Information processing system, information processing method, and program

Abstract

To provide an information processing system or the like for facilitating the management of an expected result or the like to be generated in the future by being derived from a water system.SOLUTION: A parameter information acquisition unit, a relationship model information acquisition unit, an estimation unit, and a simulation unit, in which the parameter information acquisition unit acquires two or more pieces of parameter information selected from a water quality parameter, a control parameter, and a result parameter, the relationship model information acquisition unit acquires relationship model information indicating a relationship between an index related to an expected result created in advance and the two or more parameters, and the estimation unit presents the index related to the expected result to a user as an estimation result on the basis of the acquired parameter information and the relationship model information, the simulation unit receives a change of at least one of the estimation result and each parameter from a user, and makes a trial calculation of a variation for an item for which a change from the user is not received among the estimation result and each parameter based on content of the received change and the relationship model information.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an information processing system, an information processing method, and a program. [Background technology]

[0002] Various products, including paper products, are manufactured using aqueous processes. In these processes, problems are predicted in advance in order to prevent or mitigate the occurrence of operational problems and adverse effects on products. In this regard, techniques for predicting product quality, etc. in papermaking processes, etc., are known. For example, Patent Document 1 discloses a technique for predicting expected results, etc., using predetermined parameter information and relationship model information. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-123880 Summary of the Invention [Problem to be solved by the invention]

[0004] However, when a problem occurs in any process, a quicker response than ever before is required. From this perspective, technology that is easy for system users to use is desired.

[0005] In view of the above circumstances, the present invention provides an information processing system and the like that makes it easy to manage estimated expected results and the like. [Means for solving the problem]

[0006] According to one aspect of the present invention, there is provided an information processing system for predicting possible future results in a water system or derived from the water system, the system comprising a parameter information acquisition unit, a relationship model information acquisition unit, an estimation unit, and a simulation unit, wherein the parameter information acquisition unit acquires, as parameter information, two or more parameters selected from the group consisting of water quality parameters, control parameters, and result parameters, wherein the water quality parameters are parameters relating to the water quality of the water system, the control parameters are parameters relating to the control conditions of the water system, equipment related to the water system, or raw materials added to the water system, and the result parameters are parameters having a meaning different from the expected result, and which are parameters relating to the water system, equipment related to the water system, or raw materials added to the water system, or the water system, equipment related to the water system, or raw materials added to the water system. and a relationship model information acquisition unit acquires relationship model information created in advance that indicates the relationship between an expected result or an index related to the expected result and two or more parameters. The estimation unit presents the expected result or an index related to the expected result to the user as an estimated result based on the acquired parameter information and relationship model information. Here, the estimated result is presented to the user in association with each parameter included in the parameter information. The simulation unit accepts from the user a change to at least one of the estimated result and each parameter presented by the estimation unit, and estimates fluctuations for items of the estimated result and each parameter for which no change was accepted from the user based on the content of the accepted change and the relationship model information.

[0007] According to the above aspect, an information processing system or the like is provided that facilitates management of estimated expected results, etc.

[0008] Furthermore, it may be provided in the following aspects.

[0009] (1) An information processing system for predicting a future expected result in a water system or derived from the water system, comprising a parameter information acquisition unit, a relationship model information acquisition unit, an estimation unit, and a simulation unit, wherein the parameter information acquisition unit acquires, as parameter information, two or more parameters selected from the group consisting of a water quality parameter, a control parameter, and a result parameter, wherein the water quality parameter is a parameter related to the water system, the control parameter is a parameter related to the water system, or a control condition of the water system, equipment related to the water system, or a raw material added to the water system, and the result parameter is a parameter having a meaning different from the expected result, and which is generated in the water system, equipment related to the water system, or a raw material added to the water system, or derived from the water system, equipment related to the water system, or a raw material added to the water system. the relationship model information acquisition unit acquires relationship model information created in advance that indicates a relationship between the forecast result or an index related to the forecast result and the two or more parameters, the estimation unit presents the forecast result or the index related to the forecast result to a user as a forecast result based on the acquired parameter information and relationship model information, wherein the forecast result is presented to the user in association with each parameter included in the parameter information, and the simulation unit accepts from the user a change to at least one of the forecast result and each of the parameters presented by the estimation unit, and estimates fluctuations for items of the forecast result and each of the parameters for which no change was accepted from the user, based on the content of the accepted change and the relationship model information.

[0010] (2) In the information processing system described in (1) above, the relationship model information is a model obtained by regression analysis, time series analysis, decision tree, neural network, Bayes, clustering, classification, or ensemble learning between a pre-confirmation result corresponding to the forecast result or an index related to the pre-confirmation result and the two or more parameters.

[0011] (3) In the information processing system described in (1) or (2) above, the water system is a water system in a process for producing paper products and / or materials related to the production of paper products.

[0012] (4) In the information processing system described in (3) above, the parameter information includes, as the water quality parameters, one or more selected from the group consisting of pH of the water system, electrical conductivity, oxidation-reduction potential, zeta potential, turbidity, temperature, foam height, biochemical oxygen demand (BOD), chemical oxygen demand (COD), total organic carbon (TOC), inorganic carbon, absorbance, color, appearance, whiteness, transparency, particle size distribution, degree of aggregation, amount of foreign matter, suspended solids (SS), foam area on the water surface, area of ​​dirt in the water, amount of bubbles, amount of glucose, amount of organic acid, amount of active alkali, total amount of titratable alkali, metal or metal ion content, non-metal ion content, amount of starch, amount of calcium, total chlorine content, amount of free chlorine, amount of dissolved oxygen (DO), cation demand, amount of hydrogen sulfide, degree of sulfidation, amount of hydrogen peroxide, ash concentration, respiration rate of microorganisms in the system, viable cell count, spore count, and ATP.

[0013] (5) In the information processing system described in (3) or (4) above, the parameter information includes, as the control parameters, the operating speed (machining speed) of the paper machine, the filter cloth rotation speed of the raw material dehydrator, the filter cloth rotation speed of the washer, the amount or unit of chemicals added to the water system, the amount or unit of chemicals added to the raw material added to the water system, the amount or unit of chemicals added to the equipment related to the water system, the amount or unit of steam used for heating, the temperature of steam used for heating, the pressure of steam used for heating, the flow rate from the seed box, the nip pressure of the press part, the felt vacuum pressure of the press part, the blending ratio of the papermaking raw materials, and the broken paper of the papermaking raw materials. Blend amount or unit of weight, screen opening of papermaking raw material, gap distance between rotor and stator of beater, freeness, degree of beating, draw, line pressure of press part, gravitational dewatering rate of wire part or parameter equivalent thereto, forced dewatering rate of wire part or parameter equivalent thereto, gravitational dewatering rate of press part, parameter equivalent to gravitational dewatering rate of press part, parameter for controlling gravitational dewatering rate of press part, forced dewatering rate of press part, parameter equivalent to forced dewatering rate of press part, parameter for controlling forced dewatering rate of press part , concentration of interlayer adhesive, amount or consumption unit of interlayer adhesive, amount of shower water or consumption unit, new water consumption unit, water consumption unit, timing or number of times of cleaning the wire part, timing or number of times of cleaning the press part, timing or number of times of cleaning the dryer part, timing or number of times of changing paper machine tools, amount of new water replenishment, amount of screen rejects or parameters for controlling it, screen differential pressure or equivalent parameters, amount of cleaner rejects or parameters for controlling it, amount of fuel for heating or consumption unit, flotator concentration, flotator air amount or parameters equivalent thereto, froth amount of a flotator or a parameter equivalent thereto, load on a disperser or a parameter equivalent thereto, number of times of felt singeing, flow rate of fluid flowing through the water system, air temperature, amount of steam for heating or consumption unit, amount of calcium carbonate added or consumption unit, amount of calcium bicarbonate added or consumption unit, flow rate or concentration of clarified green liquor, flow rate or concentration of crude green liquor, flow rate or concentration of diluted black liquor, flow rate or concentration of strong black liquor, flow rate or concentration of white liquor, amount of black liquor injected in a black liquor treatment system, black liquor injection concentration in a black liquor treatment system, pulp production amount, mud filter moisture content,An information processing system comprising one or more items selected from the group consisting of lime input amount or current unit in a slaking / causticizing system, lime production amount in a calcination system, fuel consumption amount, fuel consumption unit, causticization rate in a slaking / causticizing system, caustic input amount or unit consumption in a cooking system, black liquor generation amount in a black liquor treatment system, solid amount when evaporating black liquor in a black liquor treatment system, evaporation multiple when evaporating black liquor in a black liquor treatment system, boiler economizer temperature in a black liquor treatment system, moisture content of sludge in a green liquor treatment system, specific gravity of black liquor, specific gravity of green liquor, specific gravity of white liquor, specific gravity of weak liquor, specific gravity of dregs, specific gravity of calcium carbonate before calcination, and specific gravity of calcium oxide after calcination.

[0014] (6) In the information processing system described in any one of (3) to (5) above, the parameter information includes, as the result parameters, the unit weight (basis weight) of the paper product, the yield rate, the white water concentration, the moisture content of the paper product, the amount of steam or the basic unit in the equipment for manufacturing the paper product, the steam temperature in the equipment for manufacturing the paper product, the steam pressure in the equipment for manufacturing the paper product, the thickness of the paper product, the ash concentration in the paper product, the type of defect in the paper product, the number of defects in the paper product, the number of paper breaks in the process, an information processing system including one or more items selected from the group consisting of timing of the drying, freeness, degree of beating, amount of aeration, air temperature inside the dryer, jointing rate, number of joints, amount of paper waste, number of defects, defect index, outside air temperature including indoor air temperature, outside humidity including indoor humidity, moisture content of wet paper, moisture content of product, temperature of the rolls and cylinders of the dryer, measurements made by the five senses sensors, amount of fuel or fuel consumption rate, burning rate in the burning system, causticization rate in the slaking / causticizing system, amount of caustic added in the cooking system, and amount of lime added in the slaking / causticizing system.

[0015] (7) In the information processing system described in any one of (3) to (6) above, the predicted result relates to the quality or energy amount of the paper product and / or materials related to the production of the paper product, the quality relates to one or more selected from the group consisting of the number of defects, paper strength, joint rate, sizing degree, air permeability, smoothness, ash content, color tone, whiteness, texture, odor, causticization rate, burning rate, kappa number, freeness and moisture content of the paper product and / or materials related to the production of the paper product, and the energy amount relates to one or more selected from the group consisting of steam amount, steam consumption rate, fuel amount and fuel consumption rate when producing the paper product and / or materials related to the production of the paper product.

[0016] (8) An information processing method executed by an information processing system, the method comprising a step of executing processing of each part of the information processing system described in any one of (1) to (7) above.

[0017] (9) A program for causing a computer to execute the processing of each part of the information processing system described in any one of (1) to (7) above. Of course, this is not the case. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a diagram showing the overall configuration of an information processing system 1. FIG. [Figure 2] FIG. 2 is a diagram illustrating a hardware configuration of an information processing device 2. [Figure 3] FIG. 2 is a diagram showing the hardware configuration of a user terminal 3. [Figure 4] 1 is an example of a papermaking process to which the information processing system 1 can be applied. [Figure 5] FIG. 2 is a functional block diagram showing functions of the information processing device 2. [Figure 6] 1 is an activity diagram showing the flow of information processing using the information processing device 2 and the like. [Figure 7]This is a plot of the number of trouble occurrences A versus an index a related to the expected outcome for a total of 30 data sets. [Figure 8] 10 is a graph showing the magnitude of the influence of each parameter on index a related to the expected outcome. [Figure 9] FIG. 10 is a diagram showing an example of a guess result presented to a user. [Figure 10] FIG. 10 is a diagram showing the results of a simulation when a change to parameter 1 is input. [Figure 11] FIG. 10 is a diagram showing the results of a simulation when a change to the expected outcome index is input. [Figure 12] FIG. 10 is a diagram showing simulation results regarding defect indexes. DETAILED DESCRIPTION OF THE INVENTION

[0019] Hereinafter, embodiments of the present invention will be described. Note that various features shown in the following embodiments can be combined with each other.

[0020] That is, the information processing system of this embodiment is as follows. 1. An information processing system for predicting potential future outcomes in or derived from a water system, comprising: The system includes a parameter information acquisition unit, a relationship model information acquisition unit, an estimation unit, and a simulation unit, the parameter information acquisition unit acquires, as parameter information, two or more parameters selected from the group consisting of a water quality parameter, a control parameter, and a result parameter; Here, the water quality parameters are parameters related to the water quality of the water system, the control parameters are parameters relating to control conditions of the aqueous system, equipment related to the aqueous system, or raw materials added to the aqueous system; The result parameter is a parameter having a different meaning from the expected result, and is a parameter relating to a result that has occurred in the water system, equipment related to the water system, or raw materials added to the water system, or that has been derived from the water system, equipment related to the water system, or raw materials added to the water system, the relationship model information acquisition unit acquires relationship model information, created in advance, that indicates a relationship between the expected result or an index related to the expected result and the two or more parameters; the estimation unit presents the expected result or an index related to the expected result to a user as an expected result based on the acquired parameter information and the acquired relationship model information; wherein the estimation result is presented to the user in association with each parameter included in the parameter information; The simulation unit receives from the user a change to at least one of the estimation results and the parameters presented by the estimation unit, and estimates fluctuations for items of the estimation results and the parameters for which no changes were accepted from the user, based on the content of the accepted change and the relationship model information.

[0021] Incidentally, the program for realizing the software appearing in one embodiment may be provided as a non-transitory computer-readable medium, or may be provided so that it can be downloaded from an external server, or may be provided so that the program is started on an external computer and its functions are realized on a client terminal (so-called cloud computing).

[0022] Furthermore, various information processing according to an embodiment may realize input and output corresponding to the input. Here, the form of information referenced in such information processing (hereinafter referred to as reference information) is not limited as long as an output is obtained as a result of the input. The reference information may be, for example, rule-based information such as a database, a lookup table, or a predetermined function (including a decision formula such as a regression formula constructed using a statistical method), a trained model that has previously trained the correlation between input and output, or a large-scale language model that can output a desired result by inputting a prompt.

[0023] In one embodiment, a "unit" may include, for example, a combination of hardware resources implemented by a circuit in the broad sense and software information processing that can be specifically realized by these hardware resources. In one embodiment, various information is handled, and this information is represented, for example, by physical values ​​of signal values ​​representing voltage and current, high and low signal values ​​as a binary bit set consisting of 0 or 1, or quantum superposition (so-called quantum bits), and communication and calculations can be performed on a circuit in the broad sense.

[0024] Furthermore, a circuit in the broad sense is a circuit realized by at least an appropriate combination of a circuit, circuitry, processor, memory, etc. The processor may be a general-purpose processor or a dedicated circuit. That is, it includes an application specific integrated circuit (ASIC), a programmable logic device (e.g., a simple programmable logic device (SPLD), a complex programmable logic device (CPLD), and a field programmable gate array (FPGA)), etc.

[0025] 1. Hardware Configuration In this section, the hardware configuration of the information processing system 1 according to this embodiment will be described.

[0026] The information processing system 1 of this embodiment is a system used to estimate possible future results that may occur in a water system or derive from a water system. Here, the information processing system 1 of this embodiment is equipped with an information processing device 2 and a user terminal 3, which are connected via a communication line. Note that the communication line here includes the Internet, wireless, etc., and mediates the exchange of data between devices connected to the line. Furthermore, in the information processing system 1 of this embodiment, the measurement device 4 is configured to be able to measure various parameters in the water system W. The parameters measured by this measurement device 4 are configured to be able to be transmitted to the information processing device 2.

[0027] In this specification, a system exemplified as information processing system 1 is one that is made up of one or more devices or components. Therefore, even an information processing device 2 alone is an example of a system, and a system that also includes a user terminal 3, a water system W to which the system is applied, and a measuring device 4 may also be called a system. Below, we will continue to explain each device that can constitute information processing system 1.

[0028] [Information processing device 2] 2 is a diagram showing the hardware configuration of the information processing device 2. The information processing device 2 has a communication unit 21, a storage unit 22, and a control unit 23, and is configured by electrically connecting these units via a communication bus 20. Each unit provided in the information processing device 2 will be described below.

[0029] (Communications Department 21) The communication unit 21 is configured to be able to transmit various electrical signals from the information processing device 2 to external components. The communication unit 21 is also configured to be able to receive various electrical signals from the external components to the information processing device 2. Note that the communication unit 21 may have a network communication function, thereby enabling communication of various information between the information processing device 2 and external devices via a communication line.

[0030] (Storage unit 22) The memory unit 22 stores various pieces of information defined above. This can be implemented, for example, as a storage device such as a solid state drive (SSD) that stores various programs and the like related to the information processing device 2 executed by the control unit 23, or as a memory such as a random access memory (RAM) that stores temporarily required information (arguments, arrays, etc.) related to the program operations. The memory unit 22 stores various programs, variables, etc. related to the information processing device 2 executed by the control unit 23.

[0031] (Control unit 23) The control unit 23 is, for example, a central processing unit (CPU) not shown. The control unit 23 realizes various functions related to the information processing device 2 by reading out predetermined programs stored in the storage unit 22. In other words, information processing by software stored in the storage unit 22 is specifically realized by the control unit 23, which is an example of hardware, and can be executed as each functional unit included in the control unit 23. These will be described in more detail in the next section. Note that the control unit 23 is not limited to being single, and multiple control units 23 may be provided for each function. A combination of these may also be used.

[0032] [User terminal 3] FIG. 3 is a diagram showing the hardware configuration of the user terminal 3. The user terminal 3 is typically a terminal used by a person who performs operations related to the water system W. In this specification, a person who performs such operations may be simply referred to as a "user." The user terminal 3 has a communication unit 31, a memory unit 32, a control unit 33, a display unit 34, and an input unit 35, and these components are electrically connected within the user terminal 3 via a communication bus 30. Descriptions of the communication unit 31, memory unit 32, and control unit 33 will be omitted as they are substantially the same as the communication unit 21, memory unit 22, and control unit 23 in the information processing device 2 described above.

[0033] (Display section 34) The display unit 34 may be, for example, included in the housing of the user terminal 3 or may be externally attached. The display unit 34 displays a graphical user interface (GUI) screen that can be operated by the user. This is preferably implemented by selectively using display devices such as a CRT display, a liquid crystal display, an organic EL display, and a plasma display depending on the type of the user terminal 3.

[0034] (Input unit 35) The input unit 35 may be included in the housing of the user terminal 3, or may be externally attached. For example, the input unit 35 may be implemented as a touch panel integrated with the display unit 34. A touch panel allows the user to input tapping, swiping, and the like. Of course, a switch button, a mouse, a QWERTY keyboard, or the like may be used instead of a touch panel. That is, the input unit 35 accepts an operation input made by the user. The input is transferred as a command signal to the control unit 33 via the communication bus 30, and the control unit 33 can execute predetermined control or calculation as necessary.

[0035] [Measuring device 4] The measuring device 4 is configured to be able to measure various parameters related to the water system W. In other words, this measuring device 4 is a measuring device related to predetermined parameters. In this embodiment, predetermined calculation processing is performed using two or more parameters selected from the group consisting of water quality parameters, control parameters, and result parameters, and the measuring device 4 acquires parameters that form the basis of this calculation. Note that while only a single measuring device 4 is shown in FIG. 1, multiple (multiple types of) measuring devices 4 may be applied to the information processing system 1.

[0036] The measuring device 4 may be selected from a variety of sensors, depending on the parameters to be measured. Examples of the measuring device 4 that can be used include a pH meter, an electrical conductivity meter, an oxidation-reduction potential meter, a turbidity meter, a thermometer, a level meter for measuring bubble height, a COD meter, a UV meter, a particle size distribution meter, an aggregation sensor, a digital camera (or a digital video camera), an internal bubble sensor, an absorptiometer, a freeness meter, a dissolved oxygen meter, a zeta potential meter, a residual chlorine meter, a hydrogen sulfide meter, a retention / freeness meter, a color sensor, a hydrogen peroxide meter, and a five-sense sensor. The five-sense sensor here may include an image sensor, a light intensity sensor, an acoustic sensor, an ultrasonic sensor, a gas component sensor, an odor sensor, a liquid component sensor, a tactile sensor, a pressure sensor, a temperature sensor, a humidity sensor, a displacement sensor, and the like.

[0037] In addition, control parameters, etc., may be directly input for controlling the device and used as they are, or such data may be transmitted and received from the device, or the device operator may record the control parameters on a device other than the device.

[0038] [Water-based W] The aqueous system W according to the present embodiment may be any of various industrial processes involving water. The type of aqueous system W is not particularly limited, but may be, for example, an aqueous system in a process for producing paper products and / or materials related to the production of paper products. Hereinafter, these may be collectively referred to as "paper products, etc." Furthermore, in this specification, "materials related to the production of paper products" does not refer to the paper products themselves, but rather to various raw materials and intermediates used in the production of paper products. Raw materials used in the production of paper products include white water and calcium oxide generated during the paper production process. Intermediates used in the production of paper products include, for example, pulp produced before the papermaking process. Specifically, processes for producing white liquor, calcium oxide, and pulp, which are materials related to paper products, include the cooking process, washing process, black liquor concentration process, and causticizing process. The target aqueous system may also be an aqueous system other than that used in the production of paper products, etc., and may include, for example, various pipes, heat exchangers, storage tanks, kilns, and cleaning equipment. An example of a process targeted by the information processing system 1 of this embodiment is shown in FIG. 4. FIG. 4 shows an example of a papermaking process to which the information processing system 1 can be applied. In the papermaking process shown in FIG. 4, various systems are shown, including a raw material system, a preparation / papermaking system, a recovery system, and a drainage system. The measuring device 4 described above can acquire parameters from various elements present in such a papermaking process. Note that the application of the information processing system 1 is not limited to the illustrated process, and certain elements may be added or deleted as appropriate.

[0039] 2. Functional configuration In this section, the functional configuration of this embodiment will be described. Fig. 5 is a functional block diagram showing the functions of the information processing device 2. As described above, information processing by software (stored in the storage unit 22) is specifically realized by hardware (control unit 23), and can be executed as each functional unit included in the control unit 23.

[0040] Specifically, the information processing device 2 (control unit 23) may include, as its functional units, a parameter information acquisition unit 231, a relationship model information acquisition unit 232, an estimation unit 233, a simulation unit 234, a relationship model information creation unit 235, and a memory management unit 236. Note that these functional units may be increased, omitted, or integrated as appropriate depending on the application to which the information processing device 2 is applied.

[0041] (Parameter information acquisition unit 231) The parameter information acquisition unit 231 is configured to be able to execute a parameter information acquisition step. In the parameter information acquisition step, the parameter information acquisition unit 231 acquires, as parameter information, two or more parameters selected from the group consisting of water quality parameters, control parameters, and result parameters. Here, the water quality parameters are parameters related to the water quality of the water system. The control parameters are parameters related to the control conditions of the water system, equipment related to the water system, or raw materials added to the water system. The result parameters are parameters having a meaning different from the expected result, and are parameters related to the results generated in the water system, equipment related to the water system, or raw materials added to the water system, or derived from the water system, equipment related to the water system, or raw materials added to the water system. Note that, during this acquisition, the parameter information acquisition unit 231 is configured, for example, to acquire various information via the communication unit 21 from a measurement device 4 capable of measuring at least some of the parameters.

[0042] (Relationship model information acquisition unit 232) The relationship model information acquisition unit 232 is configured to be able to execute a relationship model information acquisition step. In the relationship model information acquisition step, the relationship model information acquisition unit 232 acquires relationship model information that is created in advance and indicates the relationship between an expected result or an index related to the expected result and two or more parameters. Details of this model will be explained later.

[0043] (Guessing part 233) The estimation unit 233 is configured to be able to execute an estimation process. In the estimation process, the estimation unit 233 presents the predicted result or an index related to the predicted result to the user as the predicted result based on the acquired parameter information and relationship model information. Here, the predicted result is associated with each parameter included in the parameter information and presented to the user. Here, the presented object (the predicted result) is configured to be recognizable by the user, etc. That is, the estimation unit 233 creates display information and controls it so that it is visible to the user, etc. Note that the display information may be visual information itself, such as a screen, an image, an icon, or text, generated in a manner that is visible to the user, or may be rendering information for displaying visual information, such as a screen, an image, an icon, or text, on various devices or terminals. Details of information processing related to estimation will be described later.

[0044] (Simulation Section 234) The simulation unit 234 is configured to be able to execute a simulation step. In the simulation step, the simulation unit 234 receives from the user a change to at least one of the inference results and parameters presented by the estimation unit 233, and estimates fluctuations of the inference results and parameters for which no change was accepted from the user, based on the content of the accepted change and the relationship model information. Details of the information processing related to this simulation will be explained later.

[0045] (Relationship model information creation unit 235) The relationship model information creation unit 235 is configured to be able to execute a relationship model information creation step. In the relationship model information creation step, the relationship model information creation unit 235 creates or updates relationship model information used in the above-mentioned estimation step, etc.

[0046] (Memory Management Department 236) The memory management unit 236 is configured to be able to execute a memory management process. In the memory management process, the memory management unit 236 is configured to manage various pieces of information to be stored that are related to the information processing system 1 of this embodiment. Typically, the memory management unit 236 is configured to store information handled by the information processing device 2 in a memory area. This memory area is exemplified by the memory unit 22 of the information processing device 2 or the memory units of various devices and terminals, but this memory area does not necessarily have to be within the information processing system 1, and the memory management unit 236 can also manage various pieces of information to be stored in an external storage device or the like.

[0047] 3. Details of data processing In Section 3, an information processing method executed by the information processing device 2 etc. will be described with reference to an activity diagram etc. Fig. 6 is an activity diagram showing the flow of information processing using the information processing device 2 etc.

[0048] 6, in this embodiment, first, the parameter information acquisition unit 231 of the information processing device 2 acquires parameter information (activity A101). As described above, the parameter information includes two or more parameters selected from the group consisting of water quality parameters, control parameters, and result parameters.

[0049] The parameter information is related to the water system W and includes two or more parameters selected from the group consisting of water quality parameters, control parameters, and result parameters. Typically, this parameter information relates to the target of estimation and is set taking into consideration factors such as the degree of influence. Note that the water system W here is not limited to a single tank or flow path or a continuous flow path, but also includes a single water system with multiple tanks or flow paths, specifically, a system with branching flow paths, water merging from multiple flow paths, water moving from tank to tank in batches, and water treatment along the way. Furthermore, if the water system related to the water quality parameters, control parameters, or result parameters is divided into tanks or other sections, it is sufficient to use the water quality parameters, control parameters, or result parameters for a portion of the water system, or the water quality parameters, control parameters, or result parameters for the entire water system.

[0050] The water quality parameters are not particularly limited as long as they relate to the water quality of the aqueous system W. Furthermore, the control parameters are not particularly limited as long as they relate to the control conditions of the aqueous system W, equipment related to the aqueous system W, or the raw materials added to the aqueous system W. Furthermore, the result parameters are not particularly limited as long as they have a meaning different from the expected result and relate to a result derived from the aqueous system W, equipment related to the aqueous system W, or the raw materials added to the aqueous system W, or from the aqueous system W, equipment related to the aqueous system W, or the raw materials added to the aqueous system W. Note that "having a meaning different from the expected result" includes cases where the evaluation indexes (e.g., physical quantities) are different (e.g., when one is length and the other is mass), cases where the evaluation index is the same but the evaluation targets are different (e.g., the mass of paper and the mass of an additive), or cases where the measurement locations are different (e.g., the oxidation-reduction potential of a papermaking raw material system and the oxidation-reduction potential of a papermaking system), but do not include cases where the meaning of the result is different (e.g., when the oxidation-reduction potential is positive and when the oxidation-reduction potential is negative).

[0051] Below, specific examples of water quality parameters, control parameters, and result parameters will be described when the aqueous system W is an aqueous system in a process for producing paper products, etc. These parameters may relate to the papermaking process shown in Figure 4, for example, or may relate to the cooking system, black liquor treatment system, green liquor treatment system, slaking / causticizing system, and calcination system (lime calcination process) in a pulp production system, as described in JP 2022-12850 A.

[0052] That is, the parameter information may include, as water quality parameters, one or more selected from the group consisting of pH, electrical conductivity, oxidation-reduction potential, zeta potential, turbidity, temperature, foam height, biochemical oxygen demand (BOD), chemical oxygen demand (COD), total organic carbon (TOC), inorganic carbon, absorbance, color, appearance, whiteness, transparency, particle size distribution, degree of aggregation, amount of foreign matter, suspended solids (SS), foam surface area, soiled area in water, amount of air bubbles, amount of glucose, amount of organic acid, amount of active alkali, total titratable alkali, metal or metal ion content, non-metal ion content, amount of starch, amount of calcium, total chlorine content, amount of free chlorine, amount of dissolved oxygen (DO), cation demand, amount of hydrogen sulfide, sulfidity, amount of hydrogen peroxide, ash concentration, respiration rate of microorganisms in the system, viable cell count, spore count, and ATP. These water quality parameters are acquired from any location in the water system W.

[0053] Of the above water quality parameters, "appearance" may be obtained from an RGB color sensor or camera images. "Active alkali content" and "total titratable alkali content" may indicate the alkali content evaluated as sodium content, the alkali content evaluated as calcium content, or the total alkali content. Furthermore, the metal in the "metal or metal ion content" can include heavy metals such as iron (Fe), lead (Pb), gold (Au), platinum (Pt), silver (Ag), copper (Cu), chromium (Cr), cadmium (Cd), mercury (Hg), zinc (Zn), arsenic (As), manganese (Mn), cobalt (Co), nickel (Ni), molybdenum (Mo), tungsten (W), tin (Sn), bismuth (Bi), uranium (U), and plutonium (Pu); alkali metals such as sodium (Na) and potassium (K); alkaline earth metals such as calcium (Ca) and barium (Ba); and base metals such as magnesium (Mg) and aluminum (Al). The "metal or metal ion content" may refer to the content of a specific (one) metal or metal ion, or the content of multiple metals. Furthermore, "non-metal ions" can include ions containing heteroatoms such as silicon (Si), phosphorus (P), sulfur (S), and nitrogen (N) as constituent components, as well as halogen ions such as chlorine (Cl) and bromine (Br).

[0054] The parameter information also includes, as control parameters, the operating speed of the paper machine (machine speed), the filter cloth rotation speed of the raw material dehydrator, the filter cloth rotation speed of the washer, the amount or unit of chemicals added to the water system, the amount or unit of chemicals added to the raw material added to the water system, the amount or unit of chemicals added to equipment related to the water system, the amount or unit of steam used for heating, the temperature of steam used for heating, the pressure of steam used for heating, the flow rate from the seed box, the nip pressure of the press part, the felt vacuum pressure of the press part, the blending ratio of the papermaking raw materials, the amount or unit of broken paper used in the papermaking raw materials, the opening of the screen for the papermaking raw materials, and the rotor and stator of the beater. gap distance between the wire part and the sheet, freeness, degree of beating, draw, line pressure of the press part, gravity dewatering rate of the wire part or a parameter equivalent thereto, forced dewatering rate of the wire part or a parameter equivalent thereto, gravity dewatering rate of the press part, a parameter equivalent to gravity dewatering rate of the press part, a parameter for controlling gravity dewatering rate of the press part, forced dewatering rate of the press part, a parameter equivalent to forced dewatering rate of the press part, a parameter for controlling forced dewatering rate of the press part, concentration of interlayer adhesive, amount of interlayer adhesive or consumption unit, amount of shower water or consumption unit, new water consumption unit, water consumption unit, timing or frequency of cleaning of the wire part, timing or frequency of cleaning of the press part, timing or frequency of cleaning of the dryer part, timing or frequency of changing tools in the paper machine, amount of new water replenishment, amount of screen rejects or a parameter for controlling it, screen differential pressure or a parameter equivalent thereto, amount of cleaner rejects or a parameter for controlling it, amount of fuel for heating or consumption unit, floatator concentration, amount of floatator air or a parameter equivalent thereto, amount of floatator froth or a parameter equivalent thereto load of disperser or equivalent parameters, number of felt singes, flow rate of fluid flowing through the water system, air temperature, amount of steam for heating or unit consumption, amount of calcium carbonate added or unit consumption, amount of calcium bicarbonate added or unit consumption, flow rate or concentration of clarified green liquor, flow rate or concentration of crude green liquor, flow rate or concentration of diluted black liquor, flow rate or concentration of strong black liquor, flow rate or concentration of white liquor, amount of black liquor injected in the black liquor treatment system, black liquor injection concentration in the black liquor treatment system, pulp production, mud filter moisture, amount of lime input or current unit in the slaking / causticizing system, lime production in the calcination system, fuel consumption,The control parameters may include one or more parameters selected from the group consisting of fuel consumption rate, causticization rate in the slaking / causticizing system, caustic input rate or consumption rate in the cooking system, black liquor production rate in the black liquor treatment system, solids amount during evaporation of black liquor in the black liquor treatment system, evaporation rate during evaporation of black liquor in the black liquor treatment system, boiler economizer temperature in the black liquor treatment system, water content of sludge in the green liquor treatment system, specific gravity of black liquor, specific gravity of green liquor, specific gravity of white liquor, specific gravity of weak liquor, specific gravity of dregs, specific gravity of calcium carbonate before calcination, and specific gravity of calcium oxide after calcination. These control parameters are obtained from various equipment related to the aqueous system W. In addition, when the aqueous system W is an aqueous system used in a paper product manufacturing process, examples of the equipment include equipment such as wires and felts in a paper machine that directly add chemicals, and equipment such as a dryer in a papermaking system. Of course, the control parameters are not limited to those described above and may be set appropriately depending on the type of aqueous system W and the type of equipment used in the process. Regarding the above control parameters, the type of "fuel" can be set appropriately, and may be, for example, heavy oil or petroleum coke. Furthermore, the location where the "temperature" in the "temperature of the boiler economizer in the black liquor treatment system" is acquired may be any location within the device. For example, it may be the inlet of the device, the inside of the device, or the outlet of the device.

[0055] The parameter information may also include, as result parameters, one or more selected from the group consisting of unit weight of the paper product (basis weight), yield rate, white water consistency, moisture content of the paper product, amount of steam or unit consumption in the equipment for manufacturing the paper product, steam temperature in the equipment for manufacturing the paper product, steam pressure in the equipment for manufacturing the paper product, thickness of the paper product, ash concentration in the paper product, type of defect in the paper product, number of defects in the paper product, time of paper breakage in the process, freeness, degree of beating, aeration amount, air temperature inside the dryer, splicing rate, number of splices, amount of paper loss, number of defects, defect index, outside air temperature including indoor air temperature, outside humidity including indoor humidity, moisture content of wet paper, moisture content of the product, temperature of the dryer rolls and cylinders, measurements from the five senses sensors, amount of fuel or unit consumption of fuel, calcination rate in the calcination system, causticization rate in the slaking / causticizing system, amount of caustic input in the cooking system, and amount of lime input in the slaking / causticizing system. Among these, the amount of steam in the equipment for manufacturing paper products can be, for example, the amount of steam in the paper machine dryer, the amount of steam in the kraft pulp black liquor evaporator, the amount of steam in the black liquor heater in the kraft pulp digester, or the amount of steam injected to heat the pulp raw material or white water. These result parameters are obtained from various equipment related to the water system W.

[0056] Although some parameters essentially indicate the same thing, they may be classified as two or more of water quality parameters, control parameters, and result parameters depending on the purpose. For example, in a black liquor evaporator, black liquor is heated by indirect heat exchange with steam generated from a boiler, and as a result of the heating, process steam is generated from the black liquor. This process steam is used to heat (concentrate) the concentrated black liquor in the next process. The amount of process steam generated is a result parameter because it is generated from the black liquor, and is used as a control parameter because it is used to heat (concentrate) the concentrated black liquor in the next process. Furthermore, the steam generated from the boiler to heat the black liquor is also used as a control parameter. Note that the two or more parameters acquired by the parameter information acquisition unit are not all substantially identical. For example, in the case where the entire amount of process steam generated from the black liquor is used to heat (concentrate) the concentrated black liquor in the next process, the use of the process steam generated from the black liquor as a result parameter and the amount of steam used to heat (concentrate) the concentrated black liquor as a control parameter is excluded. In such a case, the process steam generated from the black liquor as a result parameter and the amount of steam used to heat (concentrate) the concentrated black liquor as a control parameter are substantially the same. However, when a portion of the process steam generated from the black liquor is used to heat (concentrate) the concentrated black liquor in the next process, the process steam generated from the black liquor as a result parameter and the amount of steam used to heat (concentrate) the concentrated black liquor as a control parameter can be used as two parameters. In such a case, the process steam generated from the black liquor as a result parameter and the amount of steam used to heat (concentrate) the concentrated black liquor as a control parameter are not substantially the same.Furthermore, when the total amount of process steam generated from the black liquor described above is used to heat (concentrate) the concentrated black liquor in the next step, the two parameters used are the process steam generated from the black liquor as a result parameter and the amount of steam used to heat (concentrate) the concentrated black liquor as a control parameter, and if other parameters such as the pH of the water system as a water quality parameter are further combined, the multiple parameters may be substantially the same. In addition, for example, freeness and beating degree are also the same parameters, but can be included in both the control parameter and the result parameter.

[0057] These parameters may be quantitative or qualitative. When qualitative parameters are used, they may be assigned numerical values ​​and treated as quantitative data.

[0058] Note that water quality parameters, control parameters, and result parameters each encompass multiple parameters. Two or more parameters included in the parameter information can be independently selected from the water quality parameters, control parameters, and result parameters. Two or more parameters can be selected from only water quality parameters, control parameters, and result parameters (e.g., water pH and temperature), or two or more parameters can be selected from a combination of two or three of water quality parameters, control parameters, and result parameters (e.g., pH, press pressure in the press part, and thickness of the paper product). However, identical parameters (e.g., the pH of water at point A and the pH of water at point A) should not be selected (however, for example, the pH of water at point A and the pH of water at point B, which are measured at different points, may be selected).

[0059] As described above, while acquiring parameter information, the relationship model information acquisition unit 232 of the information processing device 2 acquires relationship model information (activity A102). Note that the order in which activity A101 and activity A102 are performed is arbitrary; activity A101 can be performed before activity A102, activity A102 can be performed before activity A101, or both activities can be performed in parallel (simultaneously).

[0060] This relationship model information indicates the relationship between the predicted result or an index related to the predicted result and two or more parameters, which was created in advance. Note that "in advance" refers to before the predicted result or an index related to the predicted result is estimated, and may be during actual operation of the water system W, before actual operation, or any other time before the predicted result or an index related to the predicted result is estimated.

[0061] Furthermore, the "expected results" related to the relationship model information refer to various phenomena related to the water system W. The estimation unit 233 of the information processing device 2 of this embodiment estimates the expected results or indicators related to the expected results, and the expected results related to the relationship model information also correspond to the results estimated by this estimation unit 233.

[0062] The expected results can be set appropriately depending on the subject of estimation, but if the aqueous system W is an aqueous system in a process for producing paper products, the expected results may be as shown below.

[0063] That is, in this embodiment, the expected result may relate to the quality or energy amount of paper products and / or materials related to the manufacture of paper products. Here, the quality may relate to one or more of the following: the number of defects, paper strength, joint rate, sizing degree, air permeability, smoothness, ash content, color tone, whiteness, formation, odor, causticization rate, burnt rate, kappa number, freeness, and moisture content of paper products and / or materials related to the manufacture of paper products. Furthermore, the energy amount may relate to one or more of the following: steam volume, steam consumption rate, fuel volume, and fuel consumption rate when manufacturing paper products and / or materials related to the manufacture of paper products. As mentioned above, "materials related to the manufacture of paper products" does not refer to the paper products themselves, but rather to various raw materials and intermediates used in the manufacture of paper products. In other words, raw materials used in the manufacture of paper products include white water, calcium oxide, and the like generated during the paper product manufacturing process. That is, the causticization rate and burnt rate described above may relate to these raw materials. Furthermore, intermediates used in the production of paper products include, for example, pulp produced before the papermaking process. The above-mentioned predicted results may be evaluation items related to such pulp. The predicted results may be the items listed above themselves, or a combination of the items listed above or related items. For example, a related item related to the above-mentioned calcination rate may be the amount of fuel used to achieve a predetermined calcination rate (typically, the amount of fuel used in the kiln when obtaining calcium oxide from calcium carbonate in the caustic slaking process). The type of fuel for the "fuel amount" in the predicted results is not particularly limited, and may be heavy oil, petroleum coke, etc.

[0064] Furthermore, the expected result in this embodiment may correspond to the result parameter described above. In this case, the result parameter is not usually included in the parameter information (two or more parameters).

[0065] Examples of relationship model information include, but are not limited to, a function showing the relationship between an expected result or an index related to the expected result and two or more parameters, a lookup table, or a learned model of the relationship between an expected result or an index related to the expected result and two or more parameters.

[0066] It is assumed that two or more parameters included in the parameter information acquired by the parameter information acquisition unit 231 and two or more parameters included in the parameter information used in the relationship model information are common to each other. As described above, the water quality parameters, control parameters, and result parameters each encompass a plurality of parameters. "Two or more of the parameters are common" may mean that two or more of the water quality parameters, control parameters, and result parameters (e.g., two or more water quality parameters only; pH ​​and temperature of water) are common to each other, or that a combination of water quality parameters, control parameters, and result parameters (e.g., one water quality parameter and one control parameter; e.g., pH of water and pressing pressure of the pressing part) is common to each other, or that all of the water quality parameters, control parameters, and result parameters (e.g., one water quality parameter, one control parameter, and one result parameter) are common to each other.

[0067] The relationship model information is created, for example, as follows: Prior to estimating the expected result or related indices, preliminary measurement results corresponding to the expected result or preliminary measurement indices related to the preliminary result are measured. Furthermore, two or more parameters, each of which is a water quality parameter, a control parameter, and a result parameter, are measured in the same water system. Multiple data sets of these preliminary measurement results or preliminary measurement indices and parameters are prepared, for example, by changing the day or time of measurement, so that the preliminary measurement results or preliminary measurement indices and parameters fluctuate. Next, the preliminary measurement results or preliminary measurement indices are assumed to be functions of two or more parameters, and the function form and coefficients are determined by comparing them with the preliminary measurement results or preliminary measurement indices, thereby constructing the relationship model information. Here, the relationship model information may be a model obtained by performing predetermined processing on the indices related to the expected result or preliminary confirmation result corresponding to the expected result and the two or more parameters. The processing methods that can be used here include regression analysis (linear model, generalized linear model, generalized linear mixed model, ridge regression, lasso regression, elastic net, support vector regression, projection pursuit regression, principal component regression, etc.), time series analysis (VAR model, SVAR model, ARIMAX model, SARIMAX model, state space model, HMM model, etc.), decision trees (decision tree, regression tree, random forest, XGBoost, Light GBM, etc.), neural networks (simple perceptron, multilayer perceptron, DNN, CNN, RNN, LSTM, GAN, VAE, etc.), Bayes (naive Bayes, Bayesian optimization, Bayesian network, etc.), clustering (k-means, k-means++, etc.), classification (k-nearest neighbor method, support vector machine, etc.), ensemble learning (Boosting, Adaboost, etc.), etc.

[0068] In one embodiment, the relationship model information is preferably a model obtained by regression analysis of a pre-check result corresponding to a forecast result or an index related to the pre-check result with two or more parameters. Note that the number of sample sets used in the regression analysis is not particularly limited.

[0069] It is preferable to create the relationship model information for the same water system as the water system for which the expected results are to be estimated. Furthermore, for example, when the water quality of the water system changes significantly even within the same equipment (for example, when the pulp, which is the raw material for papermaking, is changed in the papermaking system of a paper mill), it is preferable to create and use the relationship model information for the water system after the water quality change.

[0070] From this perspective, during operation of the water system W, expected results or related indicators and two or more parameters may be measured regularly or irregularly, and relationship model information may be created each time, or data may be added to update the relationship model information.

[0071] Such relationship model information may be created and updated, for example, by the function of the relationship model information creation unit 235 included in the information processing device 2. That is, the relationship model information creation unit 235 can create relationship model information suitable for the information processing of this embodiment by performing predetermined arithmetic processing on the acquired expected results or related indicators and two or more parameters. Note that this relationship model information may also be created manually by, for example, an operator.

[0072] An example of creating relationship model information will be described below. Specifically, the case will be described where an index a related to the predicted result is calculated using water quality parameter x, water quality parameter y, and control parameter z, and the index a related to this predicted result and the number of times a trouble occurs A (number of times / day) as the predicted result are calculated.

[0073] When creating the relationship model information, the water quality parameter x, water quality parameter y, and control parameter z shown in Table 1 below are measured, and the number of times a trouble occurs A is also measured, obtaining a total of 30 sets of data. The obtained data is shown in Table 1 below. Of course, the number of sets of data is not limited to this and may be set as appropriate.

[0074] The indicator a related to the expected outcome is "parameters" x, y, z, and b nare the coefficients of x, y, and z, and a0 and b0 are constants, and are expressed by the following equation (1).

number

[0075] A negative binomial regression analysis was performed based on the actual measured value of the number of times a problem occurred and the index a related to the predicted outcome in equation (1). The index a related to the predicted outcome calculated from this is also shown in Table 1. Figure 7 is a plot of the number of times a problem occurred versus the index a related to the predicted outcome for a total of 30 sets of data. The correlation coefficient between the number of times a problem occurred and the index a related to the predicted outcome was r = 0.78 (p < 0.05), indicating a strong correlation.

[0076] [Table 1]

[0077] FIG. 8 is a graph showing the magnitude of the influence of each parameter on the index a related to the expected result. In this example, the control parameter z has the greatest influence on the index a related to the expected result, followed by the water quality parameter x and the water quality parameter y. The results shown in FIG. 8 are obtained by performing a negative binomial regression analysis using the standardized scores of each parameter. The standardized score can be calculated by dividing the standard deviation by (individual value - average value). In this way, the relationship model information of this embodiment may be weighted based on the degree of influence (contribution) of each parameter (explanatory variable) on the index (target variable).

[0078] In addition, the function of the index a related to the expected results used in the regression analysis, and the water quality parameter x, water quality parameter y, and control parameter z is not limited to the above formula (1), and general formula (2) can also be used.

number

[0079] After acquiring the parameter information and the relationship model information in this way, the estimation unit 233 of the information processing device 2 presents the predicted result or an index related to the predicted result to the user as the predicted result based on the acquired parameter information and relationship model information (activity A103). Here, the predicted result is presented to the user in association with each parameter included in the parameter information.

[0080] Typically, such inference results can be output by inputting the acquired parameter information into relationship model information. That is, activity A103 can be realized by presenting the expected results or indicators related to the expected results output by input processing of such relationship model information in a manner that can be understood by the user.

[0081] Fig. 9 is a diagram showing an example of an estimation result presented to a user. In Fig. 9, parameter information (parameters 1 to 5) used by the estimation unit 233 is associated with an expected result index and displayed on the display unit 34 of the user terminal 3. That is, a predetermined screen showing the estimation result may be displayed on the display unit 34 based on the function of the estimation unit 233. Furthermore, the screen shown in Fig. 9 includes forms F1 to F5 corresponding to each parameter and a form F100 corresponding to an expected result index (an index related to the expected result), and each form shows the parameter (measured value) used by the estimation unit 233 in the estimation process and the estimation result (estimated value).

[0082] In the information processing method of this embodiment, the estimation unit 233 receives from the user a change to at least one of the inference results and parameters presented by the estimation unit 233 (activity A104). Based on the content of the received change and the relationship model information, the system estimates the fluctuations of the inference results and parameters for which no change was accepted from the user (activity A105).

[0083] The processing of the activities A104 and A105 as described above can be realized by the function of the simulation unit 234. Specific aspects will be described below.

[0084] That is, a user who comes across a screen such as that shown in Fig. 9 inputs the inference result and at least one change to each parameter. In the example shown in Fig. 9, forms F1 to F5 and F100 are configured so that various numerical values ​​can be input, and the above-mentioned activity A104 is achieved by the user arbitrarily inputting numerical values ​​into the forms.

[0085] 10 is a diagram showing the results of a simulation when a change is input for parameter 1. That is, when a user inputs a value V10 for form F1, the value shown in form F100, which indicates the expected result index, is changed to an estimated value of V200. This estimated value (V200) shown in form F100 can be obtained by inputting the input value (V10) for parameter 1 and the measured values ​​(V2 to V5) for parameters 2 to 5 into the relationship model information described above.

[0086] 10 shows an example in which the parameters 2 to 5 are calculated without being changed from the values ​​corresponding to the parameter information. That is, in this embodiment, it is also possible to perform a simulation after fixing the measurement results and the values ​​of each parameter to the contents presented as the estimated results. The control over such value changes can be set arbitrarily by the user.

[0087] Of course, in the example shown in Fig. 10, it is also possible to accept changes to some or all of parameters 2 to 5. Even in this case, as described above, the estimated value of the expected result index is calculated based on the input value of each parameter.

[0088] Meanwhile, the simulation unit 234 may execute the following process. FIG. 11 illustrates the results of a simulation performed when a change to the expected result index is input. In the example illustrated in FIG. 11, when a user inputs the expected result index value V300 into form F100, combinations of values ​​that satisfy the expected result index value V300 are presented in forms F1 to F5 corresponding to parameters 1 to 5. The combinations of values ​​presented in forms F1 to F5 are derived based on the value V300 and the relationship model information. That is, such combinations of values ​​can be obtained by back-calculating from the expected result index value V300 based on the relationship model information. Note that, when multiple combinations of estimated values ​​V10, 20, 30, 40, and 50 are expected, all or some of the expected combinations may be presented, or the most feasible combination may be presented. Note that the feasibility here may be evaluated based on the operating history of the water system W, etc. For example, a highly feasible combination may be a combination that requires a low cost to achieve each estimated value. Also, in the example shown in Fig. 11, it is possible to perform a simulation by fixing some of the parameters 1 to 5 to the contents presented as the estimation results.

[0089] The manner in which the estimation results and the parameters are changed is not limited to the manner shown in Fig. 10 or 11 (inputting numerical values ​​into a form). For example, it is assumed that the estimation unit 233 displays the estimation results and at least a part of the parameters as a graph (such as a bar graph). In this case, the simulation unit 234 can accept changes to the estimation results and the parameters when the user operates to change the shape of the graph. The presentation of the estimated results of the fluctuations in this case may also be realized by changing the shape of the graph for the corresponding item.

[0090] Furthermore, the above simulation step may be realized by calculation using shap. In an exemplary embodiment, a shap value (Shapley value) for each parameter of the relationship model information is calculated in advance, and the estimated results and trial calculations for each parameter can be performed based on the shap value. For example, in the example of FIG. 10 described above, when a user changes a parameter, a trial calculation of the value of the expected result index can be performed based on the shap value calculated in advance. On the other hand, in the example of FIG. 11 described above, when a user changes the value of the expected result index, a calculation can be performed based on the shap value to find a value that approximates the value to which the expected result index was changed.

[0091] Furthermore, the simulation unit 234 of the information processing device 2 may display the simulation results of parameters, forecast results, etc. in the following manner.

[0092] Below is an example of estimating defects (number of defects) in a papermaking process using five parameters (parameter information).The five parameters are the paper machine operating speed (machine speed), the amount of steam used in process B, the turbidity of water used in process A, the SS (suspended solids) of water used in process C, and the chemical oxygen demand (COD) of water used in process D.A predetermined weight is assigned to each parameter, and relationship model information is created to evaluate the number of defects.

[0093] FIG. 12 shows the results of a simulation in which the "turbidity of water used in process A" is reduced by 30% after the estimation unit 233 estimates the number of defects (defect index) based on each parameter. FIG. 12 is a diagram showing the simulation results related to the defect index. The simulation results shown in FIG. 12 are shown as a graph, illustrating how the defect index changes when the "turbidity of water used in process A" is reduced by 30% based on a data set accumulated in the past. Prior to the display of the simulation results shown in FIG. 12, an increase or decrease in the "turbidity of water used in process A" can be input from the user terminal 3, and a graph such as that shown in FIG. 12 is displayed based on this input. In this graph, the horizontal axis represents the value before the change of the parameter (the turbidity of water used in process A), and the vertical axis represents the amount of change in the defect index. In addition, in FIG. 12, the results of a simulation based on data accumulated as past relationship model information and the results of a simulation based on the latest data set (i.e., the most recent data set used by the estimation unit 233 for estimation) are shown in different ways.

[0094] That is, a user viewing the graph in Figure 12 can intuitively understand from the distribution shown on the graph how the number of defects will change when the turbidity of the water in the current Process A is reduced by a predetermined amount. The example shown in Figure 12 has also been verified as an actual process. As a result of this verification, it has been confirmed that reducing the turbidity in Process A reduces the number of defects in paper products, which correlates with the simulation results.

[0095] As described above, according to the information processing method executed by the information processing device 2 (information processing system 1) of this embodiment, the function of the simulation unit 234 makes it easier to manage events related to the water system W. In other words, according to this embodiment, it can be said that it becomes easier to intuitively grasp the effects of varying various parameters, etc., regarding events (expected results) related to the water system W. From this perspective, it can be said that the information processing device 2 (information processing system 1) of this embodiment can facilitate the management of estimated expected results, etc.

[0096] 4. Variations In Section 4, a modified example of the information processing method of the information processing system 1 and the like described above will be described.

[0097] The above-described embodiment has been described as a configuration of the information processing system 1, but an information processing method executed by the information processing system may also be provided, which includes steps of executing processing of each unit of the information processing system. Also, a program for causing a computer to execute processing of each unit of the information processing system 1 may also be provided.

[0098] In the above-described embodiment, an information processing method using relevance model information is described, but the information associated when creating the relevance model information is not limited to the above. In other words, the relevance model information used in this embodiment may be associated with various other conditions, such as weather conditions, regional conditions, and conditions related to the age of the facility.

[0099] In the above embodiment, the information processing system 1 performs various storage and control operations, but multiple external devices may be used instead of the information processing system 1. That is, various types of information may be distributed and stored in multiple external devices using blockchain technology or the like.

[0100] In the above embodiment, the information processing device 2 and the user terminal 3 function as separate devices. However, the user terminal 3 itself may have various functions as the control unit 23 of the information processing device 2. However, the computer may also function as a standalone computer to acquire various information and perform processes such as estimation and simulation.

[0101] Although the embodiments of the present invention have been described above, these are merely examples of the present invention, and various other configurations may be adopted. Furthermore, the present invention is not limited to the above-described embodiments, and modifications and improvements within the scope of achieving the object of the present invention are included in the present invention. [Explanation of symbols]

[0102] 1: Information processing system 2: Information processing equipment 3: User terminal 4: Measuring equipment 20: Communication bus 21: Communications Department 22: Storage section 23: Control section 30: Communication bus 31: Communications Department 32: Storage section 33: Control section 34:Display section 35: Input section 231: Parameter information acquisition unit 232: Relationship model information acquisition unit 233: Guessing part 234: Simulation Department 235: Relationship model information creation unit 236: Memory management department F1~F5, F100: Forms W: Water

Claims

1. 1. An information processing system for predicting potential future outcomes in or derived from a water system, comprising: The system includes a parameter information acquisition unit, a relationship model information acquisition unit, an estimation unit, and a simulation unit, the parameter information acquisition unit acquires, as parameter information, two or more parameters selected from the group consisting of a water quality parameter, a control parameter, and a result parameter; Here, the water quality parameters are parameters related to the water quality of the water system, the control parameters are parameters relating to control conditions of the aqueous system, equipment related to the aqueous system, or raw materials added to the aqueous system; The result parameter is a parameter having a different meaning from the expected result, and is a parameter relating to a result that has occurred in the water system, equipment related to the water system, or raw materials added to the water system, or that has been derived from the water system, equipment related to the water system, or raw materials added to the water system, the relationship model information acquisition unit acquires relationship model information, created in advance, that indicates a relationship between the expected result or an index related to the expected result and the two or more parameters; the estimation unit presents the expected result or an index related to the expected result to a user as an expected result based on the acquired parameter information and the acquired relationship model information; wherein the estimation result is presented to the user in association with each parameter included in the parameter information; The simulation unit receives from the user a change to at least one of the estimation results and each of the parameters presented by the estimation unit, and estimates fluctuations for items of the estimation results and each of the parameters for which no changes were accepted from the user based on the content of the accepted change and the relationship model information.

2. 2. The information processing system according to claim 1, An information processing system, wherein the relationship model information is a model obtained by regression analysis, time series analysis, decision tree, neural network, Bayesian, clustering, classification or ensemble learning between a pre-confirmation result corresponding to the forecast result or an indicator related to the pre-confirmation result and the two or more parameters.

3. 2. The information processing system according to claim 1, The information processing system, wherein the aqueous system is an aqueous system in a process for producing paper products and / or materials related to the production of paper products.

4. 4. The information processing system according to claim 3, The parameter information includes, as the water quality parameters, one or more selected from the group consisting of pH of the water system, electrical conductivity, oxidation-reduction potential, zeta potential, turbidity, temperature, bubble height, biochemical oxygen demand (BOD), chemical oxygen demand (COD), total organic carbon (TOC), inorganic carbon, absorbance, color, appearance, whiteness, transparency, particle size distribution, degree of aggregation, amount of foreign matter, suspended solids (SS), foam area on the water surface, area of ​​dirt in the water, amount of bubbles, amount of glucose, amount of organic acid, amount of active alkali, total amount of titratable alkali, metal or metal ion content, non-metal ion content, amount of starch, amount of calcium, total chlorine amount, amount of free chlorine, amount of dissolved oxygen (DO), cation demand, amount of hydrogen sulfide, degree of sulfidation, amount of hydrogen peroxide, ash concentration, respiration rate of microorganisms in the system, viable cell count, spore count, and ATP.

5. 4. The information processing system according to claim 3, The parameter information includes, as the control parameters, the operating speed (machining speed) of the papermaking machine, the filter cloth rotation speed of the raw material dehydrator, the filter cloth rotation speed of the washer, the amount or unit of chemicals added to the water system, the amount or unit of chemicals added to the raw material added to the water system, the amount or unit of chemicals added to the equipment related to the water system, the amount or unit of steam used for heating, the temperature of steam used for heating, the pressure of steam used for heating, the flow rate from the seed box, the nip pressure of the press part, the felt vacuum pressure of the press part, the blending ratio of the papermaking raw materials, the amount or unit of broken paper used in the papermaking raw materials, the opening of the screen for the papermaking raw materials, and the rotor of the beater. and the stator, freeness, degree of beating, draw, line pressure of the press part, gravity dewatering rate of the wire part or a parameter equivalent thereto, forced dewatering rate of the wire part or a parameter equivalent thereto, gravity dewatering rate of the press part, a parameter equivalent to gravity dewatering rate of the press part, a parameter for controlling gravity dewatering rate of the press part, forced dewatering rate of the press part, a parameter equivalent to forced dewatering rate of the press part, a parameter for controlling forced dewatering rate of the press part, concentration of interlayer adhesive, amount of interlayer adhesive or unit consumption, amount of shower water or unit consumption, new water consumption unit, water consumption unit, timing or frequency of cleaning of the wire part, timing or frequency of cleaning of the press part, timing or frequency of cleaning of the dryer part, timing or frequency of changing tools in the paper machine, amount of new water replenishment, amount of screen rejects or a parameter for controlling it, screen differential pressure or a parameter equivalent thereto, amount of cleaner rejects or a parameter for controlling it, amount of fuel for heating or unit consumption, floatator concentration, floatator air volume or a parameter equivalent thereto, floatator froth volume or equivalent parameters, disperser load or a parameter equivalent thereto, number of times of felt singeing, flow rate of fluid flowing through the water system, air temperature, amount of steam for heating or consumption unit, amount of calcium carbonate added or consumption unit, amount of calcium bicarbonate added or consumption unit, flow rate or concentration of clarified green liquor, flow rate or concentration of crude green liquor, flow rate or concentration of weak black liquor, flow rate or concentration of strong black liquor, flow rate or concentration of white liquor, amount of black liquor injected in the black liquor treatment system, black liquor injection concentration in the black liquor treatment system, pulp production amount, mud filter moisture, amount of lime input or current unit in the slaking / causticizing system, lime production amount in the calcination system,An information processing system comprising one or more items selected from the group consisting of fuel consumption, fuel consumption unit, causticization rate in a slaking / causticizing system, caustic input amount or unit consumption in a cooking system, black liquor generation amount in a black liquor treatment system, solid amount when evaporating black liquor in a black liquor treatment system, evaporation multiple when evaporating black liquor in a black liquor treatment system, boiler economizer temperature in a black liquor treatment system, moisture content of sludge in a green liquor treatment system, specific gravity of black liquor, specific gravity of green liquor, specific gravity of white liquor, specific gravity of weak liquor, specific gravity of dregs, specific gravity of calcium carbonate before calcination, and specific gravity of calcium oxide after calcination.

6. 4. The information processing system according to claim 3, The parameter information includes, as the result parameters, one or more selected from the group consisting of unit weight (basis weight) of the paper product (grams), yield rate, white water concentration, moisture content of the paper product, amount of steam or unit consumption in the equipment for manufacturing the paper product, steam temperature in the equipment for manufacturing the paper product, steam pressure in the equipment for manufacturing the paper product, thickness of the paper product, ash concentration in the paper product, type of defect in the paper product, number of defects in the paper product, timing of paper breakage in the process, freeness, degree of beating, aeration amount, air temperature inside the dryer, splicing rate, number of splices, amount of paper breakage, number of defects, defect index, outside air temperature including indoor air temperature, outside humidity including indoor humidity, moisture content of wet paper, moisture content of product, temperature of the dryer rolls and cylinders, measurements by five senses sensors, amount of fuel or unit consumption of fuel, calcination rate in the calcination system, causticization rate in the slaking / causticizing system, amount of caustic added in the cooking system, and amount of lime added in the slaking / causticizing system.

7. 4. The information processing system according to claim 3, The expected results relate to the quality or energy content of the paper product and / or materials associated with the production of the paper product; The quality is one or more selected from the group consisting of the number of defects, paper strength, joint rate, sizing degree, air permeability, smoothness, ash content, color tone, whiteness, formation, odor, causticization rate, burnt rate, kappa number, freeness, and moisture content of the paper product and / or materials related to the production of the paper product; An information processing system in which the amount of energy relates to one or more selected from the group consisting of steam amount, steam consumption rate, fuel amount, and fuel consumption rate when producing the paper product and / or materials related to the production of the paper product.

8. An information processing method executed by an information processing system, A method comprising the step of executing processing of each part of the information processing system according to any one of claims 1 to 7.

9. A program, A program for causing a computer to execute processing of each unit of the information processing system according to any one of claims 1 to 7.

Images