Information processing system, information processing method, learning system, and learning method

Abstract

An information processing system according to the present disclosure is for a combustion facility, and comprises: an acquisition unit that acquires state quantity information, which is information relating to a state quantity of the combustion facility; and an operation content derivation unit that inputs the state quantity information acquired by the acquisition unit and derives operation content for a supply apparatus that supplies an object to be incinerated to the combustion facility, in accordance with the input state quantity information, using a learning model that has been trained on the basis of a correspondence relationship between the state quantity information, the operation content of the supply apparatus, and a reward according to the state of the combustion facility.

Description

Information processing system, information processing method, learning system, and learning method

[0001] This application claims priority to Japanese Patent Application No. 2023-146237, filed on September 8, 2023, the contents of which are incorporated herein by reference.

[0002] Patent Document 1 discloses an information processing device that acquires process data including at least one of a plurality of combustion process measurement values ​​and a plurality of control values ​​in a combustion facility, acquires or generates image data based on thermal image information captured by an imaging unit provided in the combustion facility for capturing images of the interior of the combustion facility, predicts the amount of steam generated after a predetermined predicted time based on the process data and the image data, and controls a waste incinerator based on the predicted steam generation amount.

[0003] Japanese Patent Application Publication No. 2021-188813

[0004] However, while the information processing device described in Patent Document 1 can obtain a predicted steam generation amount, it can be difficult to determine the correspondence between the predicted steam generation amount and the operation of the combustion equipment that should be performed in accordance with the predicted steam generation amount.

[0005] The present disclosure has been made to solve the above-mentioned problems, and aims to provide an information processing system, an information processing method, a learning system, and a learning method that can derive appropriate operation content related to combustion equipment.

[0006] In order to solve the above problems, the information processing system according to the present disclosure is an information processing system for combustion equipment, and includes: an acquisition unit that acquires state quantity information, which is information relating to the state quantities of the combustion equipment; and an operation content derivation unit that inputs the state quantity information acquired by the acquisition unit and derives the operation content of the supply device according to the input state quantity information using a learning model that has been trained based on the correspondence between the state quantity information, the operation content of the supply device that supplies the material to be incinerated to the combustion equipment, and a reward according to the state of the combustion equipment.

[0007] The information processing method according to the present disclosure is an information processing method related to combustion equipment, and includes acquiring state quantity information, which is information relating to the state quantities of the combustion equipment, inputting the state quantity information using a learning model that has been trained based on the correspondence between the state quantity information, the operation details of a supply device that supplies material to be incinerated to the combustion equipment, and a reward according to the state of the combustion equipment, and deriving the operation details of the supply device according to the input state quantity information.

[0008] The learning system according to the present disclosure is a learning system for combustion equipment, and includes a learning unit that performs reinforcement learning of a learning model based on the correspondence between state quantity information, which is information about the state quantities of the combustion equipment, the operation details of a supply device that supplies materials to be incinerated to the combustion equipment, and a reward according to the state of the combustion equipment.

[0009] The learning method according to the present disclosure is a learning method for combustion equipment, and includes performing reinforcement learning of a learning model based on the correspondence between state quantity information, which is information about the state quantities of the combustion equipment, the operation content of a supply device that supplies materials to be incinerated to the combustion equipment, and a reward according to the state of the combustion equipment.

[0010] According to the information processing system, information processing method, learning system, and learning method of the present disclosure, appropriate operation content related to combustion equipment can be derived.

[0011] FIG. 1 is a schematic configuration diagram showing the entirety of a combustion facility according to an embodiment of the present disclosure. FIG. 2 is a block diagram I showing the functional configuration of a combustion facility according to an embodiment of the present disclosure. FIG. 3 is a diagram schematically showing a change in the configuration of a neural network in reinforcement learning according to an embodiment of the present disclosure. FIG. 4 is a diagram showing an example of operation content linked to an output layer in reinforcement learning according to an embodiment of the present disclosure. FIG. 5 is a diagram showing an example of a reward value related to a change in the configuration of a neural network in reinforcement learning according to an embodiment of the present disclosure. FIG. 6 is a flowchart showing an information processing method according to an embodiment of the present disclosure. FIG. 7 is a flowchart I showing a learning method according to an embodiment of the present disclosure. FIG. 8 is a block diagram II showing the functional configuration of a combustion facility according to an embodiment of the present disclosure. FIG. 9 is a flowchart II showing a learning method according to an embodiment of the present disclosure. FIG. 10 is a hardware configuration diagram showing the configuration of a computer according to an embodiment of the present disclosure.

[0012] A control device according to an embodiment of the present disclosure will be described below with reference to the drawings. In the following description, components having the same or similar functions will be assigned the same reference numerals. Duplicate descriptions of those components may be omitted. In this disclosure, "based on XX" means "based on at least XX" and may include a case where the control device is based on another element in addition to XX. Furthermore, "based on XX" is not limited to the direct use of XX, but may also include a case where the control device is based on XX after calculation or processing. In this disclosure, "XX or YY" is not limited to either XX or YY, but may include both XX and YY. This also applies when there are three or more selective elements. "XX" and "YY" are arbitrary elements (e.g., arbitrary information).

[0013] In this application, "acquire" is not limited to actively acquiring by sending a transmission request, but may also include passively receiving information transmitted from another device. Furthermore, "acquire" is not limited to directly acquiring target information (information to be acquired) from outside, but may also include generating and acquiring target information by performing calculations or processing on information acquired from outside.

[0014] (First embodiment) <1. Overall configuration of incineration facility> Fig. 1 is a schematic diagram showing the overall configuration of a combustion facility SF according to an embodiment. The combustion facility SF is a stoker furnace for incinerating materials G, such as municipal solid waste, industrial waste, or biomass. For ease of explanation, "materials G to be incinerated" will be referred to as "waste G" below. Note that the combustion facility SF is not limited to a stoker furnace and may be another type of incineration facility. In this embodiment, the combustion facility SF includes, for example, a crane 1, an incinerator 2, a heat recovery boiler 3, a cooling tower 4, a dust collector 5, a flue 6, a chimney 7, and an information processing system 100.

[0015] The crane 1 carries the garbage G stored in the garbage pit to a hopper 11 of the incinerator 2 (described later) and dumps it into the hopper 11. The crane 1 includes a gripping unit 1a that grips the garbage G and a weight sensor 1b provided on the gripping unit 1a. The weight sensor 1b is, for example, a load cell. The weight sensor 1b detects the weight of the garbage G gripped by the gripping unit 1a when the garbage G is gripped and lifted by the gripping unit 1a. The detection result of the weight sensor 1b is available for acquisition by the acquisition unit 110 of the information processing system 100.

[0016] The incinerator 2 is a furnace that burns waste G that has been fed into a hopper 11, which will be described later, while transporting it. Exhaust gas is generated in the incinerator 2 as the waste G is burned inside the incinerator 2. The generated exhaust gas is sent to a heat recovery boiler 3 provided on top of the incinerator 2. The heat recovery boiler 3 exchanges heat between the exhaust gas generated in the incinerator 2 and water, thereby heating the water and generating steam.

[0017] The exhaust gas that has passed through the heat recovery boiler 3 is cooled in a cooling tower 4 and then sent to a dust collector 5. After soot and dust are removed from the exhaust gas in the dust collector 5, the exhaust gas is discharged into the atmosphere through a flue 6 and a chimney 7. A gas concentration sensor 6a is provided in the flue 6. The gas concentration sensor 6a detects the concentrations of various gases (e.g., CO concentration) contained in the exhaust gas flowing through the flue 6. Note that the detection results of the gas concentration sensor 6a may include one or more of an oxygen concentration, a NOx concentration, and a SOx concentration instead of or in addition to the CO concentration. The detection results of the gas concentration sensor 6a are transmitted to an acquisition unit 110 of the information processing system 100.

[0018] 2. Incinerator Next, a detailed description will be given of the incinerator 2. The incinerator 2 has, for example, a supply mechanism 10, a furnace body 20, a stoker 30, a wind box 41, a discharge chute 42, a furnace 43, and a blower mechanism 50.

[0019] 2.1 Supply Mechanism The supply mechanism 10 is a mechanism that temporarily stores the waste G transported by the crane 1 and sequentially supplies the waste G to the processing space V of the furnace body 20, which will be described later. The supply mechanism 10 includes, for example, a hopper 11, a feeder 12, an extrusion device 13, an object measuring device 14, and a moisture measuring device 15.

[0020] The hopper 11 is a storage section provided to supply waste G into the interior of the furnace body 20. Waste G transported by the crane 1 is fed into the hopper 11. The hopper 11 has an inlet section 11a and an outlet section 11b. The inlet section 11a is an inlet section through which waste G is fed from the outside. The inlet section 11a extends, for example, vertically. The waste G fed into the inlet section 11a moves downward due to gravity. The outlet section 11b is provided below the inlet section 11a. The outlet section 11b is an outlet section that guides the waste G supplied from the inlet section 11a toward the treatment space V within the furnace body 20, which will be described later. The outlet section 11b extends, for example, horizontally.

[0021] The feeder 12 is a device that supplies waste G to the furnace body 20. The feeder 12 is an example of a "feeding device." The following describes a reciprocating feeder 12 that supplies waste G to the furnace body 20 by performing a reciprocating motion. Note that the feeder 12 is not limited to a reciprocating type, and may also be a rotary feeder 12 that supplies waste G by performing a rotary motion.

[0022] The feeder 12 is provided at the outlet 11b of the hopper 11. The feeder 12 is plate-shaped and fits along the bottom of the outlet 11b of the hopper 11. The feeder 12 is capable of reciprocating in a direction from the outlet 11b of the hopper 11 toward the treatment space V of the furnace body 20. The feeder 12 is driven by a pusher device 13 and pushes out waste G accumulated inside the hopper 11 (e.g., at the outlet 11b of the hopper 11) toward the treatment space V of the furnace body 20. The stroke of the feeder 12, the movement speed of the feeder 12, and the stop time of the feeder 12, which relate to the supply amount of waste G, are controlled by the pusher device 13.

[0023] The object measuring instrument 14 is a measuring instrument that detects the height of the waste G thrown into the hopper 11 by the crane 1. The object measuring instrument 14 is, for example, a LiDAR (Light Detection and Ranging) instrument. The object measuring instrument 14 is provided, for example, at the entrance 11a of the hopper 11, and detects the height of the waste G passing through the entrance 11a of the hopper 11. Note that the object measuring instrument 14 may directly detect the volume of the waste G by three-dimensional measurement instead of the height of the waste G. The detection result of the object measuring instrument 14 is transmitted to the acquisition unit 110 of the information processing system 100.

[0024] The moisture meter 15 is a measuring instrument that detects values ​​related to the moisture content (e.g., moisture percentage or moisture amount) of the waste G fed into the hopper 11. In this embodiment, the moisture meter 15 has an irradiation unit and a detection unit provided in the hopper 11, and an analysis unit. The irradiation unit irradiates the waste G piled up in the hopper 11 with electromagnetic waves in a predetermined frequency band. The detection unit receives the electromagnetic waves irradiated from the irradiation unit and transmitted through or reflected by the waste G. The analysis unit pre-stores correlation information that indicates the relationship between, for example, changes in the characteristics of the electromagnetic waves (e.g., changes in amplitude or phase) and the moisture content. The analysis unit detects the moisture content of the waste G based on the changes in the characteristics of the electromagnetic waves between the irradiation unit and the detection unit and the correlation information.

[0025] In this embodiment, the irradiation unit and detection unit of the moisture meter 15 are provided slightly above the feeder 12 and detect the moisture content of the waste G accumulated on the upper surface of the feeder 12. The detection result of the moisture meter 15 is available for acquisition by the acquisition unit 110 of the information processing system 100.

[0026] 2.2 Furnace Body The furnace body 20 is located adjacent to the hopper 11 and is a facility for burning waste G while transporting it. Hereinafter, the direction in which waste G is transported in the combustion facility SF is referred to as the "transport direction D." The furnace body 20 has, from upstream to downstream in the transport direction D, a drying stage 20a, a combustion stage 20b, and a post-combustion stage 20c, in this order. The drying stage 20a is located upstream of the combustion stage 20b and the post-combustion stage 20c, and is a region in which the waste G supplied from the hopper 11 is dried prior to combustion on the stoker 30. The combustion stage 20b and the post-combustion stage 20c are regions in which the dried waste G that has passed through the drying stage 20a is combusted on the stoker 30. In the combustion stage 20b, diffusion combustion occurs due to pyrolysis gas generated from the waste G, generating a luminous flame F. In the post-combustion stage 20c, fixed carbon combustion occurs after diffusion combustion of the waste G, so a luminous flame F is not generated. The combustion stage 20b and the post-combustion stage 20c are an example of a treatment space V in which waste G is combusted. The drying stage 20a is an example of a region upstream of the treatment space V in the conveying direction D.

[0027] In this embodiment, the furnace body 20 has a visible light camera 21 and an infrared camera 22. The visible light camera 21 and the infrared camera 22 are arranged downstream of the processing space V in the transport direction D and capture images from the downstream side to the upstream side in the transport direction D. In this embodiment, the visible light camera 21 and the infrared camera 22 are provided at the downstream end (hereinafter referred to as the "furnace foot") of the furnace body 20 in the transport direction D. For example, the visible light camera 21 and the infrared camera 22 capture images from the downstream side to the upstream side in the transport direction D through a window provided in the furnace foot of the furnace body 20. For example, the visible light camera 21 and the infrared camera 22 are arranged adjacent to each other vertically or horizontally.

[0028] The visible light camera 21 captures an image of the luminous flame F from the bottom of the furnace body 20. The image capture results of the visible light camera 21 are available for acquisition by the acquisition unit 110 of the information processing system 100.

[0029] The infrared camera 22 captures an image of the waste G accumulated in the drying stage 20a of the furnace body 20 (i.e., upstream of the treatment space V) through the luminous flame F from the end of the furnace body 20. In this embodiment, the infrared camera 22 also captures an image of the outlet portion 11b of the hopper 11 through the luminous flame F from the end of the furnace body 20. For example, the infrared camera 22 captures an image including the waste G accumulated on the feeder 12 at the outlet portion 11b of the hopper 11 (an image showing the accumulation state of the waste G). The image capture results of the infrared camera 22 are available for acquisition by the acquisition unit 110 of the information processing system 100.

[0030] In this embodiment, a single infrared camera 22 captures an image including both the drying stage 20a of the furnace body 20 and the outlet portion 11b of the hopper 11 (e.g., garbage G accumulated on the feeder 12). Alternatively, the furnace body 20 may be provided with a first infrared camera that captures an image of the drying stage 20a of the furnace body 20 and a second infrared camera that captures an image of the outlet portion 11b of the hopper 11 (e.g., garbage G accumulated on the feeder 12). Furthermore, the infrared camera 22 may be provided at a different position instead of at the end of the furnace body 20.

[0031] 2.3 Stoker The stoker 30 includes multiple grates 31 and a grate drive device 32 (not shown). The multiple grates 31 form a stoker surface 30a, which is the bottom surface of the furnace body 20 (e.g., the bottom surface of the treatment space V). The supply mechanism 10 supplies waste G in layers to the stoker surface 30a. The stoker surface 30a is provided across the drying stage 20a, the combustion stage 20b, and the post-combustion stage 20c. The multiple grates 31 include a fixed grate and a movable grate. The fixed grate is fixed to the upper surface of the wind box 41 (described later). The movable grate reciprocates at a constant speed along the transport direction D, stirring and mixing the waste G on the movable grate and the fixed grate (on the stoker surface 30a) while transporting it downstream.

[0032] 2.4 Wind box, discharge chute, and furnace The wind box 41 is provided below the stoker 30 and supplies combustion air to the inside of the furnace body 20 through the stoker 30. A plurality of wind boxes 41 are arranged in the conveying direction D. Each wind box 41 has a wind box pressure sensor 41a. The wind box pressure sensor 41a detects the pressure inside the wind box 41. The pressure inside the wind box 41 corresponds to the pressure of the combustion air supplied to the inside of the furnace body 20 through the primary air line 52 described below. The detection result of the wind box pressure sensor 41a can be acquired by the acquisition unit 110 of the information processing system 100.

[0033] The discharge chute 42 is a device that drops the waste G that has been burned and turned into ash into an ash extrusion device located below the furnace body 20. The discharge chute 42 is provided at the end of the furnace body 20.

[0034] The furnace 43 extends upward from the top of the furnace body 20. Exhaust gas generated by burning the waste G in the treatment space V is sent to the heat recovery boiler 3 through the furnace 43.

[0035] 2.5 Blower Mechanism The blower mechanism 50 supplies air (e.g., combustion air) to the inside of the furnace body 20. The blower mechanism 50 includes, for example, a blower 51, a primary air line 52, an air preheater 53, a secondary air line 54, a damper 55, and an air flow sensor 56.

[0036] The blower 51 is a forced draft blower that pressurizes and sends air (e.g., combustion air) into the furnace body 20. The blower 51 includes, for example, a first blower 51A and a second blower 51B. The first blower 51A pressurizes and sends combustion air into the furnace body 20 (e.g., the treatment space V) through the primary air line 52 and the air box 41. The second blower 51B pressurizes and sends combustion air into the furnace 43 through the secondary air line 54.

[0037] The primary air line 52 connects the first blower 51A and the air box 41. One or more (e.g., multiple) primary air dampers 55A are provided in the primary air line 52. The primary air dampers 55A change the flow rate of the combustion air flowing through the primary air line 52 depending on the opening degree of the primary air dampers 55A.

[0038] The air preheater 53 is a heat exchanger that preheats the air compressed and sent from the first blower 51 A. For example, the air preheater 53 is provided midway along the primary air line 52.

[0039] The secondary air line 54 connects the second blower 51B and the furnace 43. The secondary air supplied into the furnace 43 flows from above the stoker 30 toward the waste G. One or more (e.g., multiple) secondary air dampers 55B are provided in the secondary air line 54. The secondary air dampers 55B change the flow rate of the combustion air flowing through the secondary air line 54 depending on the opening degree of the secondary air dampers 55B. For ease of explanation, the primary air damper 55A and the secondary air damper 55B will hereinafter be collectively referred to as "damper 55."

[0040] The air flow sensor 56 detects the flow rate of air (e.g., combustion air) supplied into the furnace body 20. The air flow sensor 56 includes, for example, a first air flow sensor 56A and a second air flow sensor 56B. The first air flow sensor 56A is provided in the primary air line 52 and detects the flow rate of air supplied through the primary air line 52. The second air flow sensor 56B is provided in the secondary air line 54 and detects the flow rate of air supplied through the secondary air line 54. In the following description, the "detection result of the air flow sensor 56" includes, for example, the detection result of the first air flow sensor 56A and the detection result of the second air flow sensor 56B.

[0041] 3. Waste Heat Recovery Steam Generator Next, a description will be given of the waste heat recovery steam generator 3. The waste heat recovery steam generator 3 includes, for example, a boiler body 61, a pipe 62, a radiation temperature sensor (infrared temperature sensor) 63, an in-furnace pressure sensor 64, a feedwater flow rate sensor 65, and a superheater / desuperheater flow rate sensor (steam amount sensor) 66.

[0042] The boiler body 61 is connected to the furnace 43 of the incinerator 2. Exhaust gas generated in the incinerator 2 flows into the boiler body 61. A radiation temperature sensor 63 and an in-furnace pressure sensor 64 are provided in the boiler body 61. The radiation temperature sensor 63 detects the temperature inside the boiler body 61. The in-furnace pressure sensor 64 detects the pressure inside the boiler body 61. The detection result of the radiation temperature sensor 63 is transmitted to the acquisition unit 110 of the information processing system 100. The detection result of the in-furnace pressure sensor 64 is available to be acquired by the acquisition unit 110 of the information processing system 100.

[0043] The pipe 62 extends inside the boiler body 61. The pipe 62 is provided with a plurality of superheaters and a plurality of attemperators. Water is supplied to the inlet of the pipe 62 from a water supply unit. At least a portion of the water flowing through the pipe 62 is heated by heat exchange inside the boiler body 61, and becomes main steam, which flows toward external equipment (e.g., a turbine). Note that the term "steam volume" in this disclosure refers to the volume of main steam flowing from the pipe 62 toward external equipment (e.g., a turbine).

[0044] The feedwater flow rate sensor 65 is provided at the inlet of the pipe 62 and detects the flow rate of water supplied to the pipe 62. The superheater attemperator flow rate sensor 66 is provided midway through the pipe 62 and detects the flow rate of a fluid (e.g., steam) flowing through the pipe 62. For example, the superheater attemperator flow rate sensor 66 includes a first superheater attemperator flow rate sensor 66A that detects the flow rate of the fluid passing through the primary attemperator (primary superheater attemperator flow rate) and a second superheater attemperator flow rate sensor 66B that detects the flow rate of the fluid passing through the secondary attemperator (secondary superheater attemperator flow rate). In the following description, the "detection result of the superheater attemperator flow rate sensor 66" includes, for example, the detection result of the first superheater attemperator flow rate sensor 66A and the detection result of the second superheater attemperator flow rate sensor 66B. The detection results of the feedwater flow rate sensor 65 and the superheater desuperheater flow rate sensor 66 are transmitted to the acquisition unit 110 of the information processing system 100 .

[0045] 4. Information Processing System Next, the information processing system 100 will be described. FIG. 2 is a block diagram showing the functional configuration of the combustion equipment SF according to the embodiment. The information processing system 100 comprehensively controls the combustion equipment SF. For example, the information processing system 100 controls the combustion of waste G in the treatment space V of the furnace body 20. In this embodiment, the information processing system 100 includes, for example, an acquisition unit 110, a storage unit 120, an operation content derivation unit 130, a control unit 140, and a learning system 160. The information processing system 100 may also include a notification unit 150. Alternatively, the information processing system 100 may include the notification unit 150 instead of the control unit 140. In the present disclosure, the device to be controlled by the information processing system 100 is the extrusion device 13 (feeder 12) described above. The operation of each unit in the information processing system 100 described below corresponds to part of the information processing method and learning method of the present disclosure.

[0046] 4.1 Acquisition Unit The acquisition unit 110 acquires the detection results detected by the various sensors included in the combustion facility SF. For example, the acquisition unit 110 acquires the detection results (waste weight) of the weight sensor 1b, the detection results (waste height) of the object measuring device 14, the detection results (furnace temperature) of the radiation temperature sensor 63, the detection results (superheater desuperheater flow rate) of the superheater desuperheater flow rate sensor 66, and the detection results (CO concentration, etc.) of the gas concentration sensor 6a. The detection results (superheater desuperheater flow rate) of the superheater desuperheater flow rate sensor 66 are an example of the "steam amount."

[0047] In addition, the detection results of the moisture measuring device 15 (garbage moisture detection results), the imaging results of the visible light camera 21 (combustion flame image), the imaging results of the infrared camera 22 (garbage layer image), the detection results of the wind box pressure sensor 41a (wind box pressure), the detection results of the air flow sensor 56 (forced air flow rate), the detection results of the furnace pressure sensor 64 (furnace pressure), and the detection results of the water supply flow sensor 65 (water supply flow rate) are available for acquisition by the acquisition unit 110.

[0048] These pieces of information acquired or obtainable by the acquisition unit 110 are values ​​related to the state quantities of the combustion equipment SF. In the present disclosure, the information processing system 100 controls the extrusion device 13 (feeder 12) using these values ​​related to the state quantities of the combustion equipment SF. The acquisition unit 110 writes and stores the acquired state quantities in the storage unit 120 as a state quantity history (referred to as state quantity history 122).

[0049] The acquisition unit 110 further acquires state quantity information by statistically processing these values ​​related to the state quantities of the combustion equipment SF over a predetermined length of time. The "state quantity information" in the present disclosure is a representative value obtained by statistically processing these values ​​related to the state quantities of the combustion equipment SF over a predetermined length of time. For example, the state quantity information is the average value of the values ​​related to the state quantities of the combustion equipment SF over the predetermined length of time. Note that the state quantity information is not limited to the average value, and may be a median or a most frequently occurring value.

[0050] The predetermined time length is a time length that includes the time difference between the time when the waste G is supplied by the extrusion device 13 (feeder 12) and the time when a fluctuation occurs in the state quantity (e.g., steam volume) of the combustion equipment SF in response to the supply. For example, the predetermined time length is a time length that includes the time difference between the time when the waste G is supplied by the extrusion device 13 (feeder 12) and the time when a fluctuation in the state quantity (e.g., steam volume) of the combustion equipment SF in response to the supply has the most significant correlation. From another perspective, the predetermined time length is a time length that is longer than the time it takes for the feeder 12 to make one round trip. The predetermined time length is, for example, 20 minutes.

[0051] The acquisition unit 110 also acquires from the memory unit 120 the operation content derived by the operation content derivation unit 130 (referred to as derived operation content 121), a learning model M described below, and a control target value SV of the steam volume.

[0052] <4.2 Storage Unit> The storage unit 120 is written and stored with a derivation operation content 121, a learning model M, a control target value SV of the steam amount, and a state quantity history 122.

[0053] 4.3 Operation Content Derivation Unit The operation content derivation unit 130 uses a learning model M to input the state quantity information acquired by the acquisition unit 110 into the learning model M and derives the operation content of the extrusion device 13 (feeder 12) according to the input state quantity information. In the present disclosure, the learning model installed in the operation content derivation unit 130 is referred to as the learning model M. The learning model M performs learning based on a correspondence between the state quantity information acquired by the acquisition unit 110, the operation content of the extrusion device 13 (the operation content of the feeder 12) corresponding to the state quantity information acquired, and a reward according to the state (e.g., steam volume) of the combustion equipment SF resulting from the operation content of the extrusion device 13 (the operation content of the feeder 12). The state of the combustion equipment SF resulting from the operation content of the extrusion device 13 (the operation content of the feeder 12) refers to the state (e.g., steam volume) of the combustion equipment SF a predetermined time (e.g., 20 minutes) after the operation of the operation content of the extrusion device 13.

[0054] Thereafter, the operation content derivation unit 130 outputs the derived operation content to the control unit 140. The operation content derivation unit 130 may also output the derived operation content to the notification unit 150. Note that the operation content derivation unit 130 updates the learning model M to the learning model M stored in the storage unit 120 at a predetermined timing.

[0055] 4.4 Control Unit and Notification Unit The control unit 140 controls combustion in the treatment space V based on the derived operation content output by the operation content derivation unit 130. Specifically, the control unit 140 outputs a control instruction value to the extrusion device 13 (feeder 12) based on the derived operation content output by the operation content derivation unit 130, and controls the feeder 12. The control unit 140 increases the supply amount of waste G when promoting combustion. On the other hand, the control unit 140 decreases the supply amount of waste G when suppressing combustion.

[0056] For example, if the information processing system 100 is equipped with a notification unit 150, the notification unit 150 notifies an operator (an operator of the combustion facility SF) of the derived operation content output by the operation content derivation unit 130. The notification unit 150 is, for example, a display device having a display screen that displays the derived operation content output by the operation content derivation unit 130. The operator may manually input a control instruction value to the extrusion device 13 (feeder 12) based on the notified derived operation content, thereby controlling the feeder 12.

[0057] 4.5 Learning System The learning system 160 includes a learning unit 161. The learning unit 161 of the learning system 160 performs reinforcement learning of the learning model M stored in the storage unit 120 after acquiring the learning model M via the acquisition unit 110. For example, the learning unit 161 performs reinforcement learning of the learning model M based on the correspondence between state quantity information, the operation content of the extrusion device 13 (feeder 12), and the reward.

[0058] 5. Overview of Learning Model Processing Here, an overview of the learning model M in the present disclosure is shown using FIGS. 3 to 5. The operation content derivation unit 130 includes the learning model M, and state quantity information acquired by the acquisition unit 110 is input to this learning model M. The learning model M is a forward propagation neural network as shown in FIG. 3. When state quantity information after statistical processing is input to this learning model M as an input layer, the learning model M outputs an expected value for each of multiple operation content candidates for the extrusion device 13 (feeder 12) to an output layer. The input state quantities are values ​​related to the state quantities of the combustion equipment SF that are acquired or can be acquired by the acquisition unit 110. Examples of input state quantities include the lower heating value (LHV), the concentration of emissions (CO concentration), the supply amount of waste G, and the steam amount. The state quantities input to the learning model M are two or more of these. In this embodiment, the state quantities input are representative values ​​(e.g., average values) for the above-mentioned specified time length for each of the lower heating value (LHV), concentration of exhaust substances (CO concentration), supply amount of waste G, and steam amount.

[0059] In addition, the input state quantity information may be, instead of some or all of the above-mentioned state quantities, representative values ​​(or representative images) at the above-mentioned specified time of each of the detection results of the moisture meter 15 (garbage moisture detection results), the imaging results of the visible light camera 21 (combustion flame image), the imaging results of the infrared camera 22 (garbage layer image), the detection results of the wind box pressure sensor 41a (wind box pressure), the detection results of the air flow sensor 56 (forced air flow rate), the detection results of the furnace pressure sensor 64 (furnace pressure), and the detection results of the water supply flow sensor 65 (water supply flow rate).

[0060] Among the expected values ​​output to the output layer of the learning model M (expected values ​​for each of the multiple operation content candidates for the extrusion device 13 (feeder 12)), the operation content candidate corresponding to the largest expected value is output by the operation content derivation unit 130 to the control unit 140 or the notification unit 150 as the derived operation content.

[0061] The acquiring unit 110 may perform the following estimation separately from the statistical processing. The estimation performed by the acquiring unit 110 is treated as a state quantity. That is, in this embodiment, values ​​related to the state quantities for which the following estimation is performed are input to the learning model M.

[0062] The acquisition unit 110 acquires the detection result (dust weight) of the weight sensor 1b and the detection result (dust height) of the object measuring device 14. The acquisition unit 110 estimates the supply amount of dust G as a state quantity from the dust weight, the dust height, and the operation details of the extrusion device 13 (supply device). However, the method for estimating the supply amount of dust G is not limited to the above.

[0063] The acquisition unit 110 acquires the detection result (waste weight) of the weight sensor 1b, the detection result (waste height) of the object measuring device 14, and the detection result (furnace temperature) of the radiation temperature sensor 63. The acquisition unit 110 determines the furnace temperature according to the supply amount (estimated amount) of waste G from the detection result and the operation details of the extrusion device 13 (supply device), and estimates the LHV as a state quantity by back-calculating the combustion data based on these.

[0064] 4, the operation of the extrusion device 13 (feeding device) is determined by a combination of two or more of the operation amount of a first operation element of the feeder 12, the operation amount of a second operation element of the feeder 12 that is different from the first operation element, and the stop time of the feeder 12. For example, if the feeder 12 is a reciprocating feeder, the first operation element is the stroke (feeder stroke ST) of the feeder 12. The second operation element is the speed (feeder speed SD) of the feeder 12.

[0065] In the two-dimensional map of operation contents shown in FIG. 4 , the first operation element is the feeder stroke ST, and the second operation element is the feeder speed SD. In FIG. 4 , there are 40 combinations of the first operation element and the second operation element, including five patterns of the feeder speed SD and eight patterns of the feeder stroke ST. These 40 patterns represent multiple operation content candidates for the extrusion device 13 (feeder 12) set in the output layer of the learning model M. That is, 40 patterns of operation content candidates are discretely set in the output layer of the learning model M. Among the expected values ​​output to the output layer of the learning model M, the operation content corresponding to the largest expected value is the combination of feeder stroke ST: α and feeder speed SD: β among the 40 patterns shown in FIG. 4 . Note that the operation contents are not limited to this two-dimensional map; a three-dimensional map in which a stop time is added to the combination of the first operation element and the second operation element may also be used.

[0066] After the feeder 12 is controlled based on the derived operation content thus output, the learning unit 161 of the learning system 160 performs reinforcement learning of the learning model M. After the predetermined time, when a fluctuation occurs in the state quantity of the combustion equipment SF in response to the supply of waste G by the feeder 12, the learning unit 161 receives state quantity information (also referred to as detected values) after the derived operation content is implemented and a control target value from the acquisition unit 110. The control target value is a target value corresponding to the detected value of at least one indicator included in the state quantity information. The reward is derived based on the magnitude of the difference between this detected value and the control target value. For example, the learning unit 161 uses the detected value PV of the steam volume, which is one of the indicators included in the state quantity information (detected values) after the operation content is implemented, and the control target value SV of the steam volume.

[0067] As shown in Fig. 5, the learning unit 161 derives a reward based on the magnitude of the difference between the detected steam volume value PV and the control target steam volume value SV. The reward is divided into stages. For example, the reward may be set in advance for the learning model M. Alternatively, the reward may be stored in the storage unit 120, acquired by the acquisition unit 110, and then input to the learning model M by the learning unit 161.

[0068] In step ST24, which will be described later, the learning unit 161 changes the weighting related to the expected value based on the reward. The learning unit 161 changes the weighting related to the expected value by updating the connection weight between each node from the input layer to the output layer. By repeating reinforcement learning many times, the learning model M learns operation content that increases the reward relative to the state quantity. The learning unit 161 performs reinforcement learning on the learning model M at a predetermined timing.

[0069] <6. Processing Flow> Next, a description will be given of an example of the processing flow in the above-described information processing system 100. However, the order of the processing described below is not limited to the following example, and may be changed as appropriate.

[0070] 6.1 Information Processing Method First, an information processing method related to the combustion facility SF will be described. The information processing method in this embodiment is performed according to the flowchart shown in FIG. 6. First, the acquisition unit 110 of the information processing system 100 acquires state quantities from each detection result (step ST11). Next, the acquisition unit 110 of the information processing system 100 writes the acquired state quantities as the state quantity history 122 in the storage unit 120 (step ST12). Next, the acquisition unit 110 of the information processing system 100 performs statistical processing on the state quantities acquired in step ST11 (step ST13). As a result, the acquisition unit 110 acquires state quantity information, which is information related to the state quantities of the combustion facility SF. Next, the operation content derivation unit 130 of the information processing system 100 receives the state quantity information obtained by statistical processing of the state quantities from the acquisition unit 110 and inputs the state quantity information into the learning model M (step ST14). Next, the operation content derivation unit 130 of the information processing system 100 derives the operation content of the extrusion device 13 (feeder 12) according to the input state quantity information (step ST15), and outputs the derived operation content to the control unit 140 or the notification unit 150 (step ST16).

[0071] <6.2 Learning Method> Next, a learning method for the incineration facility SF will be described. The learning method in this embodiment is carried out according to the flowchart shown in Fig. 7. The learning method described below is also executed in parallel during the operation of the incineration facility SF.

[0072] First, the acquisition unit 110 of the information processing system 100 acquires state quantity information (detected values) after the operation content is performed based on the state quantity history 122 of the storage unit 120, and also acquires the operation content of the extrusion device 13 (feeder 12) from the derived operation content 121 of the storage unit 120 (step ST21). At this time, the acquisition unit 110 performs statistical processing on the state quantities after the operation content is performed in advance (step ST21a). Next, the acquisition unit 110 of the information processing system 100 acquires the control target value SV from the storage unit 120 (step ST22).

[0073] Next, the learning unit 161 of the learning system 160 of the information processing system 100 receives the state quantity information (detected value PV) and the control target value SV after the operation content is performed from the acquisition unit 110, and derives a reward from the detected value PV and the control target value SV (step ST23). Next, the learning unit 161 of the learning system 160 changes the weighting of the expected value based on the operation content of the extrusion device 13 (feeder 12) from the derived operation content 121 in the storage unit 120 and the reward corresponding to the operation content (step ST24).

[0074] As a result, the learning unit 161 performs reinforcement learning of the learning model based on the correspondence between the state quantity information, which is information about the state quantities of the combustion facility SF, the operation details of the supply device that supplies the material to be incinerated to the combustion facility SF, and the reward according to the state of the combustion facility SF. Next, the learning unit 161 of the learning system 160 writes the updated learning model M with the changed weights into the memory unit 120 (step ST25).

[0075] 7. Effects and Effects According to the information processing system 100 and information processing method configured as described above, a learning model M can be used that learns operation details that increase rewards for the state quantities of the combustion equipment SF. As a result, the information processing system 100 and information processing method of this embodiment can derive appropriate operation details related to the combustion equipment SF. As a result, fluctuations in the amount of steam from the combustion equipment SF can be suppressed.

[0076] Furthermore, with the learning system 160 and learning method configured as described above, the learning model M can learn operation details that increase the reward for the state quantity of the combustion equipment SF. As a result, the learning system 160 and learning method of this embodiment can derive appropriate operation details related to the combustion equipment SF. As a result, the amount of fluctuation in the steam volume of the combustion equipment SF can be suppressed.

[0077] Furthermore, the amount of steam can fluctuate significantly depending on the supply status of the waste G and its properties. The combustion properties (such as LHV) and powder properties (such as adhesion amount) of the waste G are strongly affected by the season and climate, so the sensitivity of controlled parameters such as the amount of steam can also change from moment to moment. For example, when controlling the supply device based on a predicted amount of steam, it is necessary to build a control logic that corresponds to the predicted value of the amount of steam. If rule-based logic is used, the number of branching processes corresponding to the predicted value of the amount of steam becomes enormous, making it difficult to take into account all seasonal fluctuations.

[0078] The information processing system 100 and information processing method configured as described above can use a learning model M that learns operation patterns that result in high rewards for the state quantities of the combustion equipment SF. As a result, the information processing system 100 and information processing method of this embodiment can automatically construct logic that derives appropriate operation content related to the combustion equipment SF, taking seasonal fluctuations into account. As a result, fluctuations in the steam volume of the combustion equipment SF can be suppressed.

[0079] Furthermore, according to the learning system 160 and learning method configured as described above, the learning model M can learn operation patterns that provide a high reward for the state quantities of the combustion equipment SF. As a result, the learning system 160 and learning method of this embodiment can automatically construct logic that derives appropriate operation content related to the combustion equipment SF, taking seasonal fluctuations into account. As a result, fluctuations in the steam volume of the combustion equipment SF can be suppressed.

[0080] Second Embodiment 8. Information Processing System An information processing system 100B according to the present disclosure will now be described as a second embodiment with reference to Fig. 8. Components common to the first embodiment are denoted by the same reference numerals, and detailed description thereof will be omitted.

[0081] In this embodiment, the information processing system 100B includes, for example, an acquisition unit 110, a storage unit 120B, an operation content derivation unit 130, a control unit 140, and a learning system 160B. The information processing system 100B may also include a notification unit 150. Alternatively, the information processing system 100B may include the notification unit 150 instead of the control unit 140. In the present disclosure, the device to be controlled by the information processing system 100 is the above-described extrusion device 13 (supply device).

[0082] Furthermore, in this embodiment, the learning model M is generated based on an initial model generated by pre-learning based on the correspondence between state quantity information and the operation details of the extrusion device 13 (feeder 12) obtained from the operator's past operation history. Based on this initial model, pre-learning is performed based on the correspondence between the state quantity information acquired by the acquisition unit 110, the operation details of the extrusion device 13 (feeder 12), and the reward corresponding to the operator's operation history. Then, reinforcement learning is performed based on the correspondence between the state quantity information acquired by the acquisition unit 110 described in the first embodiment, the operation details of the feed device, and the reward corresponding to the state of the combustion equipment SF. The operation of each unit added to the information processing system 100B described below corresponds to part of the information processing method and learning method disclosed herein.

[0083] In the information processing system 100B, the storage unit 120B newly stores an operation history (referred to as an operator operation history 123) of the extrusion device 13 (feeder 12) by the operator.

[0084] In the information processing system 100B, the learning unit 161B generates an initial model of the learning model M by performing reinforcement learning as pre-learning in addition to the reinforcement learning described in the first embodiment. In this embodiment, the information processing system 100B is provided with a generation unit 162 that adds several functions to the learning system 160B. The generation unit 162 generates an initial model of the learning model M by pre-learning based on the correspondence between the state quantity information and the operation content of the extrusion device 13 (feeder 12) obtained from the operation history of the operator.

[0085] The learning method in this embodiment is carried out according to the flowchart shown in Fig. 9. The learning method described below is also executed in parallel during the operation of the incineration facility SF.

[0086] First, the generation unit 162 of the information processing system 100B sets an initial model before learning (step ST20B). Using this initial model as a learning model M, the operation content derivation unit 130 of the information processing system 100B receives state quantity information obtained by statistically processing the state quantities from the acquisition unit 110 and inputs the state quantity information into the learning model M. Thereafter, the operation content derivation unit 130 derives the operation content of the extrusion device 13 (feeder 12) according to the input state quantity information (step ST21B).

[0087] Next, the acquisition unit 110 of the information processing system 100B acquires the operator's operation history (operator operation history 123) from the storage unit 120 (step ST22B). Next, the learning unit 161B of the learning system 160B of the information processing system 100B pre-trains the learning model M based on a correspondence between past state quantity information, the operation content of the extrusion device 13 (feeder 12) derived using the learning model M corresponding to the state quantity information acquired, and a reward based on the operator's operation actually performed when the state quantity information was acquired. Specifically, in the pre-training, the learning unit 161B derives a reward based on the degree of agreement between the operation content of the extrusion device 13 (feeder 12) derived using the learning model M and the operator operation history 123 (step ST23B).

[0088] Note that, as the pre-learning of the learning model M, reinforcement learning may be performed based on a correspondence between previously acquired state quantity information, the operator's operation content included in the operator's operation history 123 corresponding to the time when the state quantity information was acquired, and a reward corresponding to the state of the combustion equipment SF (e.g., steam volume) resulting from the operator's operation content, instead of / in addition to the degree of agreement between the operation content of the extrusion device 13 derived using the learning model M and the operator's operation history 123. For example, in this embodiment, the reward in the pre-learning is derived by reflecting the operator's operation content included in the operator's operation history 123 and the magnitude of the difference between the detection value and the control target value of at least one index included in the state quantity of the combustion equipment SF according to the operator's operation content.

[0089] Next, the learning unit 161B of the learning system 160B changes the weighting of the expected value based on the reward (step ST24). As a result, the learning unit 161 performs reinforcement learning as pre-learning based on the correspondence between the state quantity information acquired by the acquisition unit 110, the operation content of the extrusion device 13 (feeder 12), and the reward according to the operator's operation history, using this initial model as a basis. Next, the learning unit 161B of the learning system 160B writes the updated learning model with the changed weighting into the storage unit 120 (step ST25).

[0090] The learning unit 161B of the learning system 160B performs reinforcement learning as pre-learning of an initial model based on the operation details of the extrusion device 13 (feeder 12) derived using the learning model M and the reward derived from the operator operation history 123, and then performs reinforcement learning similar to that of the first embodiment. That is, reinforcement learning of the learning model M is performed based on the correspondence between the state quantity information acquired by the acquisition unit 110, the operation details of the extrusion device 13 (operation details of the feeder 12) corresponding to the state when the state quantity information is acquired, and the reward corresponding to the state of the combustion equipment SF (e.g., steam volume) resulting from the operation details of the extrusion device 13 (operation details of the feeder 12). As a result, the learning model M is obtained based on the initial model. Alternatively, the learning unit 161B may perform reinforcement learning on the initial model by appropriately selecting between reinforcement learning as pre-learning and the reinforcement learning of the first embodiment to obtain a learning model.

[0091] 9. Effects and Effects According to the information processing system 100B and information processing method configured as described above, a learning model M can be used that learns operation patterns that increase rewards for state quantities of the combustion equipment SF. As a result, the information processing system 100B and information processing method of this embodiment can derive appropriate operation content related to the combustion equipment SF. As a result, it becomes easier to maintain the combustion equipment SF in a desired state.

[0092] Furthermore, with the learning system 160B and learning method configured as described above, the learning model can learn operation patterns that provide a high reward for the state quantities of the combustion equipment SF. As a result, the learning system 160B and learning method of this embodiment can derive appropriate operation content related to the combustion equipment SF. As a result, fluctuations in the amount of steam produced by the combustion equipment SF can be suppressed.

[0093] Furthermore, according to the information processing system 100B and information processing method configured as described above, learning is performed based on the degree of agreement with the operator's operation history 123, so logic can be automatically constructed that allows for operation patterns similar to those of an operator who controls the supply device based on state quantity information. This allows for operation patterns that take into account cooperation with other existing information processing systems, enabling stable control of the extrusion device 13 (feeder 12) when the information processing system 100B of the present disclosure is introduced. Therefore, the information processing system 100B and information processing method of this embodiment can derive appropriate operation content related to the combustion equipment SF while reflecting the operator's operation patterns.

[0094] Furthermore, the learning system 160B and learning method configured as described above learns based on the degree of agreement with the operator's operation history, making it possible to automatically construct logic that allows the operator controlling the extrusion device 13 (feeder 12) to adopt a similar operation pattern to that of the operator controlling the extrusion device 13 (feeder 12) based on state quantity information. This allows for the construction of an operation pattern that takes into account cooperation with other existing information processing systems, enabling stable control of the feed device when the information processing system 100B of the present disclosure is introduced. Therefore, the learning system 160B and learning method of this embodiment can derive appropriate operation content related to the combustion equipment SF while reflecting the operator's operation pattern. As a result, fluctuations in the steam volume of the combustion equipment SF can be suppressed.

[0095] 10. Other Embodiments Although the embodiments of the present disclosure have been described above in detail with reference to the drawings, the specific configuration is not limited to this embodiment, and design modifications within the scope of the present disclosure are also included. The initial model in the present disclosure may be an initial model generated by prior learning based on a correspondence between state quantity information of a facility other than the combustion facility SF and the operation details of the supply device of the facility. For example, the learning unit 161B of the learning system 160B trains the learning model M based on a correspondence between past state quantity information, the operation details of the extrusion device 13 (feeder 12) derived using the learning model M corresponding to the acquisition of the state quantity information, and the operation details of the extrusion device 13 (feeder 12) performed in the facility when the state quantity information was acquired. In this case, the learning unit 161B derives a reward based on the degree of agreement between the operation details of the extrusion device 13 (feeder 12) derived using the learning model M and the operation details of the extrusion device 13 (feeder 12) performed in the facility.

[0096] In addition, the operation details of the extrusion device 13 (feeder 12) may be learned after correction has been made based on the size ratio between the combustion equipment 1 and another piece of equipment, in order to take into account the difference in size between the combustion equipment 1 in which the information processing system 100 is installed and another piece of equipment.

[0097] In this case, reinforcement learning as pre-learning may be performed on the initial model of the modified example, and then the reinforcement learning of the first embodiment may be performed based on a reward derived from the detection value and the control target value. Alternatively, reinforcement learning as pre-learning and reinforcement learning of the first embodiment may be appropriately selected and performed on the initial model of the modified example, to obtain a learning model.

[0098] With this configuration, this modified example can derive appropriate operation content related to the combustion facility SF while reflecting the operation pattern in another facility.

[0099] 10 is a hardware configuration diagram showing the configuration of a computer 1100 according to this embodiment. The computer 1100 includes, for example, a processor 1110, a main memory 1120, a storage 1130, and an interface 1140.

[0100] Each functional unit of the information processing system 100 or the information processing system 100B described above is implemented in a computer 1100. The operation of each functional unit described above is stored in the form of a program in a storage 1130. The processor 1110 reads the program from the storage 1130, loads it into the main memory 1120, and executes the above-described processing in accordance with the program. The processor 1110 also allocates storage areas in the main memory 1120 to be used by each functional unit described above in accordance with the program.

[0101] The program may be for realizing some of the functions to be performed by the computer 1100. For example, the program may be combined with other programs already stored in the storage 1130 or other programs implemented in other devices to perform the functions. Furthermore, the computer 1100 may include a custom large-scale integrated circuit (LSI) such as a programmable logic device (PLD) in addition to or instead of the above configuration. Examples of PLDs include programmable array logic (PAL), generic array logic (GAL), complex programmable logic device (CPLD), and field programmable gate array (FPGA). In this case, some or all of the functions realized by the processor 1110 may be realized by the integrated circuit.

[0102] Examples of storage 1130 include a magnetic disk, a magneto-optical disk, and a semiconductor memory. Storage 1130 may be an internal medium directly connected to the bus of computer 1100, or an external medium connected to computer 1100 via interface 1140 or a communication line. Furthermore, when this program is distributed to computer 1100 via a communication line, computer 1100 that receives the program may deploy the program in main memory 1120 and execute the above-described processing. Furthermore, the program may be a program for realizing part of the above-described functions. Furthermore, the program may be a so-called differential file (differential program) that realizes the above-described functions in combination with another program already stored in storage 1130.

[0103] <Additional Notes> The information processing system 100 or the information processing system 100B described in each embodiment can be understood, for example, as follows.

[0104] (1) The information processing system 100 according to the first aspect is an information processing system for an incineration facility SF, and includes an acquisition unit 110 that acquires state quantity information, which is information relating to the state quantities of the combustion facility SF, and an operation content derivation unit 130 that inputs the state quantity information acquired by the acquisition unit 110 and derives the operation content of the supply device according to the input state quantity information using a learning model that has been trained based on the correspondence between the state quantity information, the operation content of the supply device (feeder 12) that supplies the material to be incinerated G to the combustion facility SF, and a reward according to the state of the combustion facility SF.

[0105] This configuration allows for the use of a learning model that learns operation details that increase rewards relative to the state quantities of the combustion equipment SF. This allows the information processing system 100 according to the first aspect to derive appropriate operation details related to the combustion equipment SF. As a result, it becomes easier to maintain the combustion equipment SF in a desired state.

[0106] (2) The information processing system 100 according to the second aspect is the information processing system 100 of (1), in which the state quantity information is a representative value obtained by statistically processing values ​​relating to the state quantities of the combustion equipment SF over a predetermined length of time.

[0107] According to this configuration, by performing statistical processing on the state quantities over a predetermined time period, it is possible to reduce the influence of the detected value of a state quantity that momentarily increases (or decreases) with respect to the state quantities that may instantaneously fluctuate. As a result, the information processing system 100 according to the second aspect can derive more appropriate operation content related to the combustion facility SF.

[0108] (3) The information processing system 100 according to the third aspect is the information processing system 100 according to (2), and the predetermined time length is a time length including the time difference between the time when the supply device (feeder 12) supplies the material to be incinerated G and the time when the state quantity fluctuates in response to the supply.

[0109] With this configuration, by performing statistical processing of the state quantities over a predetermined, relatively long period of time as described above, it is possible to reduce the influence of the detected values ​​of the state quantities that momentarily increase (or decrease) with respect to the state quantities that may momentarily change after the supply of the materials to be incinerated G. This allows the information processing system 100 according to the third aspect to derive more appropriate operation contents related to the combustion facility SF.

[0110] (4) The information processing system 100 according to the fourth aspect is the information processing system 100 described in any one of (1) to (3), wherein the operation content of the supply device (feeder 12) is determined by a combination of two or more of the operation amount of a first operation element of the supply device, the operation amount of a second operation element of the supply device that is different from the first operation element, and the stop time of the supply device.

[0111] According to this configuration, it is possible to specify in detail the operation details of the supply device (feeder 12), and thus the information processing system 100 according to the fourth aspect can derive more appropriate operation details related to the combustion facility SF.

[0112] (5) The information processing system 100 according to the fifth aspect is the information processing system 100 described in any one of (1) to (4), wherein the supply device (feeder 12) is a reciprocating or rotary feeder, and the operation of the supply device is determined by a combination of two or more of the stroke of the feeder 12 (feeder stroke ST), the speed of the feeder 12 (feeder speed SD), and the stop time of the feeder 12.

[0113] With this configuration, the operation of the supply device (feeder 12) can be specified in detail to control the supply of materials to be incinerated G. This allows the information processing system 100 according to the fifth aspect to derive more appropriate operation details related to the combustion facility SF.

[0114] (6) The information processing system 100 according to the sixth aspect is the information processing system 100 according to any one of (1) to (5), in which the reward is derived based on the magnitude of the difference between the detected value of at least one index included in the state quantity information and the target value of the index.

[0115] With this configuration, it is possible to derive an operation that will result in a higher reward based on the magnitude of the difference between the state quantity (detected value) after the operation is performed and the control target value. As a result, the information processing system 100 according to the sixth aspect can derive a more appropriate operation related to the combustion facility SF.

[0116] (7) The information processing system 100 according to the seventh aspect is the information processing system 100 described in any one of (1) to (6), wherein the state quantities are two or more of the lower heating value, the concentration of the exhaust material, the supply amount of the material to be incinerated, or the amount of steam.

[0117] This configuration makes it possible to input state quantities that can change as materials G are supplied to the combustion facility SF, thereby enabling the information processing system 100 according to the seventh aspect to derive more appropriate operation details related to the combustion facility SF.

[0118] (8) The information processing system 100 according to the eighth aspect is the information processing system 100 according to any one of (1) to (7), and further includes a learning unit that performs reinforcement learning of a learning model based on the correspondence between the state quantity information acquired by the acquisition unit, the operation content of the supply device, and the reward.

[0119] According to this configuration, the weighting of the expected value for each operation of the supply device can be changed based on the correspondence between the operation and the reward through reinforcement learning, thereby enabling the information processing system 100 according to the eighth aspect to derive more appropriate operation related to the combustion facility SF.

[0120] (9) The information processing system 100B according to the ninth aspect is the information processing system 100B described in any one of (1) to (8), and the learning model is obtained by performing reinforcement learning based on the correspondence between the state quantity information acquired by the acquisition unit 110, the operation content of the supply device, and the reward according to the state of the combustion equipment SF, based on an initial model generated by pre-learning based at least on the correspondence between the state quantity information and the operation content of the supply device obtained from the operator's operation history.

[0121] With this configuration, the information processing system 100B according to the ninth aspect can derive appropriate operation content related to the combustion facility SF while reflecting the operation patterns of the operator, thereby ensuring a certain degree of accuracy at the initial model stage.

[0122] (10) The information processing system 100B according to the tenth aspect is the information processing system 100B described in any one of (1) to (9), and the learning model is obtained by performing reinforcement learning based on the correspondence between the state quantity information acquired by the acquisition unit 110, the operation content of the supply device, and the reward according to the state of the combustion equipment SF, based on an initial model generated by pre-learning based at least on the correspondence between the state quantity information of another facility and the operation content of the supply device of the other facility.

[0123] With this configuration, the information processing system 100B according to the tenth aspect can derive appropriate operation details related to the combustion facility SF after reflecting operation details of other facilities, thereby ensuring a certain degree of accuracy at the initial model stage.

[0124] (11) The information processing method according to the eleventh aspect is an information processing method related to a combustion facility SF, and includes acquiring state quantity information, which is information relating to the state quantities of the combustion facility SF, inputting the state quantity information using a learning model that has been trained based on the correspondence between the state quantity information, the operation content of a supply device (feeder 12) that supplies incineration material G to the combustion facility SF, and a reward according to the state of the combustion facility SF, and deriving the operation content of the supply device according to the input state quantity information.

[0125] With this configuration, a learning model can be used that learns operation details that increase rewards for the state quantities of the combustion equipment SF. This allows the information processing method according to the eleventh aspect to derive appropriate operation details related to the combustion equipment SF. As a result, it becomes easier to maintain the combustion equipment SF in a desired state.

[0126] (12) The learning system 160 according to the twelfth aspect is a learning system for a combustion facility SF, and includes a learning unit that performs reinforcement learning of a learning model based on the correspondence between state quantity information, which is information relating to the state quantities of the combustion facility SF, the operation content of a supply device (feeder 12) that supplies incineration material G to the combustion facility SF, and a reward according to the state of the combustion facility SF.

[0127] With this configuration, the learning model can learn operation details that increase the reward for the state quantity of the combustion equipment SF. This allows the learning system 160 according to the twelfth aspect to derive appropriate operation details related to the combustion equipment SF. As a result, it becomes easier to maintain the combustion equipment SF in a desired state.

[0128] (13) The learning method according to the thirteenth aspect is a learning method for a combustion facility SF, and includes performing reinforcement learning of a learning model based on the correspondence between state quantity information, which is information about the state quantities of the combustion facility SF, the operation content of a supply device (feeder 12) that supplies the material to be incinerated G to the combustion facility SF, and a reward according to the state of the combustion facility SF.

[0129] With this configuration, the learning model can learn operation details that increase the reward for the state quantity of the combustion equipment SF. As a result, the learning method according to the thirteenth aspect can derive appropriate operation details related to the combustion equipment SF. As a result, it becomes easier to maintain the combustion equipment SF in a desired state.

[0130] According to the above-described aspect, it is possible to derive appropriate operation content related to the combustion equipment.

[0131] SF... incineration equipment, G... material to be incinerated (garbage), 1... crane, 1a... gripping part, 1b... weight sensor, 2... incinerator, 3... exhaust heat recovery boiler, 4... cooling tower, 5... dust collector, 6... flue, 6a... gas concentration sensor, 7... chimney, 10... supply mechanism, 11... hopper, 12... feeder, 13... extrusion device, 14... object measuring instrument, 15... moisture measuring instrument, 20... furnace body, 21... visible light camera, 22... red External camera, 30...stoker, 31...fire grate, 32...fire grate drive device, 41...wind box, 41a...wind box pressure sensor, 42...discharge chute, 43...furnace, 50...blowing mechanism, 51...blower, 52...primary air line, 53...air preheater, 54...secondary air line, 55...damper, 56...air flow sensor, 56A...first air flow sensor, 56B...second air flow sensor, 61...boy main body, 62...pipe, 63...radiation temperature sensor (infrared temperature sensor), 63...radiation temperature sensor, 64...furnace pressure sensor, 65...feedwater flow rate sensor, 66...superheater attemperator flow rate sensor (steam amount sensor), 66...superheater attemperator flow rate sensor, 66A...first superheater attemperator flow rate sensor, 66B...second superheater attemperator flow rate sensor, 100...information processing system, 100B...information processing system, 110...acquisition unit, 120...storage unit, 120B...storage unit, 130...operation content derivation unit, 140...control unit, 150...notification unit, 160...learning system, 160B...learning system, 161...learning unit, 161B...learning unit, 162...generation unit, 1100...computer, 1110...processor, 1120...main memory, 1130...storage, 1140...interface

Claims

1. An information processing system for a combustion equipment, comprising: an acquisition unit that acquires state quantity information, which is information relating to a state quantity of the combustion equipment; and an operation content derivation unit that inputs the state quantity information acquired by the acquisition unit and derives the operation content of the supply device according to the input state quantity information using a learning model that has been trained based on a correspondence between the state quantity information, the operation content of the supply device that supplies materials to be incinerated to the combustion equipment, and a reward according to the state of the combustion equipment.

2. The information processing system according to claim 1, wherein the state quantity information is a representative value obtained by statistically processing values ​​relating to the state quantities of the combustion equipment over a predetermined period of time.

3. The information processing system of claim 2, wherein the specified length of time is a length of time that includes the time difference between the time when the supply device supplies the incineration material and the time when the state quantity fluctuates in response to the supply.

4. An information processing system as described in claim 1 or claim 2, wherein the operation content of the supply device is determined by a combination of two or more of the operation amount of a first operation element of the supply device, the operation amount of a second operation element of the supply device that is different from the first operation element, and the stop time of the supply device.

5. An information processing system as described in claim 1 or claim 2, wherein the supply device is a reciprocating or rotary feeder, and the operation of the supply device is determined by a combination of two or more of the stroke of the feeder, the speed of the feeder, and the stop time of the feeder.

6. The information processing system according to claim 1 or 2, wherein the reward is derived based on the magnitude of a difference between a detected value of at least one index included in the state quantity information and a target value of the index.

7. An information processing system according to claim 1 or 2, wherein the state quantities are two or more of a lower heating value, a concentration of exhaust substances, a supply amount of the incinerated material, or a steam amount.

8. An information processing system as described in claim 1 or claim 2, further comprising a learning unit that performs reinforcement learning of the learning model based on the correspondence between the state quantity information acquired by the acquisition unit, the operation content of the supply device, and the reward.

9. The information processing system of claim 1 or 2, wherein the learning model is obtained by performing reinforcement learning based on the correspondence between the state quantity information acquired by the acquisition unit, the operation content of the previous supply device, and the reward, based on an initial model generated by pre-learning based at least on the correspondence between the state quantity information and the operation content of the previous supply device obtained from the operator's operation history.

10. The information processing system of claim 1 or claim 2, wherein the learning model is obtained by performing reinforcement learning based on the correspondence between the state quantity information acquired by the acquisition unit, the operation content of the previous supply device, and the reward, based on an initial model generated by pre-learning based at least on the correspondence between the state quantity information in another facility and the operation content of the supply device in the other facility.

11. An information processing method related to a combustion equipment, comprising: acquiring state quantity information which is information relating to the state quantity of the combustion equipment; inputting the state quantity information using a learning model which has been trained based on the correspondence between the state quantity information, the operation contents of a supply device which supplies materials to be incinerated to the combustion equipment, and a reward according to the state of the combustion equipment; and deriving the operation contents of the supply device according to the input state quantity information.

12. A learning system for a combustion equipment, comprising a learning unit that performs reinforcement learning of a learning model based on the correspondence between state quantity information, which is information regarding the state quantity of the combustion equipment, the operation contents of a supply device that supplies materials to be incinerated to the combustion equipment, and a reward according to the state of the combustion equipment.

13. A learning method for a combustion equipment, comprising: performing reinforcement learning of a learning model based on a correspondence between state quantity information, which is information relating to the state quantity of the combustion equipment, the operation contents of a supply device that supplies materials to be incinerated to the combustion equipment, and a reward according to the state of the combustion equipment.

Images