Information processing system, information processing method, and program

Abstract

[Summary] An information processing system, etc. that can appropriately estimate an event that may arise in a pulp manufacturing system. According to an aspect of the present disclosure, an information processing system for estimating an event that may arise in a pulp manufacturing system is provided in which the pulp manufacturing system comprises a combination of: a first step of obtaining pulp by cooking wood chips with white liquor; a second step of obtaining green liquor by treating black liquor generated in the first step; a third step of obtaining clarified green liquor by treating the green liquor; a fourth step of obtaining white liquor and calcium carbonate by adding calcium oxide to the clarified green liquor, supplying the obtained white liquor to the first step, and supplying the obtained calcium carbonate to a subsequent fifth step; and a fifth step of obtaining calcium oxide by calcining the calcium carbonate, and supplying the obtained calcium oxide to the fourth step, the information processing system comprises: a parameter information acquisition unit configured to acquire, as parameter information, two or more parameters related to any of the first step to the fifth step; a relationship model information acquisition unit configured to acquire relationship model information indicating a relationship between the event or an index related to the event, which has been created in advance, and the two or more parameters; and an estimation unit configured to estimate the event or the index related to the event based on the acquired parameter information and the relationship model information.

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. 2024-120218, 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] Pulp is manufactured by adding water for cooking treatment that contains sodium hydroxide (hereinafter also referred to as "caustic soda") to wood chips and cooking them, and white liquor is used as the water for cooking treatment. Then, in the cooking process, the chips are cooked with alkali (white liquor) to obtain pulp, and cooking chemicals and heat energy are recovered from the pulp waste liquor (black liquor). In a pulp manufacturing process, chemicals are recovered from the cooking process, a pulp washing process, a black liquor concentration process, a black liquor combustion process, a green liquor treatment process, a white liquor treatment process, a slaking reaction process, a causticization reaction process, a lime calcination process, etc. and the recovered chemicals are reused. In this connection, the Patent Document 1 discloses a technique for easily managing the operation of the green liquor treatment.

[0003] [Patent Document 1] JP 2022-12850 A

[0004] On the other hand, there is still room to make the pulp manufacturing process more efficient. In particular, a technique for appropriately managing events that may arise in the process of reusing chemicals as described above is desired.

[0005] In view of the above circumstances, the present disclosure provides an information processing system, etc. that can appropriately estimate events that may arise in a pulp manufacturing system.

[0006] According to an aspect of the present disclosure, an information processing system for estimating an event that may arise in a pulp manufacturing system is provided in which the pulp manufacturing system comprises a combination of: a first step of obtaining pulp by cooking wood chips with white liquor; a second step of obtaining green liquor by treating black liquor generated in the first step; a third step of obtaining clarified green liquor by treating the green liquor; a fourth step of obtaining white liquor and calcium carbonate by adding calcium oxide to the clarified green liquor, supplying the obtained white liquor to the first step, and supplying the obtained calcium carbonate to a subsequent fifth step; and a fifth step of obtaining calcium oxide by calcining the calcium carbonate, and supplying the obtained calcium oxide to the fourth step, the information processing system comprises: a parameter information acquisition unit configured to acquire, as parameter information, two or more parameters related to any of the first step to the fifth step; a relationship model information acquisition unit configured to acquire relationship model information indicating a relationship between the event or an index related to the event, which has been created in advance, and the two or more parameters; and an estimation unit configured to estimate the event or the index related to the event based on the acquired parameter information and the relationship model information.

[0007] According to the above-mentioned aspect, an information processing system, etc. that can appropriately estimate events that may arise in a pulp manufacturing system is provided.

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

[0009] (1) An information processing system for estimating an event that may arise in a pulp manufacturing system, the pulp manufacturing system comprising a combination of: a first step of obtaining pulp by cooking wood chips with white liquor; a second step of obtaining green liquor by treating black liquor generated in the first step; a third step of obtaining clarified green liquor by treating the green liquor; a fourth step of obtaining white liquor and calcium carbonate by adding calcium oxide to the clarified green liquor, supplying the obtained white liquor to the first step, and supplying the obtained calcium carbonate to a subsequent fifth step; and a fifth step of obtaining calcium oxide by calcining the calcium carbonate, and supplying the obtained calcium oxide to the fourth step, the information processing system comprising: a parameter information acquisition unit configured to acquire, as parameter information, two or more parameters related to any of the first step to the fifth step; a relationship model information acquisition unit configured to acquire relationship model information indicating a relationship between the event or an index related to the event, which has been created in advance, and the two or more parameters; and an estimation unit configured to estimate the event or the index related to the event based on the acquired parameter information and the relationship model information.

[0010] (2) The information processing system according to (1), wherein: the parameter information acquisition unit is configured to acquire, as the parameter information, two or more parameters selected from a group consisting of a water quality parameter, an operation parameter, and a result parameter.

[0011] (3) The information processing system according to (2), wherein: the parameter information includes, as the water quality parameter, a parameter related to one or more selected from a group consisting of pH, electrical conductivity, 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 organic acids, an amount of active alkali, total titrated alkali, metal or a metal ion content, a non-metal ion content, 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, a viable bacterial count, a spore-forming bacterial count, and ATP, of a fluid circulating through the pulp manufacturing system.

[0012] (4) The information processing system according to (2) or (3), wherein: the parameter information includes, as the operation parameter, a parameter related to one or more selected from a group consisting of a type of the wood chips, quality of the wood chips, an amount of pulp production, water intensity or water usage amount in the pulp manufacturing system, a fresh water intensity or fresh water usage amount in the pulp manufacturing system, a bleaching chemical intensity or bleaching chemical usage amount, a white liquor addition rate in the first step, a steam intensity or a steam usage amount in the pulp manufacturing system, a flow rate of fluid circulating through the pulp manufacturing system, temperature of the fluid circulating through the pulp manufacturing system, an amount of cooking steam or intensity in the first step, evaporation multiple of an evaporator in the second step, a steam pressure of an evaporator in the second step, an amount of black liquor injection in the second step, a flow rate of diluted black liquor, an amount of dregs withdrawn from the green liquor, the addition amount of green liquor flocculant or the addition rate in the third step, an amount of lime mud withdrawn in the fourth step, calcium oxide input or intensity in the fourth step, causticizing chemical intensity or usage amount in the fourth step, a causticizing tank temperature in the fourth step, an amount of causticizing hot water or intensity in the fourth step, a differential pressure of a white liquor filter that filters white liquor produced in the fourth step, a discharge rate of calcium oxide produced in the fifth step, an amount of calcium oxide produced in the fifth step, an input amount or intensity of sodium hydroxide different from that of the white liquor in the first step, a temperature of a cyclone dryer in the fifth step, a salt cake input amount or intensity relative to the pulp manufacturing system, a type of fuel used in the fifth step, an amount of kiln air in the fifth step, a kiln heavy oil temperature in the fifth step, a kiln drive current in the fifth step, a kiln exhaust heat temperature in the fifth step, a moisture content of calcium carbonate supplied to the fifth step, a particle size of calcium carbonate generated in the fourth step, a particle size of calcium carbonate supplied to the fifth step, weather during operation, a rainfall amount, a usage amount or addition rate of a white liquor flocculant, a usage amount or intensity of a water-reducing agent, a lime mud filter rotation speed, a number of days of operation, and a dead load amount by an inert alkali.

[0013] (5) The information processing system according to any one of (2) to (4), wherein: the parameter information acquires, as the result parameter, a parameter different from an estimation target, and the result parameter includes a parameter related to one or more selected from a group consisting of a causticization rate in the fourth step, an amount of white liquor produced in the fourth step, an amount of active alkali in a fluid circulating in the pulp manufacturing system, a lime calcination rate in the fifth step, an amount of calcium oxide produced in the fifth step, a pulp yield in the first step, steam intensity or steam usage amount in the pulp manufacturing system, water intensity or water usage amount in the pulp manufacturing system, bleaching chemical intensity or bleaching chemical usage amount, causticizing chemical intensity or usage amount in the fourth step, an amount of grid generated in the fourth step, a fuel amount or intensity used in the fifth step, whiteness of pulp obtained in the first step, and a kappa value of pulp obtained in the first step.

[0014] (6) The information processing system according to any one of (1) to (5), wherein: the third step of the pulp manufacturing system includes: obtaining dregs and the clarified green liquor by treating the green liquor; generating weak liquor from the obtained dregs and supplying the weak liquor to the second step; and supplying the obtained clarified green liquor to the fourth step.

[0015] (7) The information processing system according to any one of (1) to (6), wherein: the event or the index related to the event estimated by the estimation unit is related to an event in a predetermined step selected from among the first step to the fifth step, and the parameter information acquisition unit is configured to acquire a parameter in a step different from the predetermined step among the first step to the fifth step.

[0016] (8) The information processing system according to any one of (1) to (7), wherein: the event or the index related to the event estimated by the estimation unit is related to an event in the fourth step or the fifth step.

[0017] (9) The information processing system according to any one of (1) to (8), 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 event or an index related to the prior measurement result and the two or more parameters.

[0018] (10) 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 (9).

[0019] (11) 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 (9). Of course, the present disclosure is not limited to the above aspects.

[0020] FIG. 1 show an overall configuration of an information processing system 1.FIG. 2 is a schematic diagram representing an example of an overall configuration of a pulp manufacturing system PM.FIG. 3 shows a hardware configuration of an information processing apparatus 2.FIG. 4 shows a hardware configuration of a user terminal 3.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 an example of an estimated result presented to a user.FIG. 8 shows a result of a simulation when a change to a parameter 1 is input.FIG. 9 shows a result of a simulation when a change to a potential result index is input.FIG. 10 is an explanatory diagram showing an example of a method for calculating an influence degree using an index X as an index (potential result).FIG. 11 is a graph schematically showing an influence degree on an event for each parameter.FIG. 12 is a schematic diagram showing progression of actual measured values (a line graph) of a parameter 1) and an influence degree of the parameter 1 on the event.FIG. 13 is a graph showing the results of Example 2.FIG. 14 is a graph showing the results of Example 3.

[0021] 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.

[0022] In other words, the information processing system according to the present embodiment is as follows. An information processing system for estimating an event that may arise in a pulp manufacturing system, the pulp manufacturing system comprising a combination of: a first step of obtaining pulp by cooking wood chips with white liquor; a second step of obtaining green liquor by treating black liquor generated in the first step; a third step of obtaining clarified green liquor by treating the green liquor; a fourth step of obtaining white liquor and calcium carbonate by adding calcium oxide to the clarified green liquor, supplying the obtained white liquor to the first step, and supplying the obtained calcium carbonate to a subsequent fifth step; and a fifth step of obtaining calcium oxide by calcining the calcium carbonate, and supplying the obtained calcium oxide to the fourth step, the information processing system comprising: a parameter information acquisition unit configured to acquire, as parameter information, two or more parameters related to any of the first step to the fifth step; a relationship model information acquisition unit configured to acquire relationship model information indicating a relationship between the event or an index related to the event, which has been created in advance, and the two or more parameters; and an estimation unit configured to estimate the event or the index related to the event based on the acquired parameter information and the relationship model information.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] The information processing system 1 according to the present embodiment is a system used to estimate an event that may arise in a pulp manufacturing system. 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, the information processing apparatus 2 is configured to measure various parameters related to a pulp manufacturing system PM. In a typical aspect, the pulp manufacturing system PM is provided with various measurement devices, and the parameters measured by these devices are configured to be transmitted to the information processing apparatus 2.

[0029] 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, and the pulp manufacturing system PM may be referred to as a system as well. The following continues the description of each configuration that may configure the information processing system 1.

[0030] <Pulp manufacturing system PM> First, the manufacturing system (pulp manufacturing system PM) to which the information processing system 1 of the present embodiment is applied will be described. FIG. 2 is a schematic diagram representing an example of an overall configuration of the pulp manufacturing system PM. The various configurations shown in FIG. 2 are merely one example of the pulp manufacturing system PM to which the information processing system 1 of the present embodiment can be applied. That is, in the pulp manufacturing system to which the present disclosure can be applied, various elements shown in FIG. 2 may be modified without departing from the abstract of the present disclosure. That is, a pulp manufacturing system to which the present disclosure can be applied may have some elements added to or some elements deleted from the various elements shown in FIG. 2. For example, although not shown, a tank or the like for storing products may be provided between each of the elements shown in FIG. 2.

[0031] As shown in FIG. 2, the pulp manufacturing system PM in the present embodiment includes a combination of a first step Pr1, a second step Pr2, a third step Pr3, a fourth step Pr4, and a fifth step Pr5. Here, the first step Pr1 is a step of obtaining pulp by cooking wood chips with white liquor. The second step Pr2 is a step of obtaining green liquor by treating the black liquor generated in the first step Pr1. The third step Pr3 is a step of obtaining clarified green liquor by treating the green liquor. The fourth step Pr4 is a step that includes obtaining white liquor and calcium carbonate by adding calcium oxide to the clarified green liquor, supplying the obtained white liquor to the first step Pr1, and supplying the obtained calcium carbonate to the fifth step Pr5. The fifth step Pr5 is a step that includes obtaining calcium oxide by calcining calcium carbonate, and supplying the obtained calcium oxide to the fourth step Pr4. Specific aspects of each step are described below. Note that within and between each step, the materials involved in pulp production are transferred as a fluid or powder. When the materials are transferred as a fluid, they are typically transferred by a pump or the like, but details thereof are omitted in the example in FIG. 2.

[0032] (First step Pr1) The first step Pr1 is a step of obtaining pulp by cooking wood chips with white liquor. Note that such a first step Pr1 may also be referred to as a "cooking system". In the present embodiment, the first step Pr1 includes a digester 511. As shown in FIG. 2, wood chips and white liquor are fed into the digester 511 to produce pulp.

[0033] The wood chips are appropriately selected in terms of species and size (particle size) depending on the pulp to be produced. The white liquor contains sodium hydroxide (caustic soda) and is supplied from the fifth step Pr5 described later. When cooking is performed in the first step Pr1, sodium hydroxide may be separately supplied from outside the pulp manufacturing system PM in addition to the white liquor supplied from the fifth step Pr5.

[0034] Although not shown in FIG. 2, the first step Pr1 may include a screening process for screening the pulp obtained by cooking, a washing process for washing the pulp, a bleaching process for bleaching the pulp, and the like. In a typical aspect, a cyclone cleaner or the like for washing pulp may be provided downstream of the digester 511 in the first step Pr1. The pulp obtained in the first step Pr1 is supplied to a papermaking process and the like to produce paper.

[0035] The waste liquid generated in the first step Pr1 is also called "black liquor", and in this pulp manufacturing system PM, the waste liquid is supplied to the second step Pr2 described below to recover sodium hydroxide.

[0036] (Second step Pr2) The second step Pr2 is a step of obtaining green liquor by treating the black liquor generated in the first step Pr1. Note that such a second step Pr2 may also be referred to as a "black liquor treatment system". In the present embodiment, the second step Pr2 includes an evaporator 521, a boiler 522, and a dissolution tank 523.

[0037] The evaporator 521 is a device that concentrates the black liquor generated in the first step Pr1. The black liquor concentrated by the evaporator 521 is transferred to the boiler 522 (recovery boiler) and burned in the boiler 522. This causes inorganic sodium salts contained in the black liquor to melt and be discharged as smelt from the bottom of the boiler 522. The black liquor before concentration is sometimes referred to as "dilute black liquor. The discharged smelt is transferred to the dissolution tank 523 and dissolved by water or the like. By dissolving the smelt in this manner, green liquor rich in sodium hydroxide and sodium carbonate is generated. In a typical aspect, weak liquor generated in the third step Pr3 and the fourth step Pr4 described below may also be added to this dissolution tank 523. The green liquor obtained in the second step Pr2 may also be referred to as "crude green liquor".

[0038] The boiler 522 may be provided with a heat recovery system for recovering heat energy. As such a heat recovery system, a conventionally known system is used (e.g. see JPH06-212586A.)

[0039] (Third step Pr3) The third step Pr3 is a step of obtaining clarified green liquor by treating the green liquor. Note that such a third step Pr3 may also be referred to as a "green liquor treatment system". In the present embodiment, the third step Pr3 includes a green liquor clarification device 531, a clarified green liquor tank 532, and a dregs treatment part 533.

[0040] The green liquor clarification device 531 may be a conventionally known device, such as a gravity settling type device or a forced filtration type device. The green liquor clarification device 531 may also be a device known as a green liquor clarifier. As the green liquor clarifier, a multi-stage clarifier, a unit clarifier, a dual-use storage tank clarifier, a sedimentation concentration type clarifier, etc. can be selected.

[0041] In the third step Pr3, a treatment (clarification treatment) is performed in which a green liquor treatment agent (green liquor flocculant) is added to the green liquor in the green liquor clarification device 531 or upstream thereof and solid-liquid separation is performed in the green liquor clarification device 531 to remove impurities such as undissolved components that remain in the crude green liquor from the crude green liquor. This clarification treatment results in a clarified green liquor from which impurities have been removed. In the present embodiment, the obtained clarified green liquor is transferred to the clarified green liquor tank 532. The green liquor treatment agent (green liquor flocculant) used as the third step Pr3 may be appropriately selected from among known materials. As an example, a green liquor treatment agent (green liquor flocculant) described in Japanese Patent No. 6901032 can be applied.

[0042] On the other hand, in the above-mentioned clarification treatment, a slurry-like sludge (green liquor mud) containing impurities is generated and accumulated in the green liquor clarification device 531. Therefore, the sludge accumulated in the green liquor clarification device 531 is withdrawn at a fixed rate (at regular intervals or when a certain amount is reached). This sludge may contain insoluble impurities (dregs) such as unburned carbon, calcium carbonate, aluminum hydroxide, iron oxide, and silicon dioxide, which result from the smelt generated by burning black liquor.

[0043] The third step Pr3 shown in FIG. 2 includes a dregs treatment part 533 and the dregs treatment part 533 is configured to treat the sludge (dregs) withdrawn from the green liquor clarification device 531. The dregs treatment part 533 is configured to treat the dregs to obtain weak liquor that dissolves the aforementioned smelt. That is, the third step Pr3 shown in the drawing includes obtaining dregs and clarified green liquor by treating green liquor, and includes supplying the obtained clarified green liquor to the fourth step Pr4 while generating weak liquor from the obtained dregs. Then, in the third step Pr3, the obtained weak liquor is supplied to the second step Pr2. The dregs can be treated here by appropriately combining known methods, such as a method that combines a mechanism for adding warm water to the dregs to disperse them (dregs mixer), a mechanism for washing the dregs (dregs washer), and a mechanism for filtering the dregs (dregs filter). The weak liquor obtained by filtering the dregs is transferred to the dissolution tank 523 of the second step Pr2, allowing smelt to be dissolved efficiently. In addition, although not limited thereto, the dregs washer here can use the filtrate obtained in a lime mud filter 551 described below as part of the washing liquid.

[0044] (Process 4 Pr4) The fourth step Pr4 is a step that includes obtaining white liquor and calcium carbonate by adding calcium oxide to the clarified green liquor, supplying the obtained white liquor to the first step Pr1, and supplying the obtained calcium carbonate to the fifth step Pr5. Note that such a fourth step Pr4 may also be referred to as a "slaking and causticizing system". In the present embodiment, the fourth step Pr4 includes a slaker 541, a plurality of causticization reaction tanks 542, a white liquor clarifier 543, a white liquor tank 544, and a lime mud washer 545.

[0045] The clarified green liquor obtained in the third step Pr3 is mixed with calcium oxide in the slaker 541. As a result, the calcium oxide is slaked with water to generate calcium hydroxide (slaked reaction process). Then, in the causticization reaction tank 542, the sodium carbonate in the clarified green liquor reacts with calcium hydroxide to produce sodium hydroxide (caustic soda) and calcium carbonate (causticizing reaction process).

[0046] The reaction liquid obtained in the causticization reaction tank 542 is transferred to the white liquor clarifier 543. In this white liquor clarifier 543, the insoluble calcium carbonate is settled and separated, and the supernatant liquid is transferred as white liquor to the white liquor tank 544. The white liquor transferred to the white liquor tank 544 is eventually supplied to the first step Pr1 and used for cooking wood pulp. Note that a white liquor filter for removing insoluble matter from the white liquor may be provided at least either upstream or downstream of the white liquor tank 544. This can improve the degree of purification of the white liquor supplied to the first step Pr1.

[0047] Meanwhile, the separated calcium carbonate is transferred to the lime mud washer 545, where it is washed, and then subjected to the fifth step Pr5. The washing conditions here can be set as appropriate. As an example, the filtrate obtained in the lime mud filter 551 described below can be used as a washing liquid. In addition, in the lime mud washer 545, the calcium carbonate that has settled after washing is recovered and transferred to the lime mud filter 551, and the supernatant liquid can be recovered as weak liquor. This weak liquor can be used to dissolve the smelt by transferring it to the dissolution tank 523 in the second step Pr2.

[0048] (Fifth step Pr5) The fifth step Pr5 is a step that includes obtaining calcium oxide by calcining calcium carbonate, and supplying the obtained calcium oxide to the fourth step Pr4. Note that such a fifth step Pr4 may also be referred to as a "calcining system". In the present embodiment, the fifth step Pr5 includes a lime mud filter 551 and a kiln 552.

[0049] The calcium carbonate generated in the fourth step Pr4 is transferred to the kiln 552 after a certain amount of water is removed by the lime mud filter 551. The calcium carbonate transferred to the kiln 552 is roasted and converted into calcium oxide. The calcium oxide converted in this way is then fed into the slaker 541 of the fourth step Pr4.

[0050] The water (filtrate) removed by the lime mud filter 551 can be transferred to the dissolution tank 523 of the second step Pr2 as weak liquor, as described earlier. In addition, an element for further removing moisture from the calcium carbonate may be provided between the lime mud filter 551 and the kiln 552. As an example, a cyclone dryer for drying calcium carbonate may be provided between the lime mud filter 551 and the kiln 552.

[0051] Each step (process) may be provided with a measurement device capable of measuring various parameters in the pulp manufacturing system PM. As described below, in an exemplary aspect of the present embodiment, a predetermined calculation processing can be performed using two or more parameters selected from the group consisting of a water quality parameter, an operation parameter, and a result parameter. The measurement device acquires parameters that form the basis of this calculation.

[0052] The measurement device may be set appropriately depending on contents of parameters to be measured. In a typical aspect, various sensors and the like can be selected as the measurement device. As the measurement device, 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.

[0053] It should be noted that in some cases, the operation parameters and the like that are directly input for controlling the device may be used as they are, and such data may be received from the device via communication. It should be noted that such parameters may be recorded outside the device by an operator of the device or the like for recording.

[0054] <Information processing apparatus 2> FIG. 3 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.

[0055] (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.

[0056] (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.

[0057] (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.

[0058] <User Terminal 3> FIG. 4 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 pulp manufacturing system PM. 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.

[0059] (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.

[0060] (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.

[0061] 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.

[0062] 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 simulation unit 234, a relationship model information creation unit 235, a data set specification unit 236, an influence degree specification unit 237, a countermeasure presentation unit 238, and a storage management unit 239. 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.

[0063] (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 related to any of the first step to the fifth step. The parameter information here may be various parameters in the pulp manufacturing system PM, but typically, the parameter information acquisition unit 231 acquires, as parameter information, two or more parameters selected from a group consisting of a water quality parameter, an operation parameter, and a result parameter. 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 that is capable of measuring at least a part of the parameters.

[0064] (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 an event or an index related to the event, which has been created in advance, and the two or more parameters. It should be noted that the "event" here may be referred to as a "potential result" or the like, since it is an estimation target in the pulp manufacturing system PM. The details of this model will be explained later.

[0065] (Estimation unit 233) The estimation unit 233 is configured to execute an estimation step. In the estimation step, the estimation unit 233 estimates an event or an index related to the event based on the acquired parameter information and relationship model information. Note that the index estimated by the estimation unit 233 may be referred to as a potential result or a related index. The contents estimated by the estimation unit 233 may be presented to the user as an estimated result. Here, the estimated result may be presented to the user in association with each parameter included in the parameter information. The presentation (estimated result) here is typically 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. Such display information is typically displayed on the display unit 34 of the user terminal 3. 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. The details of the information processing related to the estimation will be explained later.

[0066] (Simulation unit 234) The simulation unit 234 is configured to execute a simulation step. IIn the simulation step, the simulation unit 234 accepts from the user a change to at least one of the estimated result and each of the parameters presented by the estimation unit 233, and estimates a variation of an item to which a change from the user has not been accepted among the estimated result and each of the parameters based on the content of the accepted change and the relationship model information. The details of the information processing related to this simulation will be explained later.

[0067] (Relationship model information creation unit 235) The relationship model information creation unit 235 is configured to execute a relationship model information creation step. In the relationship model information creation step, the relationship model information creation unit 235 creates or updates relationship model information to be used in the above-mentioned estimation step and the like.

[0068] (Data set specification unit 236) The data set specification unit 236 is configured to execute a data set specification step. In the data set specification step, the data set specification unit 236 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 236 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 236 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 237.

[0069] (Influence degree specification unit 237) The influence degree specification unit 237 is configured to execute an influence degree specification step. In the influence degree specification step, the influence degree specification unit 237 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 237 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 237 may be referred to as an "influence degree estimation unit" or "influence degree inference unit.

[0070] (Countermeasure presentation unit 238) The countermeasure presentation unit 238 is configured to execute a countermeasure presentation step. In the countermeasure presentation step, the countermeasure presentation unit 238 presents countermeasure information related to the corresponding parameter in accordance with the influence degree specified by the influence degree specification unit 237. The above-mentioned influence degree specification unit 237 specifies the influence degree, allowing the user to understand the main factors or the like that cause the event. The countermeasure presentation unit 238 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.

[0071] (Storage management unit 239) The storage management unit 239 is configured to execute a storage management step. In the storage management step, the storage management unit 239 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 239 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 239 may also manage various kinds of information so as to be stored in an external storage device or the like.

[0072] 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.

[0073] 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, in a typical aspect, the parameter information acquisition unit 231 acquires, as the parameter information, two or more parameters selected from the group consisting of the water quality parameter, the operation parameter, and the result parameter.

[0074] That is, the parameter information is related to the pulp manufacturing system PM and may include two or more parameters selected from the group consisting of the water quality parameter, the operation parameter, and the result parameter. It should be noted that in a case where a process related to the water quality parameter, the operation parameter, or the result parameter is divided by tanks or the like, the water quality parameter, the operation parameter, or the result parameter with respect to a part of the process may be used, or the water quality parameter, the operation parameter, or the result parameter with respect to the entire process may also be used.

[0075] The water quality parameter is not particularly limited as long as the parameter is related to the fluid circulating through the pulp manufacturing system PM. For example, in the aforementioned pulp manufacturing system PM, fluids such as white liquor, black liquor, green liquor, weak liquor, lime mud, etc. circulate in the process, and these fluids usually contain water. In other words, the water quality parameter is a parameter relating to such fluids, and is not limited to liquid fluids, but may be a parameter relating to slurry fluids. In addition, the operation parameter is not particularly limited as long as it is related to operating conditions related to the pulp manufacturing system PM, a facility related to the pulp manufacturing system PM, a raw material to be added to the pulp manufacturing system PM, or the like. Furthermore, the result parameter is a parameter that arises as a result during the operation of the pulp manufacturing system PM, but is a parameter that does not correspond to the estimation target.

[0076] Hereinafter, specific examples of the water quality parameter, the operation parameter, and the result parameter in the pulp manufacturing system PM.

[0077] That is, the parameter information may include, as the water quality parameter, a parameter related to one or more selected from the group consisting of pH, electrical conductivity, 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 organic acids, an amount of active alkali, total titrated alkali, metal or a metal ion content, a non-metal ion content, 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, a viable bacterial count, a spore-forming bacterial count, and ATP, of the fluid circulating through the pulp manufacturing system.

[0078] 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).

[0079] In addition, the parameter information may include, as the operation parameter, a parameter related to one or more selected from a group consisting of the type of wood chips, quality of wood chips, an amount of pulp production, water intensity or water usage amount in the pulp manufacturing system, a fresh water intensity or fresh water usage amount in the pulp manufacturing system, a bleaching chemical intensity or bleaching chemical usage amount, a white liquor addition rate in the first step, a steam intensity or a steam usage amount in the pulp manufacturing system, a flow rate of fluid circulating through the pulp manufacturing system, temperature of the fluid circulating through the pulp manufacturing system, an amount of cooking steam or intensity in the first step, evaporation multiple of an evaporator in the second step, a steam pressure of an evaporator in the second step, an amount of black liquor injection in the second step, a flow rate of diluted black liquor, an amount of dregs withdrawn from green liquor, the addition amount of green liquor flocculant or the addition rate in the third step, an amount of lime mud withdrawn in the fourth step, calcium oxide input or intensity in the fourth step, causticizing chemical intensity or usage amount in the fourth step, a causticizing tank temperature in the fourth step, an amount of causticizing hot water or intensity in the fourth step, a differential pressure of a white liquor filter that filters white liquor produced in the fourth step, a discharge rate of calcium oxide produced in the fifth step, an amount of calcium oxide produced in the fifth step, an input amount or intensity of sodium hydroxide different from that of the white liquor in the first step, a temperature of a cyclone dryer in the fifth step, a salt cake input amount or intensity relative to the pulp manuracturing system, a type of fuel used in the fifth step, an amount of kiln air in the fifth step, a kiln heavy oil temperature in the fifth step, a kiln drive current in the fifth step, a kiln exhaust heat temperature in the fifth step, a moisture content of calcium carbonate supplied to the fifth step, a particle size of calcium carbonate generated in the fourth step, a particle size of calcium carbonate supplied to the fifth step, weather during operation, a rainfall amount, a usage amount or addition rate of a white liquor flocculant, a usage amount or intensity of a water-reducing agent, a lime mud filter rotation speed, the number of days of operation, and a dead load amount by an inert alkali.

[0080] The above-mentioned "bleaching chemical" corresponds to caustic soda, chlorine dioxide, oxygen, hydrogen peroxide, etc. In addition, the above-mentioned "causticizing chemical" corresponds to salt cake, hydrated lime, caustic soda, sodium sulfide, etc. Also, in relation to the "a type of fuel used in the fifth step" mentioned above, the "fuel" in the present specification is a concept that can include heavy oil, petroleum coke, etc. Furthermore, in relation to " a temperature of a cyclone dryer in the fifth step", the point at which the "temperature" is acquired in this specification 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.

[0081] In addition, the parameter information may include, as the result parameter, a parameter related to one or more selected from a group consisting of a causticization rate in the fourth step, an amount of white liquor produced in the fourth step, an amount of active alkali in a fluid circulating in the pulp manufacturing system, a lime calcination rate in the fifth step, an amount of calcium oxide produced in the fifth step, a pulp yield in the first step, steam intensity or steam usage amount in the pulp manufacturing system, water intensity or water usage amount in the pulp manufacturing system, bleaching chemical intensity or bleaching chemical usage amount, causticizing chemical intensity or usage amount in the fourth step, an amount of grid generated in the fourth step, a fuel amount or intensity used in the fifth step, whiteness of pulp obtained in the first step, and a kappa value of pulp obtained in the first step.

[0082] 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 operation 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 an operation 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 an operation 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 an operation 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 an operation 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 an operation 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 an operation 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 an operation 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.

[0083] 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.

[0084] Note that the water quality parameter, the operation parameter, and the result parameter are respectively concepts encompassing a plurality of parameters. 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 operation 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 operation parameter, and the result parameter or two or more parameters may be selected from a combination of two or three (for example, pH of water, a type of wood chips, and a causticization rate) of the water quality parameter, the operation 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).

[0085] While acquiring the parameter information as described above, the relationship model information acquisition unit 232 of the information processing apparatus 2 acquires relationship model information (Activity A102). The order of performing Activity A101 and Activity A102 here 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).

[0086] This relationship model information indicates a relationship between the event that may arise in the pulp manufacturing system PM, which has been created in advance, and two or more parameters. Note that "in advance" means prior to estimating an event and may be either during actual operation of the pulp manufacturing system PM or prior to actual operation, as long as it is prior to estimating the event.

[0087] In addition, the "potential result" related to the relationship model information is related to various events related to the pulp manufacturing system PM. The estimation unit 233 of the information processing apparatus 2 of the present embodiment estimates the predetermined event, and the event related to the relationship model information also corresponds to the result estimated by the estimation unit 233.

[0088] The event to be estimated can be set appropriately, but typically it may be the following.

[0089] That is, in the present embodiment, the event to be estimated may be related to a parameter related to one or more selected from a group consisting of a causticization rate in the fourth step, an amount of white liquor produced in the fourth step, an amount of active alkali of a fluid circulating in the pulp manufacturing system, a lime calcination rate in the fifth step, an amount of calcium oxide produced in the fifth step, a pulp yield in the first step, steam intensity or steam usage amount in the pulp manufacturing system, water intensity or water usage amount in the pulp manufacturing system, bleaching chemical intensity or bleaching chemical usage amount, causticizing chemical intensity or usage amount in the fourth step, an amount of grid generated in the fourth step, a fuel amount or intensity used in the fifth step, whiteness of pulp obtained in the first step, and a kappa value of the pulp obtained in the first step.

[0090] That is, the estimation target 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).

[0091] It should be noted that the events to be estimated are not limited to the above items themselves, but may be a combination of the above items or related items related to the above items. For example, the produce of the causticization rate in the fourth step and the amount of white liquor produced in the fourth step may be an estimated event (for example, this product may be identified and evaluated as a "causticization efficiency index"). Furthermore, examples of the related item include the amount of energy (amount of fuel used) required to achieve a predetermined causticization rate (causticization rate in the fourth step). In other words, the related item here may be an item that has a certain relationship to the above items and is useful in operating the pulp manufacturing system PM.

[0092] Examples of the relationship model information include, but is not particularly limited to, a function or a look-up table indicating a relationship between the event to be estimated and two or more parameters, or a trained model of a relationship between the event to be estimated and two or more parameters.

[0093] This relationship model information is created, for example, as follows. That is, prior to estimating a predetermined event, a prior measurement index related to the predetermined event is measured. In addition, in the same water system, two or more parameters, each of which is one of the water quality parameter, the operation parameter, or the result parameter, are measured. A plurality of sets of data of these prior measurement index and the parameters are prepared, for example, by changing the day and time on which measurements are taken, so that variations occur in the prior measurement index and the parameters. Next, the prior measurement index is assumed to be a function of two or more parameters, and the form of the function and coefficients are determined by comparing with the prior measurement index, thereby constructing the relationship model information. Here, the relationship model information may be a model obtained by performing a predetermined processing related to a prior measurement result corresponding to the event or an index related to the 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.

[0094] In an embodiment, the relationship model information is preferably a model obtained from a regression analysis between a prior measurement result corresponding to the event 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.

[0095] It is preferable that the relationship model information is created in the same system as the system in which the event is estimated. In addition, for example, in a case where the system changes significantly even in a same apparatus, a relationship model information with respect to the system after the system changes is preferably created and used.

[0096] From such a viewpoint, the event and the two or more parameters may be measured on a regular or irregular basis during operation of the pulp manufacturing system PM, the relationship model information may be created every time, or the relationship model information may be updated by adding data.

[0097] Such relationship model information may be created and updated by, for example, the function of the relationship model information creation unit 235 included in the information processing apparatus 2. That is, the relationship model information creation unit 235 can create relationship model information suitable for the information processing of the present embodiment by performing predetermined calculation processing on the acquired prior measurement result or the index related to the prior measurement result 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.

[0098] After acquiring the parameter information and the relationship model information in this manner, the estimation unit 233 of the information processing apparatus 2 estimates an event or an index related to the event based on the acquired parameter information and relationship model information (Activity A103). The event or the index related to the event here may be presented to the user as an estimated result. More specifically, the estimated result may be presented to the user in association with each parameter included in the parameter information.

[0099] Such an estimated result can be typically output by inputting the acquired parameter information into the relationship model information. In other words, it is possible to present the event or the index related to the event, which is output by the input processing for such relationship model information, in a manner that the user can understand.

[0100] In an exemplary embodiment, the event or the index related to the event estimated by the estimation unit 233 is related to an event in a predetermined step selected from among the first step to the fifth step, and it is preferable that the parameter information acquisition unit acquires a parameter in a step different from the predetermined step among the first step to the fifth step. As described above, in the pulp manufacturing system PM, various materials may circulate within the system, thereby acquiring parameter information outside of a predetermined step, and performing estimation based on this information may contribute to improving the accuracy of estimation.

[0101] Furthermore, although not limited thereto, the event or the index related to the event estimated by the estimation unit 233 may be related to the event in the fourth step or the fifth step. The slaking / causticizing system and the calcining system are identified as steps that require particularly strict management in the pulp manufacturing system PM, taking into account pulp quality and energy efficiency. The information processing system 1 of the present embodiment is suitably used for estimating an event related to such a step.

[0102] The information processing method of the present embodiment may include the steps shown below.

[0103] (Simulation step) The simulation step in the present embodiment includes accepting from the user a change to at least one of the estimated result and each of the parameters presented by the estimation unit 233, and estimating a variation of an item to which a change from the user has not been accepted among the estimated result and each of the parameters based on the content of the accepted change and the relationship model information.

[0104] The simulation step described above is typically triggered by the estimation unit 233 presenting the estimated result to the user. FIG. 7 shows an example of an estimated result presented to the user. In FIG. 7, the parameter information (parameters 1 to 5) used by the estimation unit 233 is associated with the potential result index and displayed on the display unit 34 of the user terminal 3. That is, a predetermined screen showing the estimated result may be displayed on the display unit 34 based on the function of the estimation unit 233. In addition, forms F1 to F5 corresponding to each parameter and a form F100 corresponding to the potential result index (an index related to the potential result) are provided on the screen shown in FIG. 7, and each form shows a parameter (measured value) used by the estimation unit 233 in the estimation processing or an estimated result (estimated value).

[0105] That is, a user who meets the screen as shown in FIG. 7 can input a change to at least one of the estimated result and each of the parameters. The example shown in FIG. 7 is configured to allow various numerical values to be input for the forms F1 to F5 and F100, and the aforementioned change is accepted in such a manner that the user inputs any numerical value into the forms.

[0106] FIG. 8 shows a result of a simulation when a change to the parameter 1 is input. That is, when the user inputs a value V10 into the form F1, the value shown in the form F100 indicating the potential result index is changed to an estimated value of V200. The estimated value (V200) shown in this form F100 can be obtained by inputting the input value (V10) for the parameter 1 and the measured values (V2 to V5) for the parameters 2 to 5 into the relationship model information described above.

[0107] The example shown in FIG. 8 shows an aspect in which the estimation is performed without varying each of the parameters 2 to 5 from the values corresponding to the aforementioned parameter information. In other words, in the present embodiment, it is possible to perform a simulation after fixing the measurement result and the values of the parameters to the contents presented as the estimated result. The control related to such a change of the value can be set arbitrarily by the user.

[0108] Of course, in the example shown in FIG. 8, it is also possible to accept changes to some or all of the parameters 2 to 5. Even in that case, the estimated value of the potential result index is calculated based on the input value of each parameter, as mentioned above.

[0109] On the other hand, the simulation unit 234 may execute the processing shown below. FIG. 9 shows a result of a simulation when a change to the potential result index is input. In the example shown in FIG. 9, when the user inputs a value V300 of the potential result index into the form F100, combinations of values that satisfy the value V300 of the potential result index are presented in the forms F1 to F5 corresponding to the parameters 1 to 5. The combinations of values presented in the forms F1 to F5 are combinations of values derived based on the value V300 and the relationship model information. That is, such combinations of values can be obtained by inversely calculating from the value V300 of the potential result index based on the relationship model information. Note that in the case where multiple combinations of the estimated values V10, V20, V30, V40, and V50 are assumed, all of the multiple assumed combinations may be presented, some of them may be presented, or the combination with the highest feasibility may be presented. The high feasibility here may be evaluated based on the operating history of the pulp manufacturing system PM and the like. As an example, a highly feasible combination may be a combination that reduces the cost of achieving each estimated value. Also, in the example shown in FIG. 9, it is possible to perform a simulation after fixing some of the parameters 1 to 5 to the contents presented as the estimated result.

[0110] The manner in which a change is made to the estimated result or each parameter is not limited to the manner shown in FIG. 8 and FIG. 9 (inputting numerical values into the form). By way of example, it is assumed that the estimation unit 233 indicates at least a part of the estimated result and each of the parameters as a graph (such as a bar graph). In that case, it is also possible for the simulation unit 234 to accept a change to the potential result and each of the parameters when the user operates to change the shape of the graph. The presentation of the estimated result of the variation in this case may also be realized by changing the shape of the graph for the corresponding item.

[0111] The simulation step described above may also be realized by calculations using SHAP. In an exemplary embodiment, the SHAP value (Shapley value) is determined in advance for each parameter of the relationship model information, and the estimated result and each of the parameters can be estimated based on the SHAP value. For example, in the example of FIG. 8 described above, the value of the potential result index can be estimated based on the SHAP value determined in advance when the user changes the parameter. On the other hand, in the example of FIG. 9 described above, when the user changes the value of the potential result index, a calculation may be performed based on the SHAP value so as to seek a value that approximates the value to which this potential result index has been changed.

[0112] Furthermore, the simulation unit 234 of the information processing apparatus 2 may show a simulation result such as a parameter, an index, etc., in the following manner.

[0113] That is, after the estimation unit 233 has estimated a predetermined index based on each parameter, a simulation in which the value of a certain parameter is reduced by a certain percentage may be presented to the user as a scatter diagram (graph). In this scatter diagram (graph), the horizontal axis indicates the value of the parameter before the change, and the vertical axis indicates the amount of change in a predetermined index. In addition, in such a scatter diagram (graph), the results of a simulation based on the data accumulated as past relationship model information and the results of a simulation based on the latest data set (i.e., the most recent data set used by the estimation unit 233 for estimation) may be shown in different forms. In other words, a user who meets such a scatter diagram (graph) can intuitively understand how the events in the pulp manufacturing system PM will change when the current parameters are reduced by a predetermined amount.

[0114] (Influence degree specification step) The influence degree specification step in the present embodiment is configured to specify the influence degree on the event for each parameter included in the data set by comparing the relationship model information with the data set.

[0115] In executing such an influence degree specification step, the data set specification unit 236 first specify a data set in which an index related to an event (potential result or related index) is associated with two or more parameters.

[0116] 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.

[0117] The data set here 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 described later, and also improves calculation accuracy of the influence degree.

[0118] However, the data set specified by the data set specification unit 236 is not limited thereto. For example, the data set specified in the data set specification unit 236 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 236 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.

[0119] 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 236.

[0120] After specifying the data set in this way, the influence degree specification unit 237 specifies the influence degree on the event for each parameter included in the data set by comparing the relationship model information acquired by the relationship model information acquisition unit 232 with the specified 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 that causes the event (the aforementioned trouble).

[0121] There may be various methods for specifying the influence degree, but in a typical aspect, the influence degree specification unit 237 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.

[0122] Here, a case where differential processing is used as the calculation processing for calculating the influence degree will be described as an example. FIG. 10 is an explanatory diagram showing an example of a method for calculating the influence degree using an index X as an index (potential result). FIG. 11 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. 10.

[0123] In the equation representing the index X in FIG. 10, t0, t1, t2, t3, t4, t5, and t6 are determined in advance. The equation related to the index X shown in FIG. 10 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.

[0124] 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 index X. Note that FIG. 10 shows an example of the calculation of the influence degree V (1), which is the value of the influence degree of the parameter P1.

[0125] 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.

[0126] Then, the ID (p) is obtained by substituting the value of the parameter of the data set into the equation related to the index X. 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.

[0127] As shown in FIG. 10, 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. 10.

[0128] Although not shown in FIG. 10, 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.

[0129] In this way, the influence degree is specified by comparing relationship model information that can be acquired by the relationship model information acquisition unit 232 with the data set specified by the data set specification unit 236. 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.

[0130] 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 index X 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.

[0131] 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.

[0132] 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.

[0133] When the influence degree specification unit 237 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 in various forms. In other words, the influence degree specification unit 237 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. 11, 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. 11, the user can visually recognize that the parameter having a greatest influence degree as a factor causing the event is the parameter P1.

[0134] 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.11, the system will be easier for users to use. For example, the influence degree specification unit 237 can specify a parameter having a large influence degree by highlighting it on the manufacturing flow diagram.

[0135] 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. 10. 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.

[0136] The timing at which the influence degree specification unit 237 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 237 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 237 may specify the influence degree of each parameter.

[0137] The influence degree specification unit 237 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. 12, for example. FIG. 12 is a schematic diagram showing the progression of actual measured values (a line graph) of the parameter 1 and the influence degree of the parameter 1 on the event. 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.

[0138] 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 pulp manufacturing system.

[0139] (Countermeasure presentation step) The countermeasure presentation step is configured to present countermeasure information in accordance with the influence degree specified by the influence degree specification unit 237. As mentioned above, for example, if the above-mentioned parameter 1 has the greatest influence degree on the event, the countermeasure information for making the situation of the parameter 1 appropriate will be presented.

[0140] The countermeasure information is, for example, stored in a database by the storage management unit 239 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 parameters is linked to at least one countermeasure information that is presented when the influence degree is high. 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 238 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.

[0141] In addition, in a case where the countermeasure presentation unit 238 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 238 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.

[0142] As described above, in the information processing system 1 of the present embodiment, the event that may arise in the pulp manufacturing system can be appropriately estimated.

[0143] 4. Variation Section 4 describes a variation of the information processing method executed by the information processing system 1 or the like described above.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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 an acquisition of various types of information, an estimation, a simulation, a specification of the influence degree, and the like.

[0148] The present disclosure will be described in more detail below with reference to examples, but the present disclosure is not limited to the following examples in any way.

[0149] <Example 1> In the pulp manufacturing system PM shown in FIG. 2, the kiln heavy oil intensity (kiln heavy oil usage amount) was set as the estimation target, and relationship model information was created. Specifically, in Example 1, the following various parameters were selected and used as the parameter information. When creating the relationship model information, each parameter was weighted according to its importance.

[0150] - Water quality parameter Iron content of green liquor Iron content of white liquor - Operation parameter Differential pressure of a white liquor filter that filters white liquor produced in the fourth step Amount of kiln air in the fifth step Kiln heavy oil temperature in the fifth step Kiln drive current in the fifth step Evaporation multiple of an evaporator in the second step - Result Parameter Causticization rate in the fourth step Amount of white liquor produced in the fourth step

[0151] In the Example 1, when the estimation equation (relationship model information) was created using only the operation parameter and the result parameter, the correlation coefficient was less than 0.7. However, by including the water quality parameter in the parameter information, the correlation coefficient exceeded 0.7.

[0152] <Example 2> In the pulp manufacturing system PM shown in FIG. 2, the relationship model information was created by setting the causticization rate and the kiln heavy oil intensity (kiln heavy oil usage amount) as the estimation target. Specifically, in Example 2, the following various parameters were selected and used as parameter information. When creating relationship model information corresponding to each estimation target, each parameter was weighted according to its importance.

[0153] - Water quality parameter Amount of active alkali in white liquor Total titrated alkali in white liquor Iron content of white liquor Suspended solids in green liquor Total titrated alkali in green liquor - Operation parameter Input amount of calcium oxide in the fourth step Input amount of sodium hydroxide different from the white liquor in the first step Amount of cooking steam in the first step Amount of black liquor injection in the recovery boiler in the second step Flow rate of diluted black liquor - Result parameter Amont of calcium oxide produced in the fifth step

[0154] As a result, an estimation equation was created with a correlation coefficient of 0.807 for the causticization rate and a correlation coefficient of 0.579 for the kiln heavy oil intensity. In this Example 2, an actual machine test was also conducted in the pulp manufacturing system PM. FIG. 13 is a graph showing the results of Example 2. Specifically, FIG. 13A shows a trend graph related to the causticization rate, and FIG. 13B shows a trend graph related to the kiln heavy oil intensity. The learning data (relationship model information) is verified during the period on the left side of the graph, and the test data from the actual machine was obtained during the period on the right side of the graph. As can be seen from this graph, a correlation has been confirmed between the predicted values and actual measured values when using the relationship model information.

[0155] <Example 3> In the pulp manufacturing system PM shown in FIG. 2, the relationship model information was created by setting the activation efficiency index as the estimation target. The activation efficiency index here is defined as the product of the causticization rate and the flow rate (amount of production) of white liquor. Specifically, in Example 3, the following various parameters were selected and used as the parameter information. When creating relationship model information, each parameter was weighted according to its importance.

[0156] - Water quality parameter Degree of sulfidation of white liquor - Operation parameter Flow rate of crude green liquor Amount of dregs withdrawn Addition amount of green liquor flocculant in the third step Amount of black liquor injection in the recovery boiler in the second step Discharge rate of calcium oxide produced in the fifth step Causticizing tank temperature in the fourth step Amount of causticizing hot water in the fourth step Inlet temperature of the cyclone dryer in the fifth step - Result parameter Amont of calcium oxide produced in the fifth step

[0157] In addition, in Example 3, an actual machine test was also conducted in the pulp manufacturing system PM. FIG. 14 is a graph showing the results of Example 3. The learning data (relationship model information) is verified during the period on the left side of the graph (measurement period), and the test data from the actual machine was obtained during the period on the right side of the graph (operation period). As can be seen from this graph, a correlation has been confirmed between the predicted values and actual measured values when using the relationship model information. In Example 3, the operational procedures for factors that have a high influence degree on the causticization index (specifically, the amount of dregs withdrawn and the discharge rate of calcium oxide produced in the fifth step) were changed. As a result, the efficiency of the causticization efficiency index improved by approximately 4%. In addition, this led to a 2% increase in pulp production amount in the pulp manufacturing system PM.

[0158] 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.

[0159] 1: Information processing system 2: Information processing apparatus 3: User terminal 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 40: Estimated value 50: Estimated value 231: Parameter information acquisition unit 232: Relationship model information acquisition unit 233: Estimation unit 234: Simulation unit 235: Relationship model information creation unit 236: Data set specification unit 237: Influence degree specification unit 238: Countermeasure presentation unit 239: Storage management unit 511: Digester 521: Evaporator 522: Boiler 523: Dissolution tank 531: Green liquor clarification device 532: Clarified green liquor tank 533: Dregs treatment part 541: Slaker 542: Causticization reaction tank 543: White liquor clarifier 544: White liquor tank 545: Lime mud washer 551: Lime mud filter 552: Kiln F1-F5, F100: Form PM: pulp manufacturing system Pr1: First step Pr2: Second step Pr3: Third step Pr4: Fourth step Pr5: Fifth step

Claims

1. An information processing system for estimating an event that may arise in a pulp manufacturing system, the pulp manufacturing system comprising a combination of: a first step of obtaining pulp by cooking wood chips with white liquor; a second step of obtaining green liquor by treating black liquor generated in the first step; a third step of obtaining clarified green liquor by treating the green liquor; a fourth step of obtaining white liquor and calcium carbonate by adding calcium oxide to the clarified green liquor, supplying the obtained white liquor to the first step, and supplying the obtained calcium carbonate to a subsequent fifth step; and a fifth step of obtaining calcium oxide by calcining the calcium carbonate, and supplying the obtained calcium oxide to the fourth step, the information processing system comprising: a parameter information acquisition unit configured to acquire, as parameter information, two or more parameters related to any of the first step to the fifth step; a relationship model information acquisition unit configured to acquire relationship model information indicating a relationship between the event or an index related to the event, which has been created in advance, and the two or more parameters; and an estimation unit configured to estimate the event or the index related to the event based on the acquired parameter information and the relationship model information.

2. The information processing system according to claim 1, wherein: the parameter information acquisition unit is configured to acquire, as the parameter information, two or more parameters selected from a group consisting of a water quality parameter, an operation parameter, and a result parameter.

3. The information processing system according to claim 2, wherein: the parameter information includes, as the water quality parameter, a parameter related to one or more selected from a group consisting of pH, electrical conductivity, 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 organic acids, an amount of active alkali, total titrated alkali, metal or a metal ion content, a non-metal ion content, 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, a viable bacterial count, a spore-forming bacterial count, and ATP, of a fluid circulating through the pulp manufacturing system.

4. The information processing system according to claim 2 or 3, wherein: the parameter information includes, as the operation parameter, a parameter related to one or more selected from a group consisting of a type of the wood chips, quality of the wood chips, an amount of pulp production, water intensity or water usage amount in the pulp manufacturing system, a fresh water intensity or fresh water usage amount in the pulp manufacturing system, a bleaching chemical intensity or bleaching chemical usage amount, a white liquor addition rate in the first step, a steam intensity or a steam usage amount in the pulp manufacturing system, a flow rate of fluid circulating through the pulp manufacturing system, temperature of the fluid circulating through the pulp manufacturing system, an amount of cooking steam or intensity in the first step, evaporation multiple of an evaporator in the second step, a steam pressure of an evaporator in the second step, an amount of black liquor injection in the second step, a flow rate of diluted black liquor, an amount of dregs withdrawn from the green liquor, the addition amount of green liquor flocculant or the addition rate in the third step, an amount of lime mud withdrawn in the fourth step, calcium oxide input or intensity in the fourth step, causticizing chemical intensity or usage amount in the fourth step, a causticizing tank temperature in the fourth step, an amount of causticizing hot water or intensity in the fourth step, a differential pressure of a white liquor filter that filters white liquor produced in the fourth step, a discharge rate of calcium oxide produced in the fifth step, an amount of calcium oxide produced in the fifth step, an input amount or intensity of sodium hydroxide different from that of the white liquor in the first step, a temperature of a cyclone dryer in the fifth step, a salt cake input amount or intensity relative to the pulp manufacturing system, a type of fuel used in the fifth step, an amount of kiln air in the fifth step, a kiln heavy oil temperature in the fifth step, a kiln drive current in the fifth step, a kiln exhaust heat temperature in the fifth step, a moisture content of calcium carbonate supplied to the fifth step, a particle size of calcium carbonate generated in the fourth step, a particle size of calcium carbonate supplied to the fifth step, weather during operation, a rainfall amount, a usage amount or addition rate of a white liquor flocculant, a usage amount or intensity of a water-reducing agent, a lime mud filter rotation speed, a number of days of operation, and a dead load amount by an inert alkali.

5. The information processing system according to any one of claims 2 to 4, wherein: the parameter information acquires, as the result parameter, a parameter different from an estimation target, and the result parameter includes a parameter related to one or more selected from a group consisting of a causticization rate in the fourth step, an amount of white liquor produced in the fourth step, an amount of active alkali in a fluid circulating in the pulp manufacturing system, a lime calcination rate in the fifth step, an amount of calcium oxide produced in the fifth step, a pulp yield in the first step, steam intensity or steam usage amount in the pulp manufacturing system, water intensity or water usage amount in the pulp manufacturing system, bleaching chemical intensity or bleaching chemical usage amount, causticizing chemical intensity or usage amount in the fourth step, an amount of grid generated in the fourth step, a fuel amount or intensity used in the fifth step, whiteness of pulp obtained in the first step, and a kappa value of pulp obtained in the first step.

6. The information processing system according to any one of claims 1 to 5, wherein: the third step of the pulp manufacturing system includes: obtaining dregs and the clarified green liquor by treating the green liquor; generating weak liquor from the obtained dregs and supplying the weak liquor to the second step; and supplying the obtained clarified green liquor to the fourth step.

7. The information processing system according to any one of claims 1 to 6, wherein: the event or the index related to the event estimated by the estimation unit is related to an event in a predetermined step selected from among the first step to the fifth step, and the parameter information acquisition unit is configured to acquire a parameter in a step different from the predetermined step among the first step to the fifth step.

8. The information processing system according to any one of claims 1 to 7, wherein: the event or the index related to the event estimated by the estimation unit is related to an event in the fourth step or the fifth step.

9. The information processing system according to any one of claims 1 to 8, 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 event or an index related to the prior measurement result and the two or more parameters.

10. 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 9.

11. 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 9.

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