.

[0192] In S1806, the model construction unit 6001 creates differential data g between the value obtained by adding the output of each constructed characteristic model to the output of the process model P and the data d[i] (S1806). Here, since the determination in S1805 is No as described above, the model construction unit 6001 skips this process.

[0193] In S1807, the differential data g is added to the model identification data group (S1807). Here, as described above, the determination in S805 is No, so the model construction unit 6001 skips this process.

[0194] In S1808, the model constructing unit 6001 executes i=i+1 (S1808).

[0195] In S1809, the model construction unit 6001 determines whether i >= the number of data D (S1809). In this case, i = 1 and the number of data D is 2, so the model construction unit 6001 determines that i >= the number of data is not true (S1809; No) and returns to S1804. Thereafter, the model construction unit 1001 skips the processes of S807 and S808. In the next loop, i = 2, i >= 2, so as in the first embodiment, the model construction unit 6001 determines that i >= the number of data is true (S1809; Yes) and proceeds to S1810.

[0196] In S1810, the model construction unit 6001 determines whether or not the list of the model identification data group M is empty (S1810). Here, the model construction unit 6001 determines that the list of the model identification data group M is still empty (S1810; Yes), and proceeds to S1811.

[0197] In S1811, the model construction unit 6001 adds differential data g between the output of the process model P and the controlled variable of the data D to the model identification data group M (S1811). For example, the model construction unit 6001 creates a model identification data group M=[data g] which is the difference between the output value of the process model P using the initial value created in S1702 and each of the records “0, I, A, [α0, β0, γ0...]” and “1, I, B, [α1, β1, γ1...]” selected in the loop from S1804 to S1809.

[0198] In S1812, the model construction unit 6001 constructs a regression model s using machine learning for the data set of the model identification data group (S1812). For example, the model construction unit 6001 constructs the regression model s using the reference value / operation amount of the data D as an explanatory variable and the control amount of the data D as a response variable.

[0199] In S1813, the model construction unit 6001 stores the regression model s calculated in S1812 as a trait model for trait l and trait type k (S1813). For example, the model construction unit 6001 records a trait model for trait type "I" of trait "Variety". When the processing up to this point is completed, the process returns to S1705, and a trait model is constructed for a different trait type "B" for the same trait "Variety", and the constructed trait model is stored in the trait model group 6003 (S1706, FIGS. 18A and 18B).

[0200] In the following loop 2, the characteristic list L=[device] and characteristic type list K=[] are unprocessed, and the characteristic l=product type and characteristic type k=2 are processed.

[0201] In S1801, the model construction unit 6001 extracts data D of characteristic l = characteristic type k from the past operation performance data 101 (S1801). For example, the model construction unit 6001 extracts data of product type B from the past operation performance data 101, and creates partial data D consisting of a data ID (#) of 2. At this time, from the past operation performance data 101 shown in FIG. 3, a record "2, B, B, [α2, β2, γ2...]" identified by a data ID (#) of 2 is extracted as partial data D. At this time, the model construction unit 1001 reassigns IDs and creates partial data D as a record "0, B, B, [α2, β2, γ2...]" as in the first embodiment.

[0202] In S1802 and S1803, the model construction unit 6001 sets the loop variable i=0, as in the case of product type I, and creates an empty list M of the model identification data group.

[0203] In S1804, the model construction unit 6001 selects the i-th data D[i] from the partial data D (S1804). Here, since i=0, the model construction unit 1001 selects the record “0, B, B, [α2, β2, γ2…]” with a data ID (#) of 0 from the partial data D.

[0204] In S1805, the model construction unit 6001 determines whether characteristic models have been constructed for the data D[i] other than characteristic l of characteristic type k (S1805). Here, as in the case of type I, characteristic models have not yet been constructed for characteristics other than the type, i.e., characteristics of the equipment, so the process proceeds to S1809.

[0205] In S1806 and S1807, as in the case of type A, the determination in S1805 is No as described above, so the model construction unit 6001 skips the process.

[0206] In S1808, the model constructing unit 6001 executes i=i+1 (S1808).

[0207] In S1809, the model construction unit 6001 determines whether i>=the number of data D (S1809). In this case, i=1 and the number of data D is 1, so the model construction unit 6001 determines that i>=the number of data (S1809; Yes) and proceeds to S1810.

[0208] In S1810, the model construction unit 1001 determines whether or not the list of the model identification data group M is empty (S1810). Here, the model construction unit 6001 determines that the list of the model identification data group M is still empty (S1810; Yes), and proceeds to S1811.

[0209] In S1811, the model construction unit 1001 adds difference data g between the output value of the process model P and the controlled variable of the data D to the model identification data group M (S1811). For example, the model construction unit 6001 creates a model identification data group M=[data g] which is the difference between the output value of the process model P using the initial value created in S1702 and each of the records “0, B, B, [α2, β2, γ2...]” selected in the loop from S1804 to S1809.

[0210] In S1812, the model construction unit 6001 constructs a regression model s using machine learning for the data set of the model identification data group M (S1812). For example, the model construction unit 6001 constructs the regression model s using the reference value / operation amount of the data D as an explanatory variable and the control amount of the data D as a response variable.

[0211] In S1813, the model construction unit 6001 stores the regression model s calculated in S1812 as a characteristic model for characteristic l and characteristic type k (S1813). For example, the model construction unit 1001 records a characteristic model for characteristic type "B" of characteristic "Variety". When the processing up to this point is completed, the process returns to S1704, and a characteristic model is constructed for characteristic type "A" of a different characteristic "Device" and stored in the characteristic model group 6003 (S1706, FIGS. 18A and 18B).

[0212] In the following loop 3, the characteristic list L=[] and the characteristic type list K=[B] are unprocessed, and the characteristic l=device and characteristic type k=A are processed.

[0213] In S1801, the model construction unit 6001 extracts data D of characteristic l = characteristic type k from the past operation performance data 101 (S1801). For example, the model construction unit 6001 extracts data of equipment A from the past operation performance data 101 and creates partial data D consisting of a data ID (#) of 0. At this time, from the past operation performance data 101 shown in FIG. 3, the record "0, I, A, [α0, β0, γ0...]" identified by a data ID (#) of 0 is extracted as partial data D.

[0214] In S1802 and S1803, the model construction unit 6001 sets the loop variable i=0, as in the cases of types A and B, and creates an empty list M of the model identification data group.

[0215] In S1804, the model construction unit 6001 selects the i-th data D[i] from the partial data D (S1804). Here, since i=0, the model construction unit 6001 selects the record “0, I, A, [α0, β0, γ0…]” with a data ID (#) of 0 from the partial data D.

[0216] In S1805, the model construction unit 6001 determines whether characteristic models other than characteristic l of characteristic type k have been constructed for the data D[i] (S1805). In this case, characteristic models other than those for the equipment, that is, the characteristic for the product type, have been constructed, so the process proceeds to S1806.

[0217] In S1806, the model construction unit 6001 creates differential data g of the data d[i] and a value obtained by adding the output of each constructed characteristic model to the output of the process model P (S1806). For example, the model construction unit 6001 creates new differential data g as the difference between the control amount of the data d[i] and the sum of the output of the characteristic model whose characteristic is product type and characteristic type is I, which was stored in S1813 of loop 1, and the output of the process model P.

[0218] In S1807, the model construction unit 6001 adds the above-mentioned differential data g to the model identification data group M (S1807). For example, the model construction unit 6001 sets the model identification data group M=[g]. By the processing of S1807, new differential data g, which is the difference between the constructed characteristic model and data d[i], is added to the list of the model identification data group M in association with the data D[i] being currently processed (record "0, I, A, [α0, β0, γ0...]").

[0219] In S1808, the model constructing unit 6001 executes i=i+1 (S1808).

[0220] In S1809, the model construction unit 6001 determines whether i>=the number of data D (S1809). In this case, i=1 and the number of data D is 1, so the model construction unit 6001 determines that i>=the number of data (S1809; Yes) and proceeds to S1810.

[0221] In S1810, the model construction unit 6001 determines whether or not the list of the model identification data group M is empty (S1810). Here, the model construction unit 6001 added the new differential data g to the list of the model identification data group M in S1807. Therefore, the model construction unit 6001 determines that the list of the model identification data group M is not empty (S1810; No), and proceeds to S1812.

[0222] In S1811, since the determination in S1810 is No as described above, the model construction unit 6001 skips this process.

[0223] In S1812, the model construction unit 6001 constructs a regression model s for the data set of the model identification data group using machine learning (S1812). For example, the model construction unit 6001 constructs the regression model s using the reference value / operation amount of the new difference data g as an explanatory variable and the control amount of the new difference data g as a response variable.

[0224] In S1813, the model construction unit 6001 stores the regression model s calculated in S1812 as a characteristic model for characteristic l and characteristic type k (S1813). For example, the model construction unit 1001 records a characteristic model for characteristic type "A" of characteristic "equipment." When the processing up to this point is completed, the process returns to S1704, and a characteristic model is constructed for a different characteristic type "B" for the same characteristic "equipment" and stored in the characteristic model group 6003 (S1706, FIGS. 18A and 18B).

[0225] In the following loop 4, the case where the characteristic list L=[] and the characteristic type list K=[] are unprocessed and the characteristic l=device and characteristic type k=B are processed will be described.

[0226] In S1801, the model construction unit 6001 extracts data D of characteristic l = characteristic type k from the past operation performance data 101 (S1801). For example, the model construction unit 6001 extracts data of equipment B from the past operation performance data 101 and creates partial data D consisting of data IDs (#) of 1 and 2. At this time, from the past operation performance data 101 shown in FIG. 3, records “1, A, B, [α1, β1, γ1 …]” and “2, B, B, [α2, β2, γ2 …]” identified by data IDs (#) of 1 and 2 are extracted as partial data D. At this time, the model construction unit 6001 reassigns IDs and creates partial data D as records “0, A, B, [α1, β1, γ1 …]” and “1, B, B, [α2, β2, γ2 …]” as in the first embodiment.

[0227] In S1802 and S1803, the model construction unit 6001 sets the loop variable i=0, as in the cases of product type A, B, and device A, and creates an empty list M of the model identification data group.

[0228] In S1804, the model construction unit 6001 selects the i-th data D[i] from the partial data D (S1804). Here, since i=0, the model construction unit 1001 selects the record “0, I, B, [α1, β1, γ1…]” with a data ID (#) of 0 from the partial data D.

[0229] In S1805, the model construction unit 6001 determines whether characteristic models other than characteristic l of characteristic type k have been constructed for the data D[i] (S1805). In this case, characteristic models other than those for the equipment, that is, the characteristic for the product type, have been constructed, so the process proceeds to S1806.

[0230] In S1806, the model construction unit 6001 creates differential data g of the data d[i] and a value obtained by adding the output of each constructed characteristic model to the output of the process model P (S1806). For example, the model construction unit 6001 creates new differential data g by calculating the difference between the control amount of the data d[0] and the sum of the output of the characteristic model whose characteristic is product type and characteristic type is I, which was stored in S1813 of loop 1.

[0231] In S1807, the model construction unit 6001 adds data g to the model identification data group M (S1807). For example, the model construction unit 6001 sets the model identification data group M=[g(I)]. By the processing of S1807, new differential data g, which is the difference between the constructed characteristic model and data d[0], is added to the list of the model identification data group M in association with the data D[0] being currently processed (record "0, I, B, [α1, β1, γ1...]").

[0232] In S1808, the model constructing unit 6001 executes i=i+1 (S1808).

[0233] In S1809, the model construction unit 6001 determines whether i >= the number of data D (S1809). Here, i = 1 and the number of data D is 2, so the model construction unit 1001 determines that i >= the number of data is not true (S1809; No) and returns to S1804. Thereafter, the model construction unit 6001 skips the processes of S1807 and S1808. In the next loop, i = 2 and i >= 2, so the model construction unit 6001 determines that i >= the number of data is true (S1809; Yes) and proceeds to S1810. After completing the next loop, the model construction unit 6001 adds the output of the characteristic model for product type B to the output of the process model P created in S1806 and creates data g, which is the difference between the control amount of data d[1] and the output of the characteristic model for product type B. Finally, a model identification data group M = [g(A), g(B)] is created.

[0234] In S1810, the model construction unit 6001 determines whether or not the list of the model identification data group M is empty (S1810). Here, the model construction unit 6001 added the new differential data g(i) to the list of the model identification data group M in S1807. Therefore, the model construction unit 6001 determines that the list of the model identification data group M is not empty (S1810; No), and proceeds to S1812.

[0235] In S1811, since the determination in S1810 is No as described above, the model construction unit 6001 skips this process.

[0236] In S1812, the model construction unit 6001 constructs a model s using machine learning for the data set of the model identification data group (S1812). For example, the model construction unit 6001 constructs a regression model s using the reference values / operation amounts of the new difference data g(a) and g(b) as explanatory variables and the control amounts of the new difference data g(a) and g(b) as objective variables.

[0237] In S1813, the model construction unit 6001 stores the regression model s calculated in S1812 as a characteristic model for characteristic l and characteristic type k (S1813). For example, the model construction unit 1001 records a characteristic model for characteristic type "B" of characteristic "equipment." When the processing up to this point is completed, the characteristic model construction processing shown in FIGS. 18A and 18B has been performed for all characteristics and all characteristic types. Therefore, the processing proceeds to S1707 of the model construction processing shown in FIG. 17.

[0238] In S1707, since processing has already been executed for each of the characteristic type list K=[A, B], the model construction unit 6001 determines that the characteristic type list K is an empty set (S1707; Yes), and proceeds to S1708.

[0239] In S1708, since the processing has already been executed for each of the characteristic list L=[product type, equipment], the model construction unit 6001 determines that the characteristic list L is an empty set (S1708; Yes), and ends the processing.

[0240] As described above, in this embodiment, a process model construction system for constructing a process model that models the characteristics of a control object by a computer includes a first processing unit (e.g., model construction unit 6001, FIG. 17) that acquires the characteristics (e.g., product type, equipment, etc.) from past operation performance data (e.g., past operation performance data 101) that indicates operation performance according to the characteristics of the control object, and a second processing unit (e.g., model construction unit 6001, FIG. 18) that calculates, for each characteristic, a characteristic model (e.g., regression model s) obtained by correcting the process model based on the difference between an actual value of the process of the control object (e.g., controlled variable of the control object) for the characteristic included in the past operation performance data and an estimated value of the control object estimated by the process model constructed by predetermined machine learning (e.g., any machine learning such as linear regression). That is, a characteristic model for each characteristic is constructed based on the process model constructed by machine learning and the difference between the process model and the past operation performance data, and the calculation results of the process model constructed by machine learning are corrected with the calculation results of the constructed characteristic model. Therefore, as in the case of the first embodiment, even when a control object has a plurality of characteristics, a process model can be constructed with less construction load and operation load than in the past, and the number of process models constructed for performing process simulation can be reduced. Furthermore, as in the case of the first embodiment, even for a control object with an enormous number of combinations of characteristics and characteristic types, it becomes possible to perform process simulation using a small number of process models constructed by the model construction unit 6001.

[0241] In this example, the case where the entire process model is replaced with a machine learning model has been described, but some of the mathematical expressions in the process model may also be replaced with a machine learning model. In addition, in this example, the differential data is an example of a controlled variable, but it is not limited to this, and other measured values ​​or values ​​that can be inferred from measured values ​​may also be used. Furthermore, in this example, the case where differential data is prepared as training data when constructing an equipment or product type model has been described, but instead, appropriate training data (such as corresponding measured values ​​designated by the user) may be prepared for one or more characteristics or characteristic types (for example, equipment or product type) as shown in Example 4. [Example]

[0242] In the seventh embodiment, an input / output interface in the model construction process shown in the first embodiment will be described. As already described, in the model construction process, the model construction unit 1001 receives the past operation performance data 101 as input data, and reads out the process model 1004 and the initial values ​​of the parameters of the process model 1004. Then, in the model construction process, the model construction unit 1001 outputs characteristic parameters for each characteristic.

[0243] FIG. 19 is a diagram showing an example of a screen (model construction screen) that the model construction unit 1001 displays on a display of a computer that constitutes the process model construction system 1000 (for example, the output device 1605 of the computer 1600 shown in FIG. 2) when executing the model construction process.

[0244] 19, the model construction unit 1001 displays a model construction screen W1901 including input past operation performance data 101, a process model 1004, and initial values ​​of parameters of the process model, which are input data for the model construction process. The user checks this input data on the model construction screen and instructs execution of the model construction process by, for example, pressing an execute button (not shown) on the screen.

[0245] 19, the model construction unit 1001 displays a model construction screen W1902 including a characteristic parameter group 1003, which is output data of the model construction process. The user can check the output data on the model construction screen and confirm the characteristic parameters for each characteristic and characteristic type.

[0246] As described above, in this embodiment, the system includes a model construction unit (e.g., model construction unit 1001) that displays, on a computer screen, input information including past operation performance data (e.g., past operation performance data 101) indicating operation performance according to the characteristics of the controlled object (e.g., product type, equipment, etc.), a process process model (e.g., a process model represented by the theoretical formula shown in FIG. 4) for modeling the characteristics of the controlled object obtained from the past operation performance data, and output information including characteristic parameters (e.g., characteristic parameters stored in the characteristic parameter group 1003 shown in FIG. 5) for correcting the process model, calculated for each characteristic of the controlled object so that the operation performance and the estimated value of the controlled object estimated by the process model satisfy a predetermined condition (e.g., a condition that the difference between the actual control amount and the estimated control amount of the process model is minimized). That is, when the model construction unit performs the model construction process, the model construction screen described above is displayed. Therefore, a user can quickly grasp the values ​​of the characteristic parameters calculated for each characteristic and characteristic type for the input process model.

[0247] In this embodiment, a screen is used as an interface when receiving the input data from the user or when presenting the output data to the user. However, the present invention is not limited to such a screen. If the input device 1606 includes an audio input / output device such as a microphone or speaker, the input data may be received by voice through the device, or the output data may be presented by voice through the device, or other user-recognizable interface may be used. [Example]

[0248] In the eighth embodiment, an input / output interface in the simulation process shown in the first embodiment will be described. As already described, in the simulation process, a simulation unit 1002 receives, as input data, characteristics of the product type, equipment, etc., and the characteristic types included therein as characteristic information 102. In addition, the simulation unit 1002 receives time-series data of the manipulated variable of the controlled object as simulation input information 103, which is the desired operating condition. Then, in the simulation process, the simulation unit 1002 outputs, for example, time-series data of the controlled variable as a result of the simulation.

[0249] FIG. 20 is a diagram showing an example of a screen (simulation execution screen) that the simulation unit 1002 displays on a display of a computer constituting the process model building system 1000 (for example, the output device 1605 of the computer 1600 shown in FIG. 2) when executing a simulation process.

[0250] 20, the simulation unit 1002 displays a simulation execution screen W2001 including simulation input information 103, which is input data for the simulation process, and characteristic information 102. The user checks this input data on the simulation execution screen and issues an instruction to execute the simulation process by, for example, pressing an execution button (not shown) on the screen.

[0251] 20, the simulation unit 1002 displays a model construction screen W2002 including the simulation results, which are output data of the simulation process. The user can check the output data on the simulation execution screen and confirm the results of the simulation, such as the time series changes in the controlled variables.

[0252] As described above, in this embodiment, the third processing unit (e.g., the simulation unit 1002) creates input information including the characteristic information (e.g., the characteristic information 102) and simulation input information (e.g., the simulation input information 103) defining desired operating conditions for the simulation, creates a corrected process model using the characteristic parameters having the same characteristics as the characteristic information, inputs the simulation input information into the created process model, and executes the simulation. The output information includes the simulation results (e.g., time-series data of the controlled variable) obtained by the execution of the simulation, and displays these output information on the computer screen as pre- and post-processing states. That is, when the simulation unit 1002 executes the simulation process, it displays the above-described simulation execution screen. Therefore, the user can quickly understand the execution results of the simulation using the process model corrected using the characteristic parameters obtained from the input characteristic information. In this embodiment, time-series data of the manipulated variable of the controlled object is provided, but a reference value may also be provided.

[0253] In this embodiment, a screen is used as an interface when receiving the input data from the user or when presenting the output data to the user. However, as in Example 7, other user-recognizable interfaces may be used. [Example]

[0254] In the ninth embodiment, an input / output interface will be described in the case where an operation method (e.g., manipulated variable) of a controlled object that satisfies the optimal operating conditions shown in the second embodiment is calculated backward, and the result is fed back to the controlled object. As already described, in the second embodiment, the control device input calculation unit 2001 receives, as input data, desired operating condition data 106 for the controlled object, and characteristics of the controlled object such as product type and equipment as characteristic information 102, and the characteristic types included therein. The control device input calculation unit 2001 calculates, for example, an input (manipulated variable in this example) that matches the optimal operating condition, which is the desired operating condition data 106, with the result of a simulation executed by the simulation unit 1002, and outputs the time-series data of the manipulated variable as an input value of the controlled object.

[0255] FIG. 21 is a diagram showing an example of a screen (feedback execution screen) displayed on a display of a computer constituting the process model construction system 1000 (for example, the output device 1605 of the computer 1600 shown in FIG. 2) when the control equipment input calculation unit 2001 back-calculates an operation method of the controlled object that satisfies the optimal operating conditions.

[0256] 21, the control device input calculation unit 2001 displays a feedback execution screen W2101 including desired operating condition data 106, which are input data when the above feedback is performed, and characteristic information 102. The user checks this input data on the feedback execution screen and instructs the back-calculation of an operating method for the controlled object that satisfies the optimal operating conditions by, for example, pressing an execution button (not shown) on the screen.

[0257] 21, the control device input calculation unit 2001 displays a feedback execution screen W2102 including the feedback result, which is the output data of the above-mentioned back calculation. The user can check the output data on the feedback execution screen and confirm the feedback result, such as the time-series change of the manipulated variable.

[0258] As described above, in this embodiment, the fourth processing unit (e.g., control device input calculation unit 2001) displays on the screen of the computer, as states before and after processing, input information including desired operating conditions for the controlled object (e.g., operating condition data 106), characteristic information indicating the characteristics of the controlled object and the characteristic types included in the characteristics (e.g., characteristic information 102), and output information including input values ​​to the controlled object (e.g., time-series data of manipulated variables) that match with the results of simulating the behavior of the process of the controlled object using a process model corrected using the characteristic parameters of the same characteristics as the characteristic information.

[0259] That is, the control device input calculation unit 2001 displays the above-mentioned feedback execution screen when it back-calculates the operation method of the controlled object that satisfies the optimal operating conditions and feeds back the result to the controlled object. Therefore, the user can grasp at a glance the feedback result of the control by the input value to the controlled object that satisfies the optimal operating conditions for the controlled object at the current time.

[0260] In this embodiment, a screen is used as an interface when accepting the input data from the user or when presenting the output data to the user. However, as in Examples 7 and 8, other user-recognizable interfaces may be used. In this example, the case of back-calculating the manipulated variable is described, but the same may be applied to the case of back-calculating the reference value.

[0261] The present invention is not limited to the above-described embodiments as they are, and in the implementation stage, the components can be modified and embodied within the scope of the gist of the present invention, or multiple components disclosed in the above-described embodiments can be appropriately combined. [Explanation of symbols]

[0262] 1000~6000 Process model building system 1001 Model Construction Department 1002 Simulation Department 1003 Characteristic Parameter Group 1004 Process Model 2001 Control equipment input calculation section 3001 Characteristic parameter model construction unit 3002 Simulation Department 4001 Model Identification Information 4002 Model Construction Department 6001 Model Construction Department 6002 Simulation Department

Claims

1. A process model construction system that constructs a process model that models characteristics of a control target by a computer, comprising: a first processing unit that acquires a plurality of different types of characteristics from past operation performance data that indicates operation performance according to the characteristics of the control object, which are elements that affect the behavior of the process model; and a second processing unit that calculates, for each characteristic, a characteristic parameter for correcting the process model such that an actual value of the process to be controlled for the characteristic included in the past operation result data and an estimated value of the process to be controlled estimated by the process model constructed using a predetermined theoretical formula satisfy a predetermined condition; the second processing unit corrects the process model based on the characteristic parameters associated with combinations of a plurality of different types of the characteristics; A process model construction system comprising:

2. a third processing unit that creates a corrected process model using the characteristic parameters of the same characteristics as the characteristics included in characteristic information that indicates the characteristics of the controlled object and characteristic types included in the characteristics and that is input to the process model creation system, and that simulates the behavior of the controlled object process using the created process model; 2. The process model construction system according to claim 1, further comprising:

3. a fourth processing unit that calculates an input value for the controlled object using a predetermined algorithm and a feedback result of a control amount for the controlled object obtained from the controlled object, and performs an optimization calculation to find an input value that matches a desired operating condition for the controlled object; 2. The process model construction system according to claim 1, further comprising:

4. a fifth processing unit that constructs a characteristic parameter model for estimating the characteristic parameter of the characteristic by machine learning using characteristic additional information that associates the characteristic of the controlled object with feature information that is an element that affects the characteristic and corresponds to the characteristic for estimating the characteristic as an explanatory variable, and the characteristic parameter that corrects a process model of the controlled object for the characteristic as an objective variable; the third processing unit simulates the behavior of the process to be controlled by using the process model corrected using the estimated value of the characteristic parameter obtained by inputting the characteristic additional information into the characteristic parameter model.

3. The process model construction system according to claim 2.

5. the process model construction system has model identification information in which the characteristic parameters of the process model corresponding to one or more characteristics are stored; the second processing unit creates model identification data that associates the process model corrected by the characteristic parameters with actual values ​​included in the past operation result data used when constructing the process model before correction, and performs a model identification process that performs identification on a pair of the process model corrected by the characteristic parameters included in the created model identification data and the actual values; In the model identification process, the characteristic parameters defined in the model identification information are selected based on a predetermined algorithm, and the process model is corrected with the selected characteristic parameters.

2. The process model construction system according to claim 1.

6. the second processing unit calculates a statistical value based on the characteristic parameter calculated for each of the characteristics, and corrects the process model constructed using the predetermined theoretical formula by using the statistical characteristic parameter based on the calculated statistical value.

2. The process model construction system according to claim 1.

7. the second processing unit creates model identification data that associates the process model corrected by the characteristic parameters with actual values ​​included in the past operation result data used when constructing the process model before correction, and performs a model identification process that performs identification on a pair of the process model corrected by the characteristic parameters included in the created model identification data and the actual values; the first processing unit sets the parameters of the process model corrected by the characteristic parameters obtained by the model identification processing as initial values ​​of the parameters of the process model.

2. The process model construction system according to claim 1.

8. the process model construction system has an input unit that accepts input of the characteristic parameters, the second processing unit sets at least some of the characteristic parameters to parameters input from the input unit, and performs the calculation for parameters other than the set parameters; 2. The process model construction system according to claim 1.

9. when a difference between the result of the simulation and the control result of the control object exceeds a predetermined threshold, the third processing unit corrects the characteristic parameters of the process model so as to reduce the difference, and re-executes the simulation.

3. The process model construction system according to claim 2.

10. A process model construction system that constructs a process model that models characteristics of a control target by a computer, comprising: a first processing unit that acquires a plurality of different types of characteristics from past operation performance data that indicates operation performance according to the characteristics of the control object, which are elements that affect the behavior of the process model; and a second processing unit that calculates, for each characteristic, a characteristic model obtained by correcting the process model based on a difference between an actual value of the process of the controlled object for the characteristic included in the past operation result data and an estimated value of the controlled object estimated by the process model constructed by predetermined machine learning, the second processing unit corrects the process model based on the characteristic model associated with a combination of a plurality of different types of the characteristics; A process model construction system comprising:

11. Input information including past operational performance data indicating operation performance according to characteristics of a control object, which is an element that affects the behavior of the process model, and process models for modeling the characteristics of a plurality of different types of the control object obtained from the past operational performance data; output information including characteristic parameters for correcting the process model, which are calculated for each characteristic of the controlled object so that the operation record and the estimated value of the controlled object estimated by the process model satisfy predetermined conditions, the output information including the characteristic parameters related to a combination of a plurality of different types of the controlled object, and the output information including the characteristic parameters related to a combination of a plurality of different types of the controlled object, are displayed on a computer screen as states before and after processing. A process model construction system comprising:

12. the third processing unit creates a corrected process model using input information including the characteristic information and simulation input information defining desired operating conditions for performing a simulation, and the characteristic parameters having the same characteristics as the characteristic information, and inputs the simulation input information into the created process model to execute the simulation, and displays output information including the results of the simulation obtained by executing the simulation on a screen of the computer as states before and after processing.

3. The process model construction system according to claim 2.

13. the fourth processing unit displays, on a screen of the computer, as states before and after processing, input information including desired operating conditions for the controlled object, characteristic information indicating characteristics of the controlled object and characteristic types included in the characteristics, and output information including input values ​​to the controlled object that match results of simulating the behavior of the process of the controlled object using a process model corrected using the characteristic parameters of the same characteristics as the characteristic information.

4. The process model construction system according to claim 3.

14. the first processing unit acquires, from the past operation performance data, a first device and a first product type that indicate characteristics of the control target; the second processing unit calculates, for each of the first equipment and the first product type, the characteristic parameters such that an actual value of the process to be controlled and an estimated value of the process to be controlled for the first equipment and the first product type satisfy a predetermined condition, and corrects the process model when generating the first product type in the first equipment based on at least the calculated characteristic parameters; 2. The process model construction system according to claim 1.

15. A process model construction method for constructing a process model that models characteristics of a control target by a computer, comprising the steps of: acquiring a plurality of different types of characteristics from past operation performance data showing operation performance according to the characteristics of the control object, which are elements that affect the behavior of the process model; calculating, for each characteristic, a characteristic parameter for correcting the process model such that an actual value of the process to be controlled for the characteristic included in the past operation record data and an estimated value of the process to be controlled estimated by the process model constructed using a predetermined theoretical formula satisfy a predetermined condition; correcting the process model based on the characteristic parameters associated with a plurality of different types of combinations of the characteristics; A process model construction method comprising:

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