Information processing system, information processing method, and program

Abstract

An information processing system, etc. that facilitates management of events related to a water system is provided. According to an aspect of the present disclosure, an information processing system that executes information processing related to an event in a water system or derived from the water system is provided in which the information processing system including: a relationship model information acquisition unit configured to acquire relationship model information indicating a relationship between an index related to the event, which has been created in advance, and two or more parameters, each of the two or more parameters being a parameter selected from a group consisting of a water quality parameter, a control parameter, and a result parameter, the water quality parameter being a parameter related to water quality of the water system, the control parameter being a parameter related to the water system, a facility related to the water system, or a control condition of a raw material to be added to the water system, the result parameter being a parameter that does not correspond to the index and relates to a result having arisen in the water system, a facility related to the water system, or a raw material to be added to the water system, or a result having been derived from the water system, a facility related to the water system, or a raw material to be added to the water system; a data set specification unit configured to specify a data set in which the index related to the event is associated with the two or more parameters; and an influence degree specification unit configured to specify an influence degree on the event for each of the parameters associated in the data set by comparing the relationship model information with the data set.

Description

INFORMATION PROCESSING SYSTEM, INFORMATION PROCESSING METHOD, AND PROGRAM

[0001] CROSS REFERENCE TO RELATED APPLICATIONS The present application claims priority to Japanese Patent Application No. 2024-120217, filed July 25, 2024, the contents of which are incorporated herein by reference in their entirety. The present disclosure relates to an information processing system, an information processing method, and a program.

[0002] Various products including paper products are manufactured in processes using a water system. In such processes, from the perspective of preventing or reducing occurrence of trouble and adverse effects on products during operations, it is standard practice to predict trouble in advance. In connection with this, techniques for estimating product quality and the like in the papermaking process, etc. are known. For example, Patent Document 1 indicates a technique for estimating a potential result, etc. by using predetermined parameter information and relationship model information.

[0003] [Patent Document 1] JP 2022-123880A

[0004] However, trouble occurs in various processes, it is important to specify specific factors with high accuracy. From this perspective, a technology that offers ease for use for system users is desired.

[0005] In view of the above circumstances, the present disclosure provides an information processing system, etc. that facilitates the management of events related to a water system.

[0006] According to an aspect of the present disclosure, an information processing system that executes information processing related to an event in a water system or derived from the water system is provided in which the information processing system including: a relationship model information acquisition unit configured to acquire relationship model information indicating a relationship between an index related to the event, which has been created in advance, and two or more parameters, each of the two or more parameters being a parameter selected from a group consisting of a water quality parameter, a control parameter, and a result parameter, the water quality parameter being a parameter related to water quality of the water system, the control parameter being a parameter related to the water system, a facility related to the water system, or a control condition of a raw material to be added to the water system, the result parameter being a parameter that does not correspond to the index and relates to a result having arisen in the water system, a facility related to the water system, or a raw material to be added to the water system, or a result having been derived from the water system, a facility related to the water system, or a raw material to be added to the water system; a data set specification unit configured to specify a data set in which the index related to the event is associated with the two or more parameters; and an influence degree specification unit configured to specify an influence degree on the event for each of the parameters associated in the data set by comparing the relationship model information with the data set.

[0007] According to the above-mentioned aspects, an information processing system, etc. that facilitates the management of events related to a water system is provided.

[0008] Furthermore, the present disclosure may be provided in each of the following aspects.

[0009] (1) An information processing system that executes information processing related to an event in a water system or derived from the water system, the information processing system comprising: a relationship model information acquisition unit configured to acquire relationship model information indicating a relationship between an index related to the event, which has been created in advance, and two or more parameters, each of the two or more parameters being a parameter selected from a group consisting of a water quality parameter, a control parameter, and a result parameter, the water quality parameter being a parameter related to water quality of the water system, the control parameter being a parameter related to the water system, a facility related to the water system, or a control condition of a raw material to be added to the water system, the result parameter being a parameter that does not correspond to the index and relates to a result having arisen in the water system, a facility related to the water system, or a raw material to be added to the water system, or a result having been derived from the water system, a facility related to the water system, or a raw material to be added to the water system; a data set specification unit configured to specify a data set in which the index related to the event is associated with the two or more parameters; and an influence degree specification unit configured to specify an influence degree on the event for each of the parameters associated in the data set by comparing the relationship model information with the data set.

[0010] (2) The information processing system according to (1), wherein: the influence degree specification unit is configured to specify the influence degree by performing a predetermined calculation processing to each of the parameters, and the calculation processing includes at least one of differential processing, statistical processing, and processing using a trained model.

[0011] (3) The information processing system according to (1) or (2), wherein: the index and the two or more parameters associated in the data set are more recently obtained than the index and the two or more parameters associated in the relationship model information.

[0012] (4) The information processing system according to any one of (1) to (3), wherein: the relationship model information is a model obtained from a regression analysis, a time series analysis, a decision tree, a neural network, Bayes, clustering, classification, or ensemble learning, between a prior measurement result corresponding to the index or an index related to the prior measurement result and the two or more parameters.

[0013] (5) The information processing system according to any one of (1) to (4), further comprising: a parameter information acquisition unit configured to acquire, as parameter information, two or more parameters selected from a group consisting of the water quality parameter, the control parameter, and the result parameter; and an estimation unit configured to estimate the index based on the acquired parameter information and the relationship model information, wherein in the data set, the index estimated by the estimation unit is associated with the two or more parameters acquired by the parameter information acquisition unit.

[0014] (6) The information processing system according to any one of (1) to (5), further comprising: a countermeasure presentation unit configured to present countermeasure information related to a corresponding parameter in accordance with the influence degree specified by the influence degree specification unit

[0015] (7) The information processing system according to any one of (1) to (6), wherein: the water system is a water system in a process of manufacturing a paper product and / or a material related to manufacture of a paper product.

[0016] (8) The information processing system according to (7), wherein: the two or more parameters include, as the water quality parameter, one or more selected from a group consisting of pH of the water system, electrical conductivity, an oxidation-reduction potential, a zeta potential, turbidity, temperature, a foam height, a biochemical oxygen demand (BOD), a chemical oxygen demand (COD), total organic carbon (TOC), inorganic carbon, absorbance, color, appearance, whiteness, transparency, a particle size distribution, a degree of flocculation, an amount of foreign matter, suspended solids (SS), a foaming area on a water surface, an area of underwater contamination, an amount of bubbles, an amount of glucose, an amount of organic acids, an amount of active alkali, total titrated alkali, metal or a metal ion content, a non-metal ion content, an amount of starch, an amount of calcium, an amount of total chlorine, an amount of free chlorine, an amount of dissolved oxygen (DO), a cationic demand, an amount of hydrogen sulfide, a degree of sulfidation, an amount of hydrogen peroxide, ash concentration, a respiration rate of microorganisms in the system, a viable bacterial count, a spore-forming bacterial count, and ATP.

[0017] (9) The information processing system according to (7) or (8), wherein: the two or more parameters include, as the control parameter, one or more selected from the group consisting of an operating speed (a papermaking speed) of a paper machine, a rotational speed of a filter cloth of a raw material dehydrator, a rotational speed of a filter cloth of a cleaning machine, an additive amount of chemicals or intensity to the water system, an additive amount of chemicals or intensity relative to a raw material to be added to the water system, an additive amount of chemicals or intensity relative to a facility related to the water system, an amount of vapor or intensity for heating, temperature of vapor for heating, pressure of vapor for heating, a flow rate from a seed box, nip pressure of a press part, felt vacuum pressure of a press part, a blending ratio of a papermaking raw material, a blended amount or intensity of waste sheets of papermaking raw material, a mesh size of a screen for a papermaking raw material, a gap distance between a rotor and a stator of a beater, freeness, a degree of beating, draw, linear pressure of a press part, a gravity dewatering amount of a wire part or equivalent parameters, a forced dewatering amount of a wire part or equivalent parameters, a gravity dewatering amount of a press part, parameters equivalent to a gravity dewatering amount of a press part, parameters for controlling the gravity dewatering amount of a press part, a forced dewatering amount of a press part, parameters equivalent to the forced dewatering amount of a press part, parameters for controlling the forced dewatering amount of a press part, concentration of interlayer adhesive, an amount or intensity of interlayer adhesive, shower water amount or intensity, fresh water intensity, water usage intensity, timing or frequency of cleaning a wire part, timing or frequency of cleaning a press part, timing or frequency of cleaning a dryer part, timing or frequency of changing tools of a paper machine, an fresh water replenishment amount, an amount of rejects from the screen or parameters for controlling them, differential pressure across the screen or a parameter equivalent thereto, an amount of rejects from the cleaner or a parameter for controlling them, a fuel amount or intensity for heating, concentration in a floatater, an amount of air in the floatater or a parameter equivalent thereto, an amount of froth in a floatater or parameters equivalent thereto, the load of a disperser or a parameter equivalent thereto, the number of felt singeing, a flow rate of fluid flowing through the water system, air temperature, an amount of vapor or intensity for heating, an amount or intensity of calcium carbonate to be added, an amount or intensity of calcium bicarbonate to be added, a flow rate or concentration of clarifying green liquor, a flow rate or concentration of raw green liquor, a flow rate or concentration of dilute black liquor, a flow rate or concentration of concentrated black liquor, a flow rate or concentration of white liquid, black liquor injection rate in a black liquor treatment system, black liquor injection concentration in a black liquor treatment system, an amount of pulp production, a moisture content of a mud filter, lime input or intensity in a slaking / causticizing system, an amount of lime production in a calcination system, fuel usage, a fuel usage intensity, causticizing efficiency in a slaking / causticizing system, caustic input or intensity in a cooking system, an amount of black liquor generated in a black liquor treatment system, an amount of solids in evaporating black liquor in a black liquor treatment system, a evaporation factor in evaporating black liquor in a black liquor treatment system, temperature of an economizer in a boiler in a black liquor treatment system, water 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 the calcium carbonate before calcination, and specific gravity of the calcium oxide after calcination.

[0018] (10) The information processing system according to any one of (7) to (9), wherein: the two or more parameters include, as the result parameter, one or more selected from a group consisting of a unit weight (grammage) of the paper product, a yield, white water concentration, moisture content of the paper product, an amount of vapor or intensity in a facility manufacturing the paper product, temperature of vapor in a facility manufacturing the paper product, pressure of vapor in a facility manufacturing the paper product, thickness of the paper product, concentration of ash in the paper product, a type of defect of the paper product, the number of defects in the paper product, a timing of paper breakage in a process, freeness, a degree of beating, an amount of aeration, air temperature in a dryer, a joint rate, the number of joints, an amount of paper loss, the number of flaws, a flaw index, outdoor temperature including indoor temperature, outdoor humidity including indoor humidity, wet paper moisture content, product moisture content, temperature of rolls and cylinders in the dryer, a five-sense sensor measurement, a fuel amount or a fuel usage intensity, a calcination rate in a calcination system, a causticizing efficiency in a slaking / causticizing system, caustic input in a cooking system, and lime input in a slaking / causticizing system.

[0019] (11) The information processing system according to any one of (7) to (10), wherein: the index relates to quality or an amount of energy of the paper product and / or a material associated with manufacture of the paper product, the quality relates to one or more selected from a group consisting of a number of defects, paper strength, a joint rate, a sizing degree, air permeability, smoothness, an ash content, a tone, whiteness, formation, odor, causticizing efficiency, calcination rate, a kappa value, freeness, and a moisture percentage of the paper product and / or the material associated with the manufacture of the paper product, and the amount of energy relates to one or more selected from a group consisting of an amount of vapor, a vapor intensity, a fuel amount, and a fuel usage intensity in manufacturing the paper product and / or the material associated with the manufacture of the paper product.

[0020] (12) An information processing method executed by an information processing system, comprising each step of executing processing of each unit of the information processing system according to any one of (1) to (11).

[0021] (13) A program configured to cause a computer to execute processing of each unit of the information processing system according to any one of (1) to (11). Of course, the present disclosure is not limited to the above aspects.

[0022] FIG. 1 show an overall configuration of an information processing system 1.FIG. 2 shows a hardware configuration of an information processing apparatus 2.FIG. 3 shows a hardware configuration of a user terminal 3.FIG. 4 is an example of a papermaking process to which an information processing system 1 can be applied.FIG. 5 is a functional block diagram showing functions of an information processing apparatus 2.FIG. 6 is an activity diagram showing a flow of information processing using an information processing apparatus 2, etc.Fig. 7 shows a plot of the number A of occurrences of trouble versus an index a related to a potential result, for a total of 30 sets of data.FIG. 8 is an explanatory diagram showing an example of a method for calculating an influence degree using a defect index as an index (potential result).FIG. 9 is a graph schematically showing an influence degree on an event for each parameter.FIG. 10 is a schematic diagram showing progression of actual measured values (a line graph) of ORP (an example of a parameter) of a raw material system 1 for an event in a water system and an influence degree of the ORP of the raw material system 1 on the event.

[0023] Hereinafter, an embodiment of the present disclosure will be described. It should be noted that various features described in the embodiment below can be combined with each other.

[0024] In other words, the information processing system according to the present embodiment is as follows. An information processing system that executes information processing related to an event in a water system or derived from the water system, the information processing system comprising: a relationship model information acquisition unit configured to acquire relationship model information indicating a relationship between an index related to the event, which has been created in advance, and two or more parameters, each of the two or more parameters being a parameter selected from a group consisting of a water quality parameter, a control parameter, and a result parameter, the water quality parameter being a parameter related to water quality of the water system, the control parameter being a parameter related to the water system, a facility related to the water system, or a control condition of a raw material to be added to the water system, the result parameter being a parameter that does not correspond to the index and relates to a result having arisen in the water system, a facility related to the water system, or a raw material to be added to the water system, or a result having been derived from the water system, a facility related to the water system, or a raw material to be added to the water system; a data set specification unit configured to specify a data set in which the index related to the event is associated with the two or more parameters; and an influence degree specification unit configured to specify an influence degree on the event for each of the parameters associated in the data set by comparing the relationship model information with the data set.

[0025] A program for realizing a software described in an embodiment may be provided as a non-transitory computer-readable storage medium, may be provided to be downloaded from an external server, or may be provided so that the program is activated on an external computer to realize function thereof on a client terminal (so-called cloud computing).

[0026] In addition, in various information processing according to an embodiment, an input and an output in response to the input can be realized. Here, as long as an output is obtained as a result of an input, the aspect of information referenced in such information processing (hereinafter, referred to as reference information) is not limited. The reference information may be, for example, rule-based information such as a database, a lookup table, a predefined function (including a determination formula such as a regression formula constructed by statistical methods), may be a trained model in which the correlation between an input and an output has been learned in advance, or may be a large-scale language model that can output a desired result by inputting a prompt.

[0027] The term "unit" in an embodiment may include, for example, a combination of hardware resources implemented as circuits in a broad sense and information processing of software that can be concretely realized by these hardware resources. Further, various information is handled in an embodiment, and the information can be represented by, for instance, physical values of signal values representing voltage and current, high and low signal values as a set of binary bits consisting of 0 or 1, or quantum superposition (so-called qubits), and communication / calculation can be executed on a circuit in a broad sense.

[0028] Furthermore, the circuit in a broad sense is a circuit realized by combining at least an appropriate number of a circuit, a circuitry, a processor, a memory, or the like. The processor may be a general-purpose processor or a dedicated circuit. In other words, a circuit includes an application specific integrated circuit (ASIC), a programmable logic device (e.g., simple programmable logic device (SPLD), a complex programmable logic device (CPLD), field programmable gate array (FPGA)), and the like.

[0029] 1. Hardware configuration This section describes a hardware configuration of an information processing system 1 according to the present embodiment. FIG. 1 show an overall configuration of the information processing system 1.

[0030] The information processing system 1 according to the present embodiment is a system used to execute information processing related to an event in a water system or derived from the water system. In the following, "an event in a water system or derived from the water system" may be simply referred to as an "event". Here, the information processing system 1 according to the present embodiment includes an information processing apparatus 2 and a user terminal 3, which are connected via a communication line. The communication line here includes the Internet, wireless, etc., and serves to mediate exchange of data between apparatuses connected to own line. Furthermore, in the information processing system 1 according to the present embodiment, a measurement device 4 is configured to measure various parameters in a water system W. The parameters measured by the measurement device 4 are configured to be transmitted to the information processing apparatus 2.

[0031] A system exemplified by the information processing system 1 in the present specification comprises one or more devices or components. Thus, even the information processing apparatus 2 alone may be an example of a system, and an example including the user terminal 3, the water system W to be applied, and the measurement device 4 may be referred to as a system as well. The following continues the description of each device, etc. that may configure the information processing system 1.

[0032] <Information processing apparatus 2> FIG. 2 shows a hardware configuration of the information processing apparatus 2. The information processing apparatus 2 includes a communication unit 21, a storage unit 22, and a controller 23, and each of these components is configured to be electrically connected by a communication bus 20. Hereinafter, each unit of the information processing apparatus 2 will be described.

[0033] (Communication unit 21) The communication unit 21 is configured to transmit various electric signals from the information processing apparatus 2 to an external component. Furthermore, the communication unit 21 is configured to receive various electric signals from an external component to the information processing apparatus 2. The communication unit 21 includes a network communication function, and thus various information may be communicated between the information processing apparatus 2 and an external device via a communication line.

[0034] (Storage unit 22) The storage unit 22 stores various kinds of information defined by the description above. This may be implemented as, for example, a storage device such as a solid state drive (SSD) that stores various programs and the like related to the information processing apparatus 2 executed by the controller 23, or a memory such as a random access memory (RAM) that stores temporarily necessary information (argument, array, or the like) related to program operations. The storage unit 22 stores various programs, variables, etc. related to the information processing apparatus 2 which are executed by the controller 23.

[0035] (Controller 23) The controller 23 is, for example, an unshown central processing unit (CPU). The controller 23 is configured to realize various functions related to the information processing apparatus 2 by reading and executing a predetermined program stored in the storage unit 22. In other words, information processing by software stored in the storage unit 22 is specifically realized by the controller 23 that is an example of hardware, thereby may be executed as each functional unit included in the controller 23. Further details on these will be described in the next section. It should be noted that the controller 23 is not limited to being singular, and may be implemented with two or more controllers 23 for each function. Additionally, a combination thereof may be applied.

[0036] <User Terminal 3> FIG. 3 shows a 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 the present specification, a person who performs such operations may be simply referred to as a "user". The user terminal 3 includes a communication unit 31, a storage unit 32, a controller 33, a display unit 34, and an input unit 35, and these components are electrically connected inside the user terminal 3 via a communication bus 30. The descriptions of the communication unit 31, the storage unit 32, and the controller 33 are omitted since they are substantially the same as those of the communication unit 21, the storage unit 22, and the controller 23 in the information processing apparatus 2 described above.

[0037] (Display Unit 34) The display unit 34 may be, for example, included in a housing of the user terminal 3 or may be externally attached. The display unit 34 is configured to display a screen of graphical user interface (GUI) that is operable by a user. For instance, this is preferable to be implemented by using different 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.

[0038] (Input unit 35) The input unit 35 may be included in a 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. With the touch panel, a user may input through tapping, swiping, or other operations. Of course, a switch button, a mouse, a QWERTY keyboard, etc. may be employed instead of the touch panel. That is, the input unit 35 receives operation input performed by the user. This input, treated as a command signal, is transferred to the controller 33 via the communication bus 30, and the controller 33 may execute predetermined control or calculation as necessary.

[0039] <Measurement device 4> The measurement device 4 is configured to measure various parameters related to the water system W. In other words, this measurement device 4 is a device for measuring a predetermined parameter. In the present embodiment, a predetermined calculation processing is performed using two or more parameters selected from a group consisting of a water quality parameter, a control parameter, and a result parameter, and the measurement device 4 acquires a parameter that serves as the basis for this calculation. Noted that, although only a single measurement device 4 is shown in FIG. 1, a plurality (or multiple types) of measurement devices 4 may be applied in the information processing system 1.

[0040] Various sensors and the like can be selected as the measurement device 4, depending on the type of parameter to be measured. As the measurement device 4, for example, a pH meter, an electrical conductivity meter, an oxidation-reduction potentiometer, a turbidimeter, a thermometer, a level meter for measuring foam height, a COD meter, a UV meter, a particle size distribution analyzer, a flocculation 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 can be used. The five-sense sensor here may include an image sensor, a luminous 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 sensors, a humidity sensor, a displacement sensor, etc.

[0041] It should be noted that in some cases, the control parameters and the like that are directly input for controlling the device may be used as they are, or such data may be received from the device via communication, or an operator of the device or the like may record the control parameters other than the device for recording purposes.

[0042] <Water System W> The water system W according to the present embodiment may be any of various industrial processes involving water. That is, the type of water system W is not particularly limited, but as an example, it may be a water system in a process of manufacturing a paper product and / or a material related to the manufacture of a paper product. In the following, these may be collectively referred to as "paper products, etc.". In the present specification, "a material related to the manufacture of a paper product" does not refer to the paper product itself to be manufactured, but rather to various raw materials and intermediates that are used in the manufacture of the paper product. In other words, raw materials used in the manufacture of the paper product include white water, calcium oxide, etc. generated during the manufacturing process of the paper product. Intermediates used in the manufacture of the paper product include, for example, pulp and the like that is manufactured prior to the papermaking process. Specifically, examples of processes for manufacturing white liquor, calcium oxide, and pulp, which are materials related to the paper product, include a cooking process, a washing process, a black liquor concentration process, and a causticizing process. In addition, as a water system other than a water system of the process of manufacturing paper products, etc., examples of the water system to be a target may include various piping, a heat exchanger, a storage tank, a kiln, and cleaning equipment. An example of a process that is a target of the information processing system 1 according to the present embodiment is shown in FIG. 4. FIG. 4 is an example of a papermaking process to which the information processing system 1 can be applied. The papermaking process shown in FIG. 4 shows various systems such as a raw material system, a preparation / papermaking system, a recovery system, and a drainage system. The measurement device 4 described above can acquire parameters from various elements present in such a papermaking process. The application of the information processing system 1 is not limited to the processes shown in the figure, and predetermined elements may be added or deleted as appropriate.

[0043] 2. Functional configuration This section describes a functional configuration of the present embodiment. FIG. 5 is a functional block diagram showing functions of the information processing apparatus 2. As mentioned above, information processing by software (stored in the storage unit 22) is specifically realized by hardware (the controller 23), and can be executed as each functional unit included in the controller 23.

[0044] Specifically, the information processing apparatus 2 (controller 23) may include, as each functional unit, a parameter information acquisition unit 231, a relationship model information acquisition unit 232, an estimation unit 233, a data set specification unit 234, an influence degree specification unit 235, a countermeasure presentation unit 236, a relationship model information creation unit 237, and a storage management unit 238. It should be noted that each functional unit may be increased, omitted, or integrated as appropriate depending on the application to which the information processing apparatus 2 is applied.

[0045] (Parameter information acquisition unit 231) The parameter information acquisition unit 231 is configured 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 a group consisting of a water quality parameter, a control parameter, and a result parameter. Here, the water quality parameter is a parameter related to water quality of the water system. The control parameter is a parameter related to the water system, a facility related to the water system, or a control condition of a raw material to be added to the water system. The result parameter is a parameter having a different meaning from the potential result and is a parameter that does not correspond to an index and relates to a result having arisen in the water system, a facility related to the water system, or a raw material to be added to the water system, or a result having been derived from the water system, a facility related to the water system, or a raw material to be added to the water system. When acquiring these parameters, the parameter information acquisition unit 231 is configured, for instance, to acquire various information via the communication unit 21 from the measurement device 4 that is capable of measuring at least a part of the parameters.

[0046] (Relationship model information acquisition unit 232) The relationship model information acquisition unit 232 is configured 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 indicating a relationship between the index related to the event, which has been created in advance, and the two or more parameters. Each of the two or more parameters in the relationship model information is a parameter selected from a group consisting of the water quality parameter, the control parameter, and the result parameter. The water quality parameter, the control parameter, and the result parameter are as described above in the parameter information acquisition unit 231, so the description will be omitted. The details of this model will be explained later.

[0047] (Estimation unit 233) The estimation unit 233 is configured to execute an estimation step. In the estimation step, the estimation unit 233 estimates an index (the potential result or the related index) based on the acquired parameter information and relationship model information. Although not described in the embodiment, this estimated index can be presented to the user via the user terminal 3, for example. The estimated index is configured to be recognizable by the user or the like. In other words, the estimation unit 233 creates display information and controls the display information so that it can be visually recognized by the user or the like. Note that, the display information may be visual information itself such as a screen, an image, an icon, a text, etc. generated in a form that is visible to the user, or the display information may be rendering information for displaying visual information such as a screen, an image, an icon, a text, etc. on various devices or terminals.

[0048] (Data set specification unit 234) The data set specification unit 234 is configured to execute a data set specification step. In the data set specification step, the data set specification unit 234 specifies a data set in which an index related to an event is associated with two or more parameters. This data set may be any of various data sets in which the above-mentioned index is associated with the above-mentioned two or more parameters, but typically, the data set specified by the data set specification unit 234 is one in which the index (the potential result or the related index) estimated by the estimation unit 233 is associated with the two or more parameters acquired by the parameter information acquisition unit 231. In other words, the data set specification unit 234 specifies the index (the potential result or the related index) estimated by the estimation unit 233 and the two or more parameters associated with the index and acquired by the parameter information acquisition unit 231. The specified data set may be used in the influence degree specification unit 235.

[0049] (Influence degree specification unit 235) The influence degree specification unit 235 is configured to execute an influence degree specification step. In the influence degree specification step, the influence degree specification unit 235 specifies the influence degree on the event for each parameter associated in the data set by comparing the relationship model information with the data set. Note that the influence degree refers to the influence degree on an event for each parameter included in the data set; for example, when an event (e.g. some kind of trouble in a manufacturing process) occurs, if the influence degree of a certain parameter in the data set is relatively high, it can be determined that the parameter is the main factor of the event (the aforementioned trouble). The influence degree specification unit 235 calculates (estimates / infers) the influence degree of each parameter associated in the data set by a predetermined calculation processing. From this perspective, the influence degree specification unit 235 may be referred to as an "influence degree estimation unit" or "influence degree inference unit.

[0050] (Countermeasure presentation unit 236) The countermeasure presentation unit 236 is configured to execute a countermeasure presentation step. In the countermeasure presentation step, the countermeasure presentation unit 236 presents countermeasure information related to the corresponding parameter in accordance with the influence degree specified by the influence degree specification unit 235. The above-mentioned influence degree specification unit 235 specifies the influence degree, allowing the user to understand the main factors or the like that cause the event. The countermeasure presentation unit 236 presents the countermeasure to the user, thereby further improving the ease of use of the system. The countermeasure information is presented, for example, by being output to the user terminal 3. The countermeasure information may be visual information itself generated in a manner that is visible to the user, such as characters, numeral values, diagrams, photographs, videos, screens, images, icons, text, etc., or may be rendering information for displaying the visual information on various devices or terminals, for example.

[0051] (Relationship model information creation unit 237) The relationship model information creation unit 237 is configured to execute a relationship model information creation step. In the relationship model information creation step, the relationship model information creation unit 237 creates or updates relationship model information to be used in the above-mentioned estimation step and the like.

[0052] (Storage management unit 238) The storage management unit 238 is configured to execute a storage management step. In the storage management step, the storage management unit 238 is configured to manage various information to be stored that is related to the information processing system 1 according to the present embodiment. Typically, the storage management unit 238 is configured to allow the information, etc. handled by the information processing apparatus 2 to be stored in a storage area. Examples of the storage area include the storage unit 22 of the information processing apparatus 2 or storage units of various devices or terminals, but the storage area does not necessarily have to be within the system of the information processing system 1, and the storage management unit 238 may also manage various kinds of information so as to be stored in an external storage device or the like.

[0053] 3. Details of information processing In Section 3, an information processing method executed by the information processing apparatus 2, etc. will be described with reference to an activity diagram, etc. FIG. 6 is an activity diagram showing a flow of information processing using the information processing apparatus 2, etc.

[0054] <Activity A101: Acquire parameter information> As shown in FIG. 6, in the present embodiment, the parameter information acquisition unit 231 of the information processing apparatus 2 first acquires parameter information (Activity A101). As mentioned above, the parameter information includes two or more parameters selected from the group consisting of the water quality parameter, the control parameter and the result parameter.

[0055] The parameter information is related to the water system W regarding an estimation target and includes two or more parameters selected from the group consisting of the water quality parameter, the control parameter, and the result parameter. Note that the water system W here is not limited to one present in a single tank or a single flow path or one in which a continuous flow is present. One with a plurality of tanks or flow paths, specifically, a water system in which a branch is present in a flow path, a water system with a confluence of a plurality of flow paths, a water system that is transferred in batch units from one tank to another, and a water system in which some kind of processing is performed in a mid-flow are also considered one water system. In addition, when a water system related to the water quality parameter, the control parameter, or the result parameter is divided according to tanks or the like, the water quality parameter, the control parameter, or the result parameter with respect to a part of the water system may be used, or the water quality parameter, the control parameter, or the result parameter with respect to the entire water system may also be used.

[0056] The water quality parameter is not particularly limited as long as the parameter is related to a water quality of the water system W. In addition, the control parameter is not particularly limited as long as the parameter is related to the water system W, a facility related to the water system W, or a control condition of a raw material to be added to the water system W. Furthermore, the result parameter is not particularly limited as long as the parameter has a different meaning from the potential result and is related to a result having arisen in the water system W, a facility related to the water system W, or a raw material to be added to the water system W, or a result having been derived from the water system W, a facility related to the water system W, or a raw material to be added to the water system W. Note that while "a parameter that does not correspond to an index" in the result parameter includes a case where assessment indices (for example, physical amount) differs (for example, when one of the assessment indices is a length and the other assessment index is a mass), a case where the assessment index is identical but objects of assessment differ from each other (for example, a mass of paper and a mass of an additive) and a case where locations of measurement differ from each other (for example, an oxidation-reduction potential of a papermaking raw material system and an oxidation-reduction potential of a papermaking system), whereas, a case where the results have different meanings (for example, in a case where an oxidation-reduction potential is positive, and in a case where an oxidation-reduction potential is negative) is not included.

[0057] Hereinafter, specific examples of the water quality parameter, the control parameter, and the result parameter in a case where the water system W is a water system in a process of manufacturing paper products, etc. will be described. These parameters may be related to, for example, the papermaking process as shown in FIG. 4, or may relate to, for example, a cooking system, a black liquor treatment system, a green liquor treatment system, a slaking / causticizing system, and a calcination system (lime calcination process) in a pulp manufacturing system, as described in JP 2022-12850A.

[0058] That is, the parameter information may include, as the water quality parameter, one or more selected from the group consisting of pH of the water system, electrical conductivity, an oxidation-reduction potential, a zeta potential, turbidity, temperature, a foam height, a biochemical oxygen demand (BOD), a chemical oxygen demand (COD), total organic carbon (TOC), inorganic carbon, absorbance, color, appearance, whiteness, transparency, a particle size distribution, a degree of flocculation, an amount of foreign matter, suspended solids (SS), a foaming area on a water surface, an area of underwater contamination, an amount of bubbles, an amount of glucose, an amount of organic acids, an amount of active alkali, total titrated alkali, metal or a metal ion content, a non-metal ion content, an amount of starch, an amount of calcium, an amount of total chlorine, an amount of free chlorine, an amount of dissolved oxygen (DO), a cationic demand, an amount of hydrogen sulfide, a degree of sulfidation, an amount of hydrogen peroxide, ash concentration, a respiration rate of microorganisms in the system, a viable bacterial count, a spore-forming bacterial count, and ATP. These water quality parameters are acquired from any point in the water system W.

[0059] It should be noted that, among the above-mentioned water quality parameters, "appearance" may be acquired from an RGB color sensor or a camera image. The "amount of active alkali" or "total titrated alkali" may refer to the amount of alkali evaluated as sodium, the amount of alkali evaluated as calcium, or the total amount of alkali. Furthermore, the metal in the "metal or metal ion content" may 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). Note that the "metal or metal ion content" may be the content of a specific (one) metal or metal ion, or the content of multiple types of metals. In addition, the "non-metal ion" may include ions containing hetero atoms 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).

[0060] In addition, the parameter information may include, as the control parameter, one or more selected from the group consisting of an operating speed (a papermaking speed) of a paper machine, a rotational speed of a filter cloth of a raw material dehydrator, a rotational speed of a filter cloth of a cleaning machine, an additive amount of chemicals or intensity to the water system, an additive amount of chemicals or intensity relative to a raw material to be added to the water system, an additive amount of chemicals or intensity relative to a facility related to the water system, an amount of vapor or intensity for heating, temperature of vapor for heating, pressure of vapor for heating, a flow rate from a seed box, nip pressure of a press part, felt vacuum pressure of a press part, a blending ratio of a papermaking raw material, a blended amount or intensity of waste sheets of papermaking raw material, a mesh size of a screen for a papermaking raw material, a gap distance between a rotor and a stator of a beater, freeness, a degree of beating, draw, linear pressure of a press part, a gravity dewatering amount of a wire part or equivalent parameters, a forced dewatering amount of a wire part or equivalent parameters, a gravity dewatering amount of a press part, parameters equivalent to a gravity dewatering amount of a press part, parameters for controlling the gravity dewatering amount of a press part, a forced dewatering amount of a press part, parameters equivalent to the forced dewatering amount of a press part, parameters for controlling the forced dewatering amount of a press part, concentration of interlayer adhesive, an amount or intensity of interlayer adhesive, shower water amount or intensity, fresh water intensity, water usage intensity, timing or frequency of cleaning a wire part, timing or frequency of cleaning a press part, timing or frequency of cleaning a dryer part, timing or frequency of changing tools of a paper machine, an fresh water replenishment amount, an amount of rejects from the screen or parameters for controlling them, differential pressure across the screen or a parameter equivalent thereto, an amount of rejects from the cleaner or a parameter for controlling them, a fuel amount or intensity for heating, concentration in a floatater, an amount of air in the floatater or a parameter equivalent thereto, an amount of froth in a floatater or parameters equivalent thereto, the load of a disperser or a parameter equivalent thereto, the number of felt singeing, a flow rate of fluid flowing through the water system, air temperature, an amount of vapor or intensity for heating, an amount or intensity of calcium carbonate to be added, an amount or intensity of calcium bicarbonate to be added, a flow rate or concentration of clarifying green liquor, a flow rate or concentration of raw green liquor, a flow rate or concentration of dilute black liquor, a flow rate or concentration of concentrated black liquor, a flow rate or concentration of white liquid, black liquor injection rate in a black liquor treatment system, black liquor injection concentration in a black liquor treatment system, an amount of pulp production, a moisture content of a mud filter, lime input or intensity in a slaking / causticizing system, an amount of lime production in a calcination system, fuel usage, a fuel usage intensity, causticizing efficiency in a slaking / causticizing system, caustic input or intensity in a cooking system, an amount of black liquor generated in a black liquor treatment system, an amount of solids in evaporating black liquor in a black liquor treatment system, a evaporation factor in evaporating black liquor in a black liquor treatment system, temperature of an economizer in a boiler in a black liquor treatment system, water 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 the calcium carbonate before calcination, and specific gravity of the calcium oxide after calcination. These control parameters are acquired from various facilities and the like related to the water system W. In the case where the water system W is the water system in the process of manufacturing paper products, the facility here includes, for example, a wire and felt or the like of the paper machine to which chemicals are directly added, and a dry part (dryer) or the like of the papermaking system. With respect to the above control parameter, the type of "fuel" can be set appropriately, and may be, for example, heavy oil or petroleum coke. Furthermore, the point where the "temperature" in the "temperature of an economizer in a boiler in a black liquor treatment system" is acquired may be any point within the device. For example, it may be an inlet point of the device, it may be inside the device, or it may be an outlet point of the device.

[0061] In addition, the parameter information includes, as the result parameter, one or more selected from the group consisting of a unit weight (grammage) of a paper product, a yield, white water concentration, moisture content of a paper product, an amount of vapor or intensity in a facility manufacturing the paper product, temperature of vapor in a facility manufacturing the paper product, pressure of vapor in a facility manufacturing the paper product, thickness of the paper product, concentration of ash in the paper product, a type of defect of the paper product, the number of defects in the paper product, a timing of paper breakage in a process, freeness, a degree of beating, an amount of aeration, air temperature in a dryer, a joint rate, the number of joints, an amount of paper loss, the number of flaws, a flaw index, outdoor temperature including indoor temperature, outdoor humidity including indoor humidity, wet paper moisture content, product moisture content, temperature of rolls and cylinders in the dryer, a five-sense sensor measurement, a fuel amount or a fuel usage intensity, a calcination rate in a calcination system, a causticizing efficiency in a slaking / causticizing system, caustic input in a cooking system, and lime input in a slaking / causticizing system. Among these result parameters, as the amount of vapor in a facility manufacturing the paper product, for example, an amount of vapor in a dryer of the paper machine, an amount of vapor in a kraft pulp black liquor evaporator, an amount of vapor in a black liquor heater of a kraft pulp digester, and an amount of vapor which is injected in order to heat pulp raw material or white water can be used. These result parameters are acquired from various facilities and the like related to the water system W.

[0062] Note that there are parameters which originally represent a same matter but are classified according to their purpose into two or more of the water quality parameter, the control parameter, and the result parameter. For example, in a black liquor evaporator, black liquor is heated by indirect heat exchange with vapor generated by a boiler and, as a result of the heating, process vapor is generated from the black liquor. The process vapor is used to heat (thicken) thickened black liquor in a next step. The amount of generated process vapor is a result parameter from the perspective of being generated from the black liquor and is also used as a control parameter from the perspective of being used in heating (thickening) thickened black liquor in a next step. In addition, the vapor generated by the boiler in order to heat the black liquor is used as a control parameter. It should be noted that the two or more parameters acquired by the parameter information acquisition unit are prevented from all being substantially identical. For example, in a case where all of the process vapor generated from the black liquor described above is used to heat (thicken) the thickened black liquor in the next step, the process vapor generated from the black liquor as a result parameter and the amount of vapor to be used to heat (thicken) the thickened black liquor as a control parameter are excluded as the two parameters (parameters other than these two are not used). This is because, in such a case, the process vapor generated from the black liquor as a result parameter and the amount of vapor to be used to heat (thicken) the thickened black liquor as a control parameter are substantially identical. However, in a case where a part of the process vapor generated from the black liquor is used to heat (thicken) the thickened black liquor in a next step, the process vapor generated from the black liquor as a result parameter and the amount of vapor to be used to heat (thicken) the thickened black liquor as a control parameter can be used as the two parameters. This is because, in such a case, the process vapor generated from the black liquor as a result parameter and the amount of vapor to be used to heat (thicken) the thickened black liquor as a control parameter are not substantially identical. Furthermore, in a case where all of the process vapor generated from the black liquor described above is used to heat (thicken) the thickened black liquor in a next step, in addition to using the process vapor generated from the black liquor as a result parameter and the amount of vapor to be used to heat (thicken) the thickened black liquor as a control parameter as the two parameters, a plurality of parameters may be substantially identical if further combining other parameter such as the pH of the water system as a water quality parameter. In addition, for example, while freeness and degree of beating are the identical parameters, freeness and degree of beating can be included in both the control parameter and the result parameter.

[0063] These parameters may be quantitative or qualitative. When using a qualitative parameter, a numerical value may be assigned to the parameter and the parameter may be handled as quantitative data.

[0064] Note that the water quality parameter, the control parameter, and the result parameter are respectively concepts encompassing a plurality of parameters. With respect to "acquiring, as parameter information, two or more parameters selected from a group consisting of a water quality parameter, a control parameter, and a result parameter", the two or more parameters included in the parameter information are respectively independent and can be selected from the respective parameters of the water quality parameter, the control parameter, and the result parameter, and two or more parameters (for example, pH and temperature of water) may be selected from only one of the water quality parameter, the control parameter, and the result parameter or two or more parameters may be selected from a combination of two or three (for example, pH, press pressure of a press part, and thickness of the paper product) of the water quality parameter, the control parameter, and the result parameter. However, exact identical parameters (for example, pH of water at point A and pH of water at point A) are not to be selected (however, for example, pH of water at point A and pH of water at point B, which are different measurement points, may be selected).

[0065] The order of performing Activity A101 here and the Activity A102 that will be described later is arbitrary. Activity A101 can be performed prior to Activity A102, Activity A102 can be performed prior to Activity A101, or both activities can be performed in parallel (simultaneously).

[0066] <Activity A102: Acquisition of relationship model information> In Activity A102, the relationship model information acquisition unit 232 acquires relationship model information. The relationship model information indicates a relationship between an index, which has been created in advance, and the two or more parameters. In a typical example, the relationship model information here is created by the relationship model information creation unit 237.

[0067] Note that "in advance" means prior to estimating an index and may be either during actual operation of the water system W or prior to actual operation, as long as it is prior to estimating the index. The "index" is quantitative in one example of the embodiment and can be understood, for example, as something for evaluating the status of an event. If the index indicates an unfavorable condition (if it has an unfavorable value), it is evaluated accordingly that the event is not in a favorable state and, for example, that troubles are likely to occur in the manufacturing process or that the quality of the products to be manufactured may deteriorate. In the embodiment, the index can be expressed as a potential result or an index related to the potential result. In the following, "an index related to the potential result" may be referred as "a related index" to distinguish it from the generic "index". In addition, an "event" is an event in a water system W or derived from the water system W, and represents, for example, a concept such as a situation in the manufacturing process of a given product or the quality of the product to be manufactured. In the embodiment, as an example, the event is described as referring to a case in which there is concern about trouble in the manufacturing process or a deterioration in produce quality, but the event is not limited to this and may also be applied to cases in which the event refers to a favorable situation in the manufacturing process or a situation in which product quality can be ensured.

[0068] The "potential result" related to the relationship model refer to various events related to the water system W. The estimation unit 233 of the information processing apparatus 2 in the present embodiment estimates the potential result (index), and the potential result related to the relationship model information also corresponds to the result estimated by the estimation unit 233. The potential result can be set appropriately depending on the estimation target, but in the case where the water system W is a water system in a process of manufacturing a paper product, the potential result may be as shown below. That is, in the present embodiment, the potential result (index) may be related to the quality or the amount of energy of the paper product and / or a material associated with the manufacture of the paper product. The quality may relate to one or more selected from the group consisting of the number of defects, paper strength, a joint rate, a sizing degree, air permeability, smoothness, an ash content, a tone, whiteness, formation, odor, causticizing efficiency, calcination rate, a kappa value, freeness, and a moisture percentage of a paper product and / or a material related to the manufacture of the paper product. The amount of energy may relate to one or more selected from the group consisting of an amount of vapor, a vapor intensity, a fuel amount, and a fuel usage intensity, in manufacturing a paper product and / or a material related to the manufacture of the paper product. As mentioned above, "a material related to the manufacture of the paper product" does not refer to the paper product itself to be manufactured, but to various raw materials and intermediates used in the manufacture of the paper product. That is, a raw material used in the manufacture of the paper product includes white water, calcium oxide, and the like that are generated during the manufacturing process of the paper product. In other words, the causticizing efficiency and the calcination rate described above may be related to these raw materials. Intermediates used in the manufacture of the paper product include, for example, pulp manufactured prior to the papermaking process. The above-mentioned potential result may be an evaluation item related to such pulp. The potential result may be the items listed above themselves, or may be a combination of the items listed above, or related items. For example, examples of the related items related to the calcination rate described above include the amount of fuel used to satisfy the predetermined calcination rate (typically, the amount of fuel used in the kiln when obtaining calcium oxide which is used in the causticizing and slaking processes, from calcium carbonate). The type of fuel for the "fuel amount" in the potential result (index) is not particularly limited, and may be heavy oil, petroleum coke, or the like.

[0069] In addition, the potential result in the present embodiment may correspond to the result parameter described above. In this case, the result parameter is usually not included in the parameter information (two or more parameters).

[0070] Examples of the relationship model information include, but are not particularly limited to, a function or a look-up table indicating a relationship between the potential result or a related index and two or more parameters, or a trained model of a relationship between the potential result or a related index and two or more parameters.

[0071] It is assumed that the two or more parameters included in the parameter information acquired by the parameter information acquisition unit 231 and the parameters included in the parameter information used in the relationship model information have two or more parameters that are common to each other. As described above, the water quality parameter, the control parameter, and the result parameter are respectively concepts encompassing a plurality of parameters. "Have two or more parameters that are common to each other" may mean two or more of any parameters (for example, two or more of only water quality parameters; pH and temperature of water) among the water quality parameter, the control parameter, and the result parameter being common, a combination of the water quality parameter, the control parameter, and the result parameter (for example, one water quality parameter and one control parameter; for example, pH of water and press pressure of the press part) being common, or all of the water quality parameter, the control parameter, and the result parameter (for example, one water quality parameter, one control parameter, and one result parameter) being common.

[0072] In addition, the "related index" in the index may be an index that has a certain correlation with the potential result (e.g., a function of two or more parameters), and may be independently created by the user (operator, etc.) of such information processing system, rather than a generally known index.

[0073] This relationship model information is created, for example, as follows. That is, prior to estimating the potential result or the related index, a prior measurement result corresponding to the potential result or a prior measurement index related to the prior result is measured. In addition, in the same water system, two or more parameters, each of which is one of the water quality parameter, the control parameter, or the result parameter, are measured. A plurality of sets of data of these prior measurement result or prior measurement index and the parameter are prepared, for example, by changing the day and time on which measurements are taken, so that variations occur in the prior measurement result or prior measurement index and the parameter. Next, the prior measurement result or prior measurement index is assumed to be a function of two or more parameters, and the form and coefficients of the function are determined by comparing with the prior measurement result or the prior measurement index, thereby constructing relationship model information. Here, the relationship model information may be a model obtained by performing a predetermined processing on a prior measurement result corresponding to the index or an index related to a prior measurement result, and two or more parameters. As the processing here, the following can be used: regression analysis (a linear model, a generalized linear model, a generalized linear mixed model, ridge regression, lasso regression, an elastic net, support vector regression, projection pursuit regression, principal component regression, etc.), a time series analysis (a VAR model, a SVAR model, an ARIMAX model, a SARIMAX model, a state space model, a HMM model, etc.), a decision tree (a decision tree, a regression tree, a random forest, XGBoost, Light GBM, etc.), a neural network (a simple perceptron, a multilayer perceptron, a DNN, a CNN, a RNN, a LSTM, a GAN, a VAE, etc.), Bayes (naive Bayes, Bayesian optimization, a Bayesian network, etc.), clustering (k-means, k-means++, etc.), classification (a k-nearest neighbor algorithm, a support vector machine, etc.), ensemble learning (Boosting, Adaboost, etc.) or the like.

[0074] In an embodiment, the relationship model information is preferably a model obtained from a regression analysis between a prior measurement result corresponding to the potential result or an index related to the prior measurement result and the two or more parameters. Note that the number of sample sets when performing a regression analysis is not particularly limited.

[0075] The creation of the relationship model information is preferably performed in a same water system as the water system of which the potential result is to be estimated. In addition, for example, in a case where the water quality of a water system changes significantly even in a same apparatus (for example, a case where pulp that is a papermaking raw material is changed or the like in a papermaking system at a paper mill), a relationship model information with respect to the water system after the water quality changes is preferably created and used.

[0076] From such a viewpoint, the potential result or the related index and the two or more parameters may be measured on a regular or irregular basis during operation of the water system W, the relationship model information may be created every time, or the relationship model information may be updated by adding data.

[0077] Such relationship model information may be created and updated by, for example, the function of the relationship model information creation unit 237 included in the information processing apparatus 2. That is, the relationship model information creation unit 237 can create relationship model information suitable for the information processing of the present embodiment by performing predetermined calculation processing on the acquired potential result or related index and two or more parameters. Noted that, the creation of relationship model information may be manually performed by, for example, an operator or the like.

[0078] An example of creating the relationship model information will be described below. Specifically, a case will be described in which an index a related to a potential result is calculated using a water quality parameter x, a water quality parameter y, and a control parameter z, and the index a related to the potential result and the number A of occurrences of trouble (times / day) as the potential result are calculated.

[0079] When creating the relationship model information, the water quality parameter x, the water quality parameter y, and the control parameter z shown in Table 1 below are measured and, at the same time, the number A of occurrences of trouble is also measured to acquire a total of 30 sets of data. The acquired data is shown in Table 1 below. Of course, the number of sets of data is not limited thereto, and may be set appropriately.

[0080] The index a related to the potential result is represented by the following Equation (1), where "parameters" stand for x, y, and z, bnis a coefficient of x, y, and z, and a0and b0are constants. <Equation 1>

[0081] A negative binominal regression analysis is performed based on actual measured values of the number A of occurrences of trouble and the index a related to the potential result of Equation (1). The index a related to the potential result calculated by the negative binominal regression analysis is also shown in Table 1. In addition, Fig. 7 shows a plot of the number A of occurrences of trouble versus the index a related to the potential result, for a total of 30 data sets. A correlation coefficient between the number A of occurrences of trouble and the index a related to the potential result is r = 0.78 (p < 0.05), and a strong correlation is observed.

[0082]

[0083] Note that a function of the index a related to the potential result used in the regression analysis and the water quality parameter x, the water quality parameter y, and the control parameter z is not limited to Equation (1) described above, and a general Equation (2) can be used. <Equation 2>

[0084] <Activity A103: Estimation of index> After acquiring the parameter information in Activity A101 and acquiring the relationship model information in Activity A102, the estimation unit 233 estimates an index (potential result or related index). Here, it is preferable that the estimated result is presented to the user in association with each parameter included in the parameter information. Such an estimated result can typically be output by inputting the acquired parameter information into the relationship model information.

[0085] <Activity A104: Specify data set> The data set specification unit 234 specifies a data set in which an index related to an event (potential result or related index) is associated with two or more parameters.

[0086] Here, the parameter is a parameter associated with the index, in other words, may have a relevance to the quality of the value of the index. The selection of parameters is made, for example, based on the knowledge of the user, etc. or through analysis by an arbitrary information processing apparatus. If this selection is appropriate, the correlation with the index will be appropriate, and thus it is expected that the accuracy of the index and the accuracy of an influence degree described below will also be improved.

[0087] As described above, the data set is data in which an index related to an event and two or more parameters are associated as a set. In an exemplary embodiment, it is preferable that the index and the two or more parameters associated in the data set are more recently obtained than the index and the two or more parameters associated in the relationship model information. In other words, the data (index and two or more parameters) that constitute the data set may be new data that has not necessarily been used to create the relationship model information. That is, the data set in the present embodiment may be a data set based on the most recently obtained parameter information and the corresponding index. The corresponding index here may typically be a value output by inputting the most recently obtained parameter information into the relationship model information. This ensures real-time property of the calculation of the influence degree of Activity A105 described later, and also improves calculation accuracy of the influence degree.

[0088] However, the data set specified by the data set specification unit 234 is not limited thereto. For example, the data set specified in the data set specification unit 234 may be used to create the relationship model information, and then this data set may further be used to calculate the influence degree. In other words, in the embodiment, the data set specified by the data set specification unit 234 may be part of the data set that constitutes the relationship model information, and does not necessarily need to be independent of the data in the relationship model information. Note that there is also an aspect that when the two are independent, the calculation accuracy of the influence degree can be expected to be improved.

[0089] In addition, in the present embodiment, the data set is described as using an index related to the event which is estimated by the estimation unit 233, but this is not limited thereto. By utilizing a statistical or machine learning method, an index related to the event may be calculated in a simulated manner, and the simulatively calculated index may be specified as a data set. For example, a method called Permutation Importance can be used to generate a simulated index by applying two or more new parameters to an arbitrary range of data sets used to create the relationship model information and pseudo-evaluating them, and this simulated index may be used as the index to be specified by the data set specification unit 234.

[0090] <Activity A105: Specify influence degree> The influence degree specification unit 235 specifies the influence degree on the event for each parameter included in the data set by comparing the relationship model information acquired in Activity A102 with the data set specified in Activity A104. Note that the influence degree refers to the influence degree on an event for each parameter included in the data set; for example, when an event (e.g. some kind of trouble in a manufacturing process) occurs, if the influence degree of a certain parameter in the data set is relatively high, it can be determined that the parameter is the main factor that causes the event (the aforementioned trouble).

[0091] There may be various methods for specifying the influence degree, but in a typical aspect, the influence degree specification unit 235 specifies the influence degree by performing a predetermined calculation processing to each parameter. This predetermined calculation processing includes at least one of the differential processing, statistical processing, and processing using a trained model.

[0092] Here, a case where differential processing is used as the calculation processing for calculating the influence degree will be described as an example. FIG. 8 is an explanatory diagram showing an example of a method for calculating the influence degree using a defect index as an index (potential result). FIG. 9 is a graph schematically showing the influence degree on the event for each parameter. In the following description, an example is given in which the parameters of the data set include six parameters (parameter P1 to parameter P6) as shown in FIG. 9.

[0093] In the defect index ID as an index (potential result) shown in FIG. 8, t0, t1, t2, t3, t4, t5, and t6 are determined in advance. The equation for the defect index ID shown in FIG. 8 and the values of t0 and the like correspond to the relationship model information. Furthermore, a and b are constants and can be set appropriately. The value of the defect index ID itself corresponds to an index (potential result).

[0094] Each influence degree V (n) of a parameter Pn (n = 1 to 6) can be expressed as ID (p) - ID (n). In this way, the influence degree is calculated (specified) by the differential processing related to the defect index ID. Note that FIG. 8 shows an example of the calculation of the influence degree V (1), which is the value of the influence degree of the parameter P1.

[0095] ID (p) is calculated using a parameter obtained more recently than the parameter included in the relationship model information. The parameter obtained more recently is, for example, not a parameter used to create the relationship model information in the relationship model information creation unit described above, but a new parameter obtained separately from this.

[0096] Then, the ID (p) is obtained by substituting the value of the parameter of the data set into the equation for the defect index ID. Specifically, A (p1) in the equation for ID (p) is the more recently obtained value for the parameter P1, B (p2) is the more recently obtained value for the parameter P2, C (p3) is the more recently obtained value for the parameter P3, D (p4) is the more recently obtained value for the parameter P4, E (p5) is the more recently obtained value for the parameter P5, and F (p6) is the more recently obtained value for the parameter P6. These values A (p1) to F (p6) are typically values obtained at the same timing, but in an exemplary aspect, they may be values obtained at different timings. For example, an aspect is exemplified in which the value A (p1) is a value acquired X hours ago, while the value B (p2) is acquired as an average value in a time period from Y days ago to Z hours ago.

[0097] As shown in FIG. 8, ID (1) can be calculated based on the reference value A (s1) for the parameter P1 and the parameters (values B (p2) to F (p6)) obtained more recently than the parameters included in the relationship model information. The reference value A (s1) can be set based on various perspectives, such as a past average value during the relationship model creation period, etc., a target reference value, a design value, and a numerical value for the period in which operation was good. The same applies to the reference value B (s2) to the reference value F (s6) of the other parameters P2 to P6. In other words, the reference values described here are based on the relationship model information. Note that the ID (1) term is the same as ID (p) except for the reference value A (s1) part, as shown by the arrow Ar in FIG. 8.

[0098] Although not shown in FIG. 8, the influence degree V (2), which is a value of the influence degree of the parameter P2, can be calculated by calculating ID (p) - ID (2). The value of ID (p) has been explained above so will be omitted here, but for ID (2), the value of B (p2) becomes the reference value B (s2). All other aspects are the same as ID (p). Note that the influence degree V (3), which is the value of the influence degree of the parameter P3, and subsequent influence degrees can be calculated in the same manner.

[0099] In this way, the influence degree is specified by comparing the relationship model information acquired in Activity A102 with the data set specified in Activity A104. ID (p) is a value considering the data set, but ID (n) considers not only the above-mentioned data set but also a reference value, and thus considers the relationship model information. In one example of an embodiment (differential processing), the influence degree V (n) of each parameter is given by ID (p) - ID (n), and thus the influence degree can be said to be a value obtained by comparing the data set with the relationship model.

[0100] In the above-mentioned description, a case is described as an example in which, when calculating ID (p) and ID (n), parameter values (values A (p1) to F (p6)) obtained more recently than the parameters included in the relationship model information were used. In other words, the more recently obtained parameters (values A (p1) to F (p6)) used in calculating ID (p) and ID (n) are the values of parameter P1 to parameter P6 themselves, but are not limited thereto. For example, a moving average may be used instead of the value itself. In addition, composite values of two or more parameters (composite parameter values) may also be used as the parameters (values A (p1) to F (p6)) used in calculating the defect index described here. In this case, the influence degree may be specified using the composite parameter corresponding to the composite value when calculating the influence degree.

[0101] Furthermore, in the above description, an example has been described in which differential processing is used as calculation processing, but processing such as statistical processing and a trained model can also be used. For this processing, for example, Shap, Permutation Importance, Feature Importance, impulse response functions, etc. can be used. These processing may be combined with the above-mentioned differential processing to calculate the influence degree with higher reliability and robustness.

[0102] Furthermore, although it has been described above that only one reference value is set for the value of each ID (n), this is not limited thereto. For example, when calculating the value of one ID (n), multiple reference values such as the reference value A (s1) and the reference value B (s2) may be used. In this case, the influence degree due to the combination of two or more parameters is calculated. In other words, it becomes possible to specify the influence degree related to the interaction of parameters.

[0103] When the influence degree specification unit 235 specifies the influence degree of each parameter as described above, the influence degree is shown to the user via the display unit 34 of the user terminal 3, for example in a form such as that shown in FIG. 9. In other words, the influence degree specification unit 235 can output the calculated influence degree to the user terminal 3 as information that allows the user to understand it (influence degree information) and is able to specify the influence degree. The form of the influence degree information is not particularly limited, and may be, for example, visual information itself generated in a visible manner such as numeral values or graphs as shown in FIG. 9, or rendering information for displaying visual information. Furthermore, the information may be sound information rather than visual information, or both. In the example of FIG. 9, the user can visually recognize that the parameter having a greatest influence degree as a factor causing the event is the parameter P2, "ORP (oxidation-reduction potential) of the raw material system 1".

[0104] If users can visually recognize the factor that causes the event as a manufacturing flow diagram in addition to the graph such as the one shown in FIG. 9, the system will be easier for users to use. For example, as shown in FIG. 4, the influence degree specification unit 235 can specify a parameter having a large influence degree (in the example of FIG. 4, the parameter P2 "ORP of the raw material system 1") by highlighting it on the manufacturing flow diagram.

[0105] Note that "specification" does not necessarily include content such as output of numerical values or graphs (output of visual information) or output of audio (output of audio information). In other words, "specification" may include, for example, simply calculating the influence degree through the calculation processing described in FIG. 8. For example, if there is no particular problem with the manufacturing process or product quality, it is not necessary to inform the user, and also, there are cases where the user is not interested in the magnitude of the influence degree of the parameters on the event, but is interested in countermeasures against the event, as described later.

[0106] The timing at which the influence degree specification unit 235 specifies the influence degree may be regular or irregular. When specifying the influence degree regularly, for example, an interval of several seconds to several tens of minutes can be set. The influence degree specification unit 235 may also specify the influence degree based on manual instructions from the user (instructions from the user to specify the influence degree). Furthermore, in a case where the value of the index calculated by the estimation unit 233 exceeds a preset threshold value, the influence degree specification unit 235 may specify the influence degree of each parameter.

[0107] FIG. 10 is a schematic diagram showing the progression of actual measured values (a line graph) of the ORP (an example of a parameter) of the raw material system 1 for an event in the water system and the influence degree of the ORP of the raw material system 1 on the event. The influence degree specification unit 235 may also output to the user terminal 3 a screen showing the actual measured values and the influence degree over time, as shown in FIG. 10, for example. This allows the user to understand the change over time in the actual measured values of the parameter and the influence degree of that parameter, making the system easier for the user to use.

[0108] In this manner, in the embodiment, the influence degree is specified for the parameters (parameter P1 to parameter P6) related to the data set. This makes it possible to specify, with high accuracy, which factor among all parameters (all factors) is most influential with respect to the event that is currently occurring (e.g., a trouble in the manufacturing process), thereby improving usability for the system user and facilitating the management of events involving the water system.

[0109] <Activity A106: Present countermeasure information> The countermeasure presentation unit 236 presents countermeasure information in accordance with the influence degree specified by the influence degree specification unit 235 in Activity A105. As mentioned above, for example, if the above-mentioned parameter P2 "ORP of the raw material system 1" has the greatest influence degree as a factor causing the event, the countermeasure information for making the situation of "ORP of the raw material system 1" appropriate will be presented.

[0110] The countermeasure information is, for example, stored in a database by the storage management unit 238 of the information processing apparatus 2. The countermeasure information is linked to one or more countermeasures that should be taken when the influence degree is calculated to be large. That is, in one example of the embodiment, each of the parameter P1 to the parameter P6 is linked to at least one countermeasure information that is presented when the influence degree is high. As mentioned earlier, the countermeasure information to be presented may be visual information itself generated in a manner that is visible to the user, such as characters, numeral values, diagrams, photographs, videos, screens, images, icons, text, etc., or may be rendering information for displaying the visual information on various devices or terminals, for example. When there are multiple parameters with a high influence degree, the countermeasure information presented by the countermeasure presentation unit 236 may correspond to these multiple parameters with a high influence degree. Typically, when the influence degree of each of the parameter P1 and the parameter P2 is high, it is also possible to present one or two or more countermeasures that can effectively control both of these parameters P1 and P2.

[0111] In addition, in a case where the countermeasure presentation unit 236 is capable of presenting a plurality of pieces of countermeasure information, the display order may be based on the score. The display order may be random, and only the score may be displayed. The score indicates the degree to which the countermeasure information is recommended. The score may be, for example, a score set in advance by the user. The score may also reflect the results and effects of countermeasures taken by the user based on the countermeasure information. In this case, the countermeasure presentation unit 236 only needs to be able to accept feedback on the results and effects of the countermeasure taken by the user based on the countermeasure information. In other words, the user takes countermeasures based on the countermeasure information and the input of a score according to the results and effects of those countermeasures is accepted.

[0112] As described above, according to the information processing apparatus 2 (information processing system 1) of the present embodiment, the function of the influence degree specification unit 235 makes it possible to effectively specify the influence degree of a parameter on an event in the water system W. This enables those engaged in the process related to the water system W to easily take operational countermeasures, etc.

[0113] 4. Variation Section 4 describes a variation of the information processing method executed by the information processing system 1 or the like described above.

[0114] Although the above-mentioned embodiment is described as a configuration of the information processing system 1, an information processing method executed by an information processing system may be provided in which the method includes each step of executing processing of each unit of the information processing system. In addition, a program causing a computer to execute processing of each unit of the information processing system 1 may also be provided.

[0115] The embodiment described above shows an information processing method using the relationship model information, but information associated when creating the relationship model information is not limited thereto. That is, the relationship model information used in the present embodiment may be associated with various other conditions such as a weather condition, a condition related to region, and a condition related to the age of the facility.

[0116] In the above-described embodiment, the information processing system 1 executes various storage and control operations, but a plurality of external devices may be used instead of the information processing system 1. In other words, various information and the like may be divided to be stored into the plurality of the external devices by using blockchain technology, or the like.

[0117] In the embodiment described above, the aspect is shown in which the information processing apparatus 2 and the user terminal 3 function as separate devices. However, the user terminal 3 itself may have various functions as the controller 23 of the information processing apparatus 2. That is, the user terminal 3 may function as a stand-alone computer to execute processing such as acquiring various types of information, performing estimation, specifying the influence degree, and the like.

[0118] The above-described embodiments of the present disclosure are examples of the present invention, and various configurations other than the above-described configurations may be adopted. The present disclosure is not limited to the embodiments described above, and the present disclosure encompasses variations, improvements, etc. within the scope of the spirit of the present disclosure.

[0119] 1: Information processing system 2: Information processing apparatus 3: User terminal 4: Measurement device 20: Communication bus 21: Communication unit 22: Storage unit 23: Controller 30: Communication bus 31: Communication unit 32: Storage unit 33: Controller 34: Display unit 35: Input unit 231: Parameter information acquisition unit 232: Relationship model information acquisition unit 233: Estimation unit 234: Data set specification unit 235: Influence degree specification unit 236: Countermeasure presentation unit 237: Relationship model information creation unit 238: Storage management unit W: Water system

Claims

1. An information processing system that executes information processing related to an event in a water system or derived from the water system, the information processing system comprising: a relationship model information acquisition unit configured to acquire relationship model information indicating a relationship between an index related to the event, which has been created in advance, and two or more parameters, each of the two or more parameters being a parameter selected from a group consisting of a water quality parameter, a control parameter, and a result parameter, the water quality parameter being a parameter related to water quality of the water system, the control parameter being a parameter related to the water system, a facility related to the water system, or a control condition of a raw material to be added to the water system, the result parameter being a parameter that does not correspond to the index and relates to a result having arisen in the water system, a facility related to the water system, or a raw material to be added to the water system, or a result having been derived from the water system, a facility related to the water system, or a raw material to be added to the water system; a data set specification unit configured to specify a data set in which the index related to the event is associated with the two or more parameters; and an influence degree specification unit configured to specify an influence degree on the event for each of the parameters associated in the data set by comparing the relationship model information with the data set.

2. The information processing system according to claim 1, wherein: the influence degree specification unit is configured to specify the influence degree by performing a predetermined calculation processing to each of the parameters, and the calculation processing includes at least one of differential processing, statistical processing, and processing using a trained model.

3. The information processing system according to claim 1 or 2, wherein: the index and the two or more parameters associated in the data set are more recently obtained than the index and the two or more parameters associated in the relationship model information.

4. The information processing system according to any one of claims 1 to 3, wherein: the relationship model information is a model obtained from a regression analysis, a time series analysis, a decision tree, a neural network, Bayes, clustering, classification, or ensemble learning, between a prior measurement result corresponding to the index or an index related to the prior measurement result and the two or more parameters.

5. The information processing system according to any one of claims 1 to 4, further comprising: a parameter information acquisition unit configured to acquire, as parameter information, two or more parameters selected from a group consisting of the water quality parameter, the control parameter, and the result parameter; and an estimation unit configured to estimate the index based on the acquired parameter information and the relationship model information, wherein in the data set, the index estimated by the estimation unit is associated with the two or more parameters acquired by the parameter information acquisition unit.

6. The information processing system according to any one of claims 1 to 5, further comprising: a countermeasure presentation unit configured to present countermeasure information related to a corresponding parameter in accordance with the influence degree specified by the influence degree specification unit7. The information processing system according to any one of claims 1 to 6, wherein: the water system is a water system in a process of manufacturing a paper product and / or a material related to manufacture of a paper product.

8. The information processing system according to claim 7, wherein: the two or more parameters include, as the water quality parameter, one or more selected from a group consisting of pH of the water system, electrical conductivity, an oxidation-reduction potential, a zeta potential, turbidity, temperature, a foam height, a biochemical oxygen demand (BOD), a chemical oxygen demand (COD), total organic carbon (TOC), inorganic carbon, absorbance, color, appearance, whiteness, transparency, a particle size distribution, a degree of flocculation, an amount of foreign matter, suspended solids (SS), a foaming area on a water surface, an area of underwater contamination, an amount of bubbles, an amount of glucose, an amount of organic acids, an amount of active alkali, total titrated alkali, metal or a metal ion content, a non-metal ion content, an amount of starch, an amount of calcium, an amount of total chlorine, an amount of free chlorine, an amount of dissolved oxygen (DO), a cationic demand, an amount of hydrogen sulfide, a degree of sulfidation, an amount of hydrogen peroxide, ash concentration, a respiration rate of microorganisms in the system, a viable bacterial count, a spore-forming bacterial count, and ATP.

9. The information processing system according to claim 7 or 8, wherein: the two or more parameters include, as the control parameter, one or more selected from the group consisting of an operating speed (a papermaking speed) of a paper machine, a rotational speed of a filter cloth of a raw material dehydrator, a rotational speed of a filter cloth of a cleaning machine, an additive amount of chemicals or intensity to the water system, an additive amount of chemicals or intensity relative to a raw material to be added to the water system, an additive amount of chemicals or intensity relative to a facility related to the water system, an amount of vapor or intensity for heating, temperature of vapor for heating, pressure of vapor for heating, a flow rate from a seed box, nip pressure of a press part, felt vacuum pressure of a press part, a blending ratio of a papermaking raw material, a blended amount or intensity of waste sheets of papermaking raw material, a mesh size of a screen for a papermaking raw material, a gap distance between a rotor and a stator of a beater, freeness, a degree of beating, draw, linear pressure of a press part, a gravity dewatering amount of a wire part or equivalent parameters, a forced dewatering amount of a wire part or equivalent parameters, a gravity dewatering amount of a press part, parameters equivalent to a gravity dewatering amount of a press part, parameters for controlling the gravity dewatering amount of a press part, a forced dewatering amount of a press part, parameters equivalent to the forced dewatering amount of a press part, parameters for controlling the forced dewatering amount of a press part, concentration of interlayer adhesive, an amount or intensity of interlayer adhesive, shower water amount or intensity, fresh water intensity, water usage intensity, timing or frequency of cleaning a wire part, timing or frequency of cleaning a press part, timing or frequency of cleaning a dryer part, timing or frequency of changing tools of a paper machine, an fresh water replenishment amount, an amount of rejects from the screen or parameters for controlling them, differential pressure across the screen or a parameter equivalent thereto, an amount of rejects from the cleaner or a parameter for controlling them, a fuel amount or intensity for heating, concentration in a floatater, an amount of air in the floatater or a parameter equivalent thereto, an amount of froth in a floatater or parameters equivalent thereto, the load of a disperser or a parameter equivalent thereto, the number of felt singeing, a flow rate of fluid flowing through the water system, air temperature, an amount of vapor or intensity for heating, an amount or intensity of calcium carbonate to be added, an amount or intensity of calcium bicarbonate to be added, a flow rate or concentration of clarifying green liquor, a flow rate or concentration of raw green liquor, a flow rate or concentration of dilute black liquor, a flow rate or concentration of concentrated black liquor, a flow rate or concentration of white liquid, black liquor injection rate in a black liquor treatment system, black liquor injection concentration in a black liquor treatment system, an amount of pulp production, a moisture content of a mud filter, lime input or intensity in a slaking / causticizing system, an amount of lime production in a calcination system, fuel usage, a fuel usage intensity, causticizing efficiency in a slaking / causticizing system, caustic input or intensity in a cooking system, an amount of black liquor generated in a black liquor treatment system, an amount of solids in evaporating black liquor in a black liquor treatment system, a evaporation factor in evaporating black liquor in a black liquor treatment system, temperature of an economizer in a boiler in a black liquor treatment system, water 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 the calcium carbonate before calcination, and specific gravity of the calcium oxide after calcination.

10. The information processing system according to any one of claims 7 to 9, wherein: the two or more parameters include, as the result parameter, one or more selected from a group consisting of a unit weight (grammage) of the paper product, a yield, white water concentration, moisture content of the paper product, an amount of vapor or intensity in a facility manufacturing the paper product, temperature of vapor in a facility manufacturing the paper product, pressure of vapor in a facility manufacturing the paper product, thickness of the paper product, concentration of ash in the paper product, a type of defect of the paper product, the number of defects in the paper product, a timing of paper breakage in a process, freeness, a degree of beating, an amount of aeration, air temperature in a dryer, a joint rate, the number of joints, an amount of paper loss, the number of flaws, a flaw index, outdoor temperature including indoor temperature, outdoor humidity including indoor humidity, wet paper moisture content, product moisture content, temperature of rolls and cylinders in the dryer, a five-sense sensor measurement, a fuel amount or a fuel usage intensity, a calcination rate in a calcination system, a causticizing efficiency in a slaking / causticizing system, caustic input in a cooking system, and lime input in a slaking / causticizing system.

11. The information processing system according to any one of claims 7 to 10, wherein: the index relates to quality or an amount of energy of the paper product and / or a material associated with manufacture of the paper product, the quality relates to one or more selected from a group consisting of a number of defects, paper strength, a joint rate, a sizing degree, air permeability, smoothness, an ash content, a tone, whiteness, formation, odor, causticizing efficiency, calcination rate, a kappa value, freeness, and a moisture percentage of the paper product and / or the material associated with the manufacture of the paper product, and the amount of energy relates to one or more selected from a group consisting of an amount of vapor, a vapor intensity, a fuel amount, and a fuel usage intensity in manufacturing the paper product and / or the material associated with the manufacture of the paper product.

12. An information processing method executed by an information processing system, comprising each step of executing processing of each unit of the information processing system according to any one of claims 1 to 11.

13. A program configured to cause a computer to execute processing of each unit of the information processing system according to any one of claims 1 to 11.

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