Moisture content control device, incineration equipment system, moisture content control method, and program

Abstract

To provide a water content control device, an incineration facility system, a water content control method and a program, even when the properties of sludge is changed in a sludge dewatering machine, capable of stabilizing an in-furnace temperature of an incineration furnace by controlling the water content of dewatered sludge.SOLUTION: A water content control device comprises: a dewatering condition information acquisition part for acquiring dewatering condition information showing conditions of dewatering in a sludge dewatering machine dewatering sludge; an in-furnace temperature acquisition part for acquiring an in-furnace temperature of an incineration furnace incinerating the dewatered sludge dewatered by the sludge dewatering machine; a water content prediction part for predicting the water content of the dewatered sludge using a learned model in which a relation between the dewatering condition information and the water content of the dewatered sludge has been mechanically learned; and a water content optimization part for optimizing the predicted water content by model prediction control based on at least the acquired in-furnace temperature.SELECTED DRAWING: Figure 2

Description

Technical Field

[0001] The present invention relates to a moisture content control device, an incineration facility system, a moisture content control method, and a program.

Background Art

[0002] Conventionally, as a technique for stabilizing the temperature inside a furnace of an incinerator that incinerates dewatered sludge in a sludge treatment facility such as sewage sludge, a technique for stabilizing the temperature inside the furnace by controlling the moisture content of the sludge dewatered by a sludge dewatering machine (hereinafter also referred to as "dewatered sludge"), and a technique for stabilizing the temperature inside the furnace by performing multi-input multi-output predictive control are known.

[0003] For example, Patent Document 1 below discloses a technique for adjusting the moisture content of the sludge input into the incinerator and stabilizing the temperature inside the furnace by controlling a moisture content adjustment mechanism based on the combustion state of the sludge using the concentration of nitrous oxide gas as an index, thereby adjusting the mixing ratio of the dewatered sludge and the dried sludge.

[0004] Also, Patent Document 2 below discloses a technique for creating a learning model that learns a model for operating control of a treatment plant facility with a multi-input multi-output system from various process data obtained by operating the treatment plant facility, and stabilizing the temperature inside the furnace by performing predictive operation control after a predetermined time using the learning model.

Prior Art Documents

Patent Documents

[0005]

Patent Document 1

Patent Document 2

Summary of the Invention

Problems to be Solved by the Invention

[0006] Incidentally, the viscosity of sludge changes due to changes in sludge temperature, or changes in sludge properties due to changes in sludge temperature cause changes in viscosity. When the viscosity of sludge changes due to changes in sludge temperature in this way, the relationship between the shaft torque of the sludge dewatering machine and the water content of the dewatered sludge also changes. For example, when a heating dewatering machine that improves dewatering efficiency by heating sludge is used as a sludge dewatering machine, the properties of the sludge change due to changes in the sludge temperature, making it difficult to stably measure the water content. Along with this, it also becomes difficult to control the temperature inside the incinerator by controlling the water content as in the technologies of Patent Document 1 and Patent Document 2.

[0007] In view of the above problems, an object of the present invention is to provide a water content control device, an incineration facility system, a water content control method, and a program capable of stabilizing the temperature inside an incinerator by controlling the water content of dewatered sludge even when the properties of the sludge change in a sludge dewatering machine.

Means for Solving the Problems

[0008] To solve the above problems, a water content control device according to an aspect of the present invention includes a dehydration condition information acquisition unit that acquires dehydration condition information indicating dehydration conditions in a sludge dewatering machine that dehydrates sludge, a furnace internal temperature acquisition unit that acquires the temperature inside an incinerator that incinerates the dewatered sludge dehydrated by the sludge dewatering machine, and a learned model in which the relationship between the dehydration condition information and the water content of the dewatered sludge is machine-learned 、 discharged from the sludge dehydrator according to the dehydration conditions indicated by the acquired dehydration condition information a water content prediction unit that predicts the water content of the dewatered sludge, before the acquired furnace internal temperature and the predicted water content and by used at least as an input model predictive control outputs the moisture content at which the temperature in the furnace stabilizes a water content optimization unit that performs, A moisture content control unit that controls the operation of the sludge dehydrator so that the moisture content of the dehydrated sludge discharged from the sludge dehydrator becomes the moisture content output from the moisture content optimization unit; and is provided with.

[0009] An incineration facility system according to an aspect of the present invention includes a water content control device.

[0010] The moisture content control method according to one aspect of the present invention includes a dehydration condition information acquisition process in which a dehydration condition information acquisition unit acquires dehydration condition information indicating the conditions of dehydration in a sludge dehydrator for dehydrating sludge, a furnace internal temperature acquisition process in which a furnace internal temperature acquisition unit acquires the furnace internal temperature of an incinerator for incinerating the dehydrated sludge dehydrated by the sludge dehydrator, and a moisture content prediction process in which a moisture content prediction unit predicts the moisture content of the dehydrated sludge using a learned model obtained by machine learning the relationship between the dehydration condition information and the moisture content of the dehydrated sludge 、 discharged from the sludge dehydrator according to the dehydration conditions indicated by the acquired dehydration condition information a moisture content prediction process for predicting the moisture content of the dehydrated sludge, and a moisture content optimization process in which a moisture content optimization unit before uses the acquired furnace internal temperature and and the predicted moisture content and to used at least as an input perform moisture content optimization by model predictive control outputs the moisture content at which the temperature in the furnace stabilizes and includes The moisture content control unit controls the operation of the sludge dehydrator so that the moisture content of the dehydrated sludge discharged from the sludge dehydrator becomes the moisture content output from the moisture content optimization unit. The moisture content control process; .

[0011] A program according to one aspect of the present invention causes a computer to function as a dehydration condition information acquisition means for acquiring dehydration condition information indicating the conditions of dehydration in a sludge dehydrator for dehydrating sludge, a furnace internal temperature acquisition means for acquiring the furnace internal temperature of an incinerator for incinerating the dehydrated sludge dehydrated by the sludge dehydrator, a moisture content prediction means for predicting the moisture content of the dehydrated sludge using a learned model obtained by machine learning the relationship between the dehydration condition information and the moisture content of the dehydrated sludge 、 discharged from the sludge dehydrator according to the dehydration conditions indicated by the acquired dehydration condition information a moisture content prediction means for predicting the moisture content of the dehydrated sludge, before uses the acquired furnace internal temperature and and the predicted moisture content and to used at least as an input perform moisture content optimization by model predictive control outputs the moisture content at which the temperature in the furnace stabilizes a moisture content optimization means, and A moisture content control means for controlling the operation of the sludge dehydrator so that the moisture content of the dehydrated sludge discharged from the sludge dehydrator becomes the moisture content output from the moisture content optimization means; function as

Advantages of the Invention

[0012] According to the present invention, even when the properties of the sludge change in the sludge dehydrator, the furnace internal temperature of the incinerator can be stabilized by controlling the moisture content of the dehydrated sludge.

Brief Description of the Drawings

[0013]

Figure 1

Figure 2

Figure 3

Figure 4

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Figure 6

Figure 7

MODE FOR CARRYING OUT THE INVENTION

[0014] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

[0015] <<1. First Embodiment>> The first embodiment will be described with reference to FIGS. 1 to 4.

[0016] <1-1. Configuration of Incineration Facility System> First, with reference to FIG. 1, the configuration of the incineration facility system according to the first embodiment will be described. FIG. 1 is a diagram showing an example of the configuration of the incineration facility system according to the first embodiment. As shown in FIG. 1, the incineration facility system 1 includes a concentrator 10, a heating dehydrator 20, a torque sensor 21, a thermo thermometer 22, a dehydrated sludge hopper 23, a sludge flow meter 24, an incinerator 30, a high-pressure water flow meter 31, a fuel flow meter 32, a waste heat boiler 40, a heat utilization facility 50, a dust collector 60, a supercharger 61, an oxygen concentration meter 62, a white smoke prevention preheater 63, a fan 64, a hot water boiler 65, a flue gas treatment tower 70, and a control panel 100.

[0017] The thickener 10 concentrates the flocculated sludge A that is mixed and flocculated in a flocculation tank (not shown) by adding a flocculant to sewage sludge such as mixed raw sludge generated from a sewage treatment plant. For example, the thickener 10 has a thickening filtration screen inside, and separates and thickens the water content of the flocculated sludge A supplied from the thickener 10 with this thickening filtration screen. The thickened sludge B discharged from the thickener 10 is supplied to the heating dehydrator 20. Note that a flocculant such as an inorganic flocculant such as ferric polysulfate (PFS) or a polymer flocculant may be added to the thickened sludge B supplied to the heating dehydrator 20. Further, the thickened sludge B may be heated using the warm water K supplied from the warm water boiler 65 described later.

[0018] The heating dehydrator 20 is an example of a sludge dehydrator that dehydrates the thickened sludge B supplied from the thickener 10. The heating dehydrator 20 has a function of heating and dehydrating the thickened sludge B. The heating dehydrator 20 heats the thickened sludge B using, for example, the warm water K supplied from the warm water boiler 65 described later. The heating dehydrator 20 may be any of a vertical screw press, a horizontal screw press, and a rotary press as long as it is a sludge dehydrator capable of dehydrating the thickened sludge B.

[0019] The torque sensor 21 measures the shaft torque in the heating dehydrator 20. For example, the torque sensor 21 is provided so as to be able to measure the torque of the rotation drive unit (not shown) of the heating dehydrator 20. The torque sensor 21 measures the torque of the rotation drive unit as the shaft torque in the heating dehydrator 20 and outputs the measurement result to the control panel 100.

[0020] The thermo thermometer 22 measures the temperature of the sludge (sludge temperature) dehydrated by the heating dehydrator 20. For example, the thermo thermometer 22 is provided so as to be able to measure the temperature of the dehydrated sludge C discharged into the discharge chamber in the heating dehydrator 20 after dehydration. The thermo thermometer 22 measures the sludge temperature of the dehydrated sludge C and outputs the measurement result to the control panel 100.

[0021] The dehydrated sludge hopper 23 temporarily stores the dehydrated sludge C discharged from the heating dehydrator 20. The dehydrated sludge C stored in the dehydrated sludge hopper 23 is supplied to the incinerator 30 by, for example, a dehydrated sludge supply pump (not shown). A weighing scale (not shown) is provided in the dehydrated sludge hopper 23. The weighing scale provided in the dehydrated sludge hopper 23 measures the weight of the dehydrated sludge hopper 23 and outputs the measurement result to the control panel 100. For example, the weighing scale measures the weight of the dehydrated sludge hopper 23 before the start of the supply of the dehydrated sludge C and the weight of the dehydrated sludge hopper 23 in a state where the supply of the dehydrated sludge C has been temporarily stopped after the start of the supply of the dehydrated sludge C. The supply amount of the dehydrated sludge C supplied to the incinerator 30 can be calculated from the difference between these weights.

[0022] The sludge flowmeter 24 measures the supply amount (sludge flow rate) of the dehydrated sludge C supplied from the dehydrated sludge hopper 23 to the incinerator 30 and outputs the measurement result to the control panel 100.

[0023] The incinerator 30 incinerates the dehydrated sludge C supplied from the heating dehydrator 20. The incinerator 30 is, for example, a forced-feed type fluidized bed incinerator, and the dehydrated sludge C supplied to the incinerator 30 is burned while being fluidized together with a fluidized medium by the high-temperature and high-pressure combustion air D supplied from a forced feeder 61 described later, thereby generating high-temperature combustion exhaust gas G.

[0024] The high-pressure water flowmeter 31 measures the supply amount (high-pressure water flow rate) of the high-pressure water E supplied to the incinerator 30 and outputs the measurement result to the control panel 100.

[0025] The fuel flowmeter 32 measures the supply amount (fuel flow rate) of the fuel F supplied to the incinerator 30 and outputs the measurement result to the control panel 100. The fuel includes, for example, auxiliary fuels for incinerators such as heavy oil, kerosene, city gas, and digester gas.

[0026] The combustion exhaust gas G generated in the incinerator 30 is supplied to the waste heat boiler 40, cooled by being heat-exchanged with the heat medium H supplied from the heat utilization facility 50, further supplied to the dust collector 60 for dust collection, and then supplied to the supercharger 61 described above to pressurize the combustion air D supplied to the incinerator 30. Note that the heat utilization facility 50 includes, for example, a dryer and a generator.

[0027] The combustion exhaust gas G discharged from the supercharger 61 is supplied to the white smoke prevention preheater 63, and cooled by preheating the white smoke prevention air I supplied from the fan 64 in this white smoke prevention preheater 63.

[0028] The oxygen concentration meter 62 measures the oxygen concentration in the combustion exhaust gas G supplied to the white smoke prevention preheater 63, and outputs the measurement result to the control panel 100.

[0029] The combustion exhaust gas G cooled in the white smoke prevention preheater 63 is supplied to the hot water boiler 65 for further cooling, and then supplied to the flue gas treatment tower 70. The flue gas treatment tower 70 sprays water supplied from the outside to remove predetermined substances such as dust and impurities from the combustion exhaust gas G supplied from the hot water boiler 65, and exhausts the combustion exhaust gas G from the chimney. Further, the flue gas treatment tower 70 supplies the hot water K heated by the heat of the combustion exhaust gas G with the water supplied from the outside to the hot water boiler 65. The hot water boiler 65 supplies the hot water K supplied from the flue gas treatment tower 70 to the concentrator 10 and the heating and dehydrator 20.

[0030] The control panel 100 controls each part in the incineration facility system 1. The control panel 100 is configured by a computer or the like.

[0031] <1-2. Functional configuration of the control panel> The configuration of the incineration facility system 1 according to the first embodiment has been described above. Subsequently, with reference to FIG. 2, the functional configuration of the control panel 100 according to the first embodiment will be described. FIG. 2 is a block diagram showing an example of the functional configuration of the control panel 100 according to the first embodiment. As shown in FIG. 2, the control panel 100 includes a communication unit 110, a dehydration condition information acquisition unit 120, a disturbance information acquisition unit 121, a furnace internal temperature acquisition unit 122, a balance information acquisition unit 123, a storage unit 130, a moisture content prediction unit 140, a moisture content optimization unit 150, and a moisture content control unit 160.

[0032] (1) Communication unit 110 The communication unit 110 has a function of transmitting and receiving various information. For example, the communication unit 110 communicates with each part of the incineration facility system 1.

[0033] (2) Dehydration condition information acquisition unit 120 The dehydration condition information acquisition unit 120 has a function of acquiring dehydration condition information. The dehydration condition information is information indicating the conditions for dehydration in the heating dehydrator 20. The dehydration condition information is, for example, information indicating the shaft torque of the rotation drive unit that rotates when the heating dehydrator 20 dehydrates sludge, the sludge temperature of the dehydrated sludge, the sludge properties of the sludge, and the like. The dehydration condition information acquisition unit 120 acquires at least any one of the shaft torque, the sludge temperature, or the sludge properties as the dehydration condition information.

[0034] The shaft torque acquired by the dehydration condition information acquisition unit 120 is, for example, the shaft torque output from the torque sensor 21. Also, the sludge temperature acquired by the dehydration condition information acquisition unit 120 is, for example, the sludge temperature output from the thermo thermometer 22. Also, the sludge properties acquired by the dehydration condition information acquisition unit 120 are, for example, the mixing ratio of primary sedimentation sludge, excess sludge, digested sludge, etc. (hereinafter also referred to as "sludge mixing ratio"). The sludge mixing ratio is calculated from the flow rates of primary sedimentation sludge, excess sludge, digested sludge, mechanically concentrated sludge, etc. supplied to a sludge mixing tank (not shown), for example. The flow rates of primary sedimentation sludge, excess sludge, digested sludge, etc. are, for example, the flow rates output from flow meters provided in the respective supply pipes of primary sedimentation sludge, excess sludge, digested sludge, etc. supplied to the sludge mixing tank. The sludge adjusted to a predetermined mixing ratio in a sludge mixing tank or the like is supplied to a flocculation tank (not shown) for concentration and dehydration.

[0035] Note that the dehydration condition information is not limited to such examples, and the dehydration condition information acquisition unit 120 may acquire information other than the shaft torque, sludge temperature, and sludge properties as the dehydration condition information.

[0036] (3) Disturbance information acquisition unit 121 The disturbance information acquisition unit 121 has a function of acquiring disturbance information. The disturbance information is information indicating a disturbance that affects the change in the furnace temperature. The disturbance information is, for example, the sludge temperature, sludge flow rate, high-pressure water flow rate, fuel flow rate, oxygen concentration, sludge conveyance time, and the like. The disturbance information acquisition unit 121 acquires at least one of the sludge temperature or the sludge conveyance time among the sludge temperature, sludge flow rate, high-pressure water flow rate, fuel flow rate, oxygen concentration, and sludge conveyance time as the disturbance information.

[0037] The sludge temperature acquired by the disturbance information acquisition unit 121 is, for example, the sludge temperature output from the thermo thermometer 22. The sludge flow rate acquired by the disturbance information acquisition unit 121 is, for example, the sludge flow rate output from the sludge flow meter 24. The high-pressure water flow rate acquired by the disturbance information acquisition unit 121 is, for example, the high-pressure water flow rate output from the high-pressure water flow meter 31. The fuel flow rate acquired by the disturbance information acquisition unit 121 is, for example, the fuel flow rate output from the fuel flow meter 32. The oxygen concentration acquired by the disturbance information acquisition unit 121 is, for example, the oxygen concentration output from the oxygen concentration meter 62. The sludge conveyance time acquired by the disturbance information acquisition unit 121 is, for example, the conveyance time of the dehydrated sludge from the heating dehydrator 20 to the incinerator 30. The sludge conveyance time is the total time of the time calculated based on the conveyance distance from the heating dehydrator 20 to the incinerator 30 and the residence time of the dehydrated sludge in the dehydrated sludge hopper 23. The disturbance information acquisition unit 121 may acquire the sludge conveyance time calculated externally, or may acquire the sludge conveyance time by calculating it itself.

[0038] (4) Furnace temperature acquisition unit 122 The in - furnace temperature acquisition unit 122 has a function of acquiring the in - furnace temperature. The in - furnace temperature is the temperature inside the incinerator 30 that incinerates the dewatered sludge dewatered by the heating and dewatering machine 20, and is, for example, the temperature inside the fluidized bed of a fluid medium (such as fluidized sand, etc.) (sand layer temperature), the temperature of the free - board part, etc. In the first embodiment, the in - furnace temperature acquired by the in - furnace temperature acquisition unit 122 includes a set value and a measured value.

[0039] The set value of the in - furnace temperature is, for example, the temperature inside the furnace set by a control device of the incinerator 30 provided in the incinerator 30. The in - furnace temperature acquisition unit 122 acquires the set value of the in - furnace temperature from the control device, etc. For example, when the moisture content optimization unit 150 described later performs moisture content optimization, the in - furnace temperature acquisition unit 122 acquires the set value of the in - furnace temperature as the in - furnace temperature used by the moisture content optimization unit 150.

[0040] The measured value of the in - furnace temperature is, for example, the temperature inside the furnace measured by a sensor device, etc. provided so as to be able to measure the temperature inside the incinerator 30. The in - furnace temperature acquisition unit 122 acquires the measured value of the in - furnace temperature from the sensor device, etc. For example, the in - furnace temperature acquisition unit 122 acquires, as the measured value, the in - furnace temperature of the incinerator 30 measured after the heating and dewatering machine 20 is controlled based on the moisture content optimized by the moisture content optimization unit 150 described later. The measured value of the in - furnace temperature is used for updating the prediction model (feedback control) in the moisture content optimization unit 150.

[0041] (5) Income - expenditure information acquisition unit 123 The income - expenditure information acquisition unit 123 has a function of acquiring income - expenditure information. The income - expenditure information is information indicating the material balance and heat balance in the dewatering of sludge by the heating and dewatering machine 20. Specifically, the income - expenditure information acquisition unit 123 acquires the combustion exhaust gas volume, dry gas volume, moisture content brought in by combustion air, moisture content generated during sludge combustion, and moisture content generated during auxiliary fuel combustion as income - expenditure information.

[0042] (6) Storage unit 130 The storage unit 130 has a function of storing various information. The storage unit 130 is composed of a storage medium, for example, a HDD (Hard Disk Drive), a NAS (Network Attached Storage), an SSD (Solid State Drive), a flash memory, an EEPROM (Electrically Erasable Programmable Read Only Memory), a RAM (Random Access read / write Memory), a ROM (Read Only Memory), or an arbitrary combination of these storage media. The storage unit 130 stores, for example, a learned model used by the moisture content prediction unit 140 described later, a prediction model used by the moisture content optimization unit 150, and the like.

[0043] (7) Moisture content prediction unit 140 The moisture content prediction unit 140 has a function of predicting the moisture content of dewatered sludge. The moisture content prediction unit 140 predicts, for example, the moisture content of the dewatered sludge from the dewatering condition information acquired by the dewatering condition information acquisition unit 120 according to the dewatering conditions. In the first embodiment, the moisture content prediction unit 140 acquires the moisture content of the dewatered sludge using a model capable of outputting the moisture content of the dewatered sludge. For example, the moisture content prediction unit 140 predicts the moisture content of the dewatered sludge using a learned model. The learned model is a learned model obtained by machine learning the relationship between the dewatering condition information and the moisture content of the dewatered sludge. That is, when the dewatering condition information is input as input data, the learned model can output the moisture content of the dewatered sludge as output data. Specifically, the moisture content prediction unit 140 inputs the dewatering condition information acquired by the dewatering condition information acquisition unit 120 as input data into the learned model and predicts the output moisture content as the moisture content of the dewatered sludge.

[0044] Any machine learning method may be applied to the learned model used by the moisture content prediction unit 140. The arbitrary machine learning method is, for example, linear regression, support vector machine regression, Gaussian process regression, decision tree, neural network, and the like. Note that the model capable of outputting the water content of dewatered sludge is not limited to a machine - learned trained model. For example, the model capable of outputting the water content of dewatered sludge may be a regression model, a regression formula, a classification model, etc. Also, the water content prediction unit 140 may predict the water content of the dewatered sludge based on the revenue and expenditure information acquired by the revenue and expenditure information acquisition unit 123. For example, the prediction of the water content based on the revenue and expenditure information calculates the sludge water content by further subtracting the amount of water generated during sludge combustion from the value obtained by subtracting the amount of dry gas in the combustion gas and the exhaust gas. Specifically, the sludge water content is calculated from the following formula. Sludge water content = (Combustion exhaust gas volume - Dry gas volume - Water volume brought in by combustion air - Water volume generated during sludge combustion - Water volume generated during auxiliary fuel combustion) / Input sludge volume

[0045] (8) Water content optimization unit 150 The water content optimization unit 150 has a function of optimizing the water content predicted by the water content prediction unit 140. For example, the water content optimization unit 150 optimizes the water content (predicted value) predicted by the water content prediction unit 140 by model predictive control (MPC: Model Predictive Control) based at least on the furnace temperature (set value) acquired by the furnace temperature acquisition unit 122. The water content optimization unit 150 performs model predictive control using an optimizer and a prediction model. The prediction model simulates the model of the incineration of dewatered sludge in the incinerator 30. Specifically, in model predictive control, the optimizer and the prediction model predict the future response so that the input furnace temperature (set value) is obtained, and the optimal water content (set value) is output.

[0046] Further, the moisture content optimization unit 150 may use the disturbance information acquired by the disturbance information acquisition unit 121 in optimizing the moisture content (predicted value). That is, the moisture content optimization unit 150 may optimize the moisture content (predicted value) by model predictive control based on at least the in-furnace temperature (set value) and the disturbance information. Thereby, the moisture content optimization unit 150 can output a more optimal moisture content (set value) by predicting the future response considering the disturbance by the optimizer and the prediction model in model predictive control. That is, the moisture content optimization unit 150 can optimize the moisture content (predicted value) with higher accuracy than the optimization using only the in-furnace temperature (set value) by also using the disturbance information.

[0047] Further, by the moisture content optimization unit 150 predicting the future including disturbances by model predictive control, the in-furnace temperature becomes stable, and it becomes possible to operate the incinerator 30 under conditions where the production amount and emission amount of nitrous oxide, which is said to be one of the ozone-depleting gases that destroy the ozone layer, are low. Also, it becomes possible to stably continue the state where the sludge burns stably (self-ignites) without using auxiliary fuel. Also, when it is necessary to maintain the in-furnace temperature by combustion assistance using auxiliary fuel, the stable in-furnace temperature makes it possible to reduce the usage amount of auxiliary fuel such as heavy oil, and it also becomes possible to reduce costs and the emission amount of carbon dioxide.

[0048] The moisture content optimization unit 150 updates (feedback control) the prediction model used for model predictive control based on the result of control based on the output moisture content (set value). For example, the moisture content optimization unit 150 updates the prediction model based on the in-furnace temperature (measured value) of the incinerator 30 measured after the heating and dehydration machine 20 is controlled based on the moisture content (set value). Thereby, the moisture content optimization unit 150 can improve the prediction accuracy by the prediction model and also improve the optimization accuracy of the moisture content (predicted value).

[0049] (9) Moisture content control unit 160 The water content control unit 160 has a function of controlling the water content of the dewatered sludge discharged from the heating dehydrator 20. For example, the water content control unit 160 controls the operation of the heating dehydrator 20 so that the water content (set value) output from the water content optimization unit 150 is obtained. As an example, the water content control unit 160 controls the water content of the dewatered sludge by operating the amount of the flocculant added to the concentrated sludge supplied to the heating dehydrator 20.

[0050] <1-3. Input and Output in the Control Panel> As described above, the functional configuration of the control panel 100 according to the first embodiment has been described. Subsequently, with reference to FIG. 3, the input and output in the control panel 100 according to the first embodiment will be described. FIG. 3 is a diagram showing an example of the input and output in the control panel 100 according to the first embodiment. FIG. 3 shows an example in which the shaft torque, the sludge temperature, and the sludge mixing ratio are acquired as the dehydration condition information, and the furnace temperature (set value) and the disturbance information are used for the optimization of the water content (predicted value).

[0051] As shown in FIG. 3, first, the shaft torque, the sludge temperature, and the sludge mixing ratio acquired by the dehydration condition information acquisition unit 120 and the balance information acquired by the balance information acquisition unit 123 are input to the water content prediction unit 140 of the control panel 100. The shaft torque and the sludge temperature are measured values measured by the heating dehydrator 20. The water content prediction unit 140 outputs the water content (predicted value) of the dewatered sludge based on the input dehydration condition information. The water content (predicted value) output from the water content prediction unit 140 is input to the water content optimization unit 150.

[0052] In addition to the water content (predicted value), the disturbance information acquired by the disturbance information acquisition unit 121 and the furnace temperature (set value) acquired by the furnace temperature acquisition unit 122 are also input to the water content optimization unit 150. The water content optimization unit 150 outputs the water content (set value) based on the input water content (predicted value), disturbance information, and furnace temperature (set value). The water content (set value) output from the water content optimization unit 150 is input to the water content control unit 160.

[0053] Based on the input moisture content (set value), the moisture content control unit 160 outputs an operation amount for controlling the moisture content of the dewatered sludge in the heating and dewatering machine 20. The operation amount output from the moisture content control unit 160 is input to the heating and dewatering machine 20 or equipment related to the heating and dewatering machine 20.

[0054] The heating and dewatering machine 20 discharges the dewatered sludge that has been dewatered based on the input operation amount. The dewatered sludge discharged from the heating and dewatering machine 20 is supplied to the incinerator 30. The incinerator 30 incinerates the supplied dewatered sludge and outputs the in-furnace temperature (measured value) measured during or after incineration. The in-furnace temperature (measured value) output from the incinerator 30 is input to the moisture content optimization unit 150. Based on the input in-furnace temperature (measured value), the moisture content optimization unit 150 updates the prediction model.

[0055] <1-4. Operation of the control panel> The above describes the input and output in the control panel 100 according to the first embodiment. Subsequently, with reference to FIG. 4, the operation of the control panel 100 according to the first embodiment will be described. FIG. 4 is a flowchart showing an example of the operation of the control panel 100 according to the first embodiment. FIG. 4 shows an example in which the shaft torque, sludge temperature, and sludge mixing ratio are obtained as dewatering condition information, and the in-furnace temperature (set value) and disturbance information are used for optimizing the moisture content (predicted value).

[0056] As shown in FIG. 4, first, the dewatering condition information acquisition unit 120 of the control panel 100 acquires the dewatering condition information (step S101). Specifically, the dewatering condition information acquisition unit 120 acquires the shaft torque output from the torque sensor 21, the sludge temperature output from the thermo thermometer 22, the sludge flow rate output from the sludge flow meter 24, and the balance information output from the balance information acquisition unit 123 as the dewatering condition information.

[0057] Next, the moisture content prediction unit 140 of the control panel 100 predicts the moisture content of the dewatered sludge (step S102). Specifically, the moisture content prediction unit 140 predicts, as the moisture content (predicted value) of the dewatered sludge, the moisture content output from the learned model using the dehydration condition information acquired by the dehydration condition information acquisition unit 120 as input data.

[0058] Next, the disturbance information acquisition unit 121 of the control panel 100 acquires disturbance information (step S103). Specifically, the disturbance information acquisition unit 121 acquires, as disturbance information, the sludge temperature output from the thermo thermometer 22, the sludge flow rate output from the sludge flow meter 24, the high-pressure water flow rate output from the high-pressure water flow meter 31, the fuel flow rate output from the fuel flow meter 32, the oxygen concentration output from the oxygen concentration meter 62, and the sludge conveyance time calculated by the disturbance information acquisition unit 121.

[0059] Next, the in-furnace temperature acquisition unit 122 of the control panel 100 acquires the in-furnace temperature (step S104). Specifically, the in-furnace temperature acquisition unit 122 acquires the in-furnace temperature (set value) output from the control device of the incinerator 30 provided in the incinerator 30.

[0060] Next, the moisture content optimization unit 150 optimizes the moisture content (predicted value) (step S105). Specifically, the moisture content optimization unit 150 optimizes, by model predictive control, the moisture content (predicted value) predicted by the moisture content prediction unit 140 based on the disturbance information acquired by the disturbance information acquisition unit 121 and the in-furnace temperature (set value) acquired by the in-furnace temperature acquisition unit 122, and outputs the moisture content (set value).

[0061] Next, the moisture content control unit 160 controls the moisture content (step S106). Specifically, the moisture content control unit 160 controls the dehydration of the sludge in the heating dehydrator 20 so that the dewatered sludge has the moisture content (set value) optimized by the moisture content optimization unit 150, and ends the process.

[0062] As described above, the control panel 100 (moisture content control device) according to the first embodiment includes a dehydration condition information acquisition unit 120, a furnace internal temperature acquisition unit 122, a moisture content prediction unit 140, and a moisture content optimization unit 150. The dehydration condition information acquisition unit 120 acquires dehydration condition information indicating the conditions for dehydrating sludge in the heating dehydrator 20. The furnace internal temperature acquisition unit 122 acquires the furnace internal temperature (set value) of the incinerator 30 that incinerates the dehydrated sludge dehydrated by the heating dehydrator 20. The moisture content prediction unit 140 predicts the moisture content (predicted value) of the dehydrated sludge using a learned model obtained by machine learning the relationship between the dehydration condition information and the moisture content of the dehydrated sludge. The moisture content optimization unit 150 optimizes the predicted moisture content (predicted value) by model predictive control based at least on the acquired furnace internal temperature (set value).

[0063] With such a configuration, when the sludge dehydration condition (for example, sludge temperature) in the heating dehydrator 20 changes, the control panel 100 according to the first embodiment can predict the moisture content of the dehydrated sludge discharged according to the dehydration condition and optimize the moisture content according to the incineration status (for example, furnace internal temperature) of the incinerator 30. That is, even when it is difficult to stably measure the moisture content of the dehydrated sludge due to changes in the sludge temperature, the control panel 100 can stably control the furnace internal temperature of the incinerator by controlling the moisture content of the dehydrated sludge based on the predicted and optimized moisture content.

[0064] Therefore, the control panel 100 according to the first embodiment can stabilize the furnace internal temperature of the incinerator by controlling the moisture content of the dehydrated sludge even when the properties of the sludge change in the sludge dehydrator.

[0065] <<2. Second Embodiment>> The first embodiment has been described above. Subsequently, with reference to FIGS. 5 to 7, the second embodiment will be described. In the second embodiment, an example will be described in which the calculated moisture content (calculated value) calculated based on the balance information, which is information regarding the material balance and the heat balance, is also used for the optimization of the moisture content (predicted value).

[0066] <2-1. Configuration of the Incineration Facility System> Since the configuration of the incineration facility system according to the second embodiment is the same as that of the incineration facility system 1 according to the first embodiment, duplicate explanations are omitted.

[0067] <2-2. Functional Configuration of Control Panel> As described above, the configuration of the incineration facility system according to the second embodiment has been described. Subsequently, with reference to FIG. 5, the functional configuration of the control panel 100a according to the second embodiment will be described. FIG. 5 is a block diagram showing an example of the functional configuration of the control panel 100a according to the second embodiment. As shown in FIG. 5, the control panel 100a includes a communication unit 110, a dehydration condition information acquisition unit 120, a disturbance information acquisition unit 121, a furnace internal temperature acquisition unit 122, a balance information acquisition unit 123, a storage unit 130, a moisture content prediction unit 140, a moisture content calculation unit 141, a moisture content optimization unit 150a, and a moisture content control unit 160.

[0068] (1) Communication Unit 110 Since the function of the communication unit 110 according to the second embodiment is the same as that of the communication unit 110 according to the first embodiment, duplicate explanations are omitted.

[0069] (2) Dehydration Condition Information Acquisition Unit 120 Since the function of the dehydration condition information acquisition unit 120 according to the second embodiment is the same as that of the dehydration condition information acquisition unit 120 according to the first embodiment, duplicate explanations are omitted.

[0070] (3) Disturbance Information Acquisition Unit 121 Since the function of the disturbance information acquisition unit 121 according to the second embodiment is the same as that of the disturbance information acquisition unit 121 according to the first embodiment, duplicate explanations are omitted.

[0071] (4) Furnace Internal Temperature Acquisition Unit 122 Since the function of the furnace internal temperature acquisition unit 122 according to the second embodiment is the same as that of the furnace internal temperature acquisition unit 122 according to the first embodiment, duplicate explanations are omitted.

[0072] (5) Balance Information Acquisition Unit 123 Since the functions of the revenue and expenditure information acquisition unit 123 according to the second embodiment are the same as those of the revenue and expenditure information acquisition unit 123 according to the first embodiment, duplicate explanations are omitted.

[0073] (6) Memory unit 130 Since the functions of the memory unit 130 according to the second embodiment are the same as those of the memory unit 130 according to the first embodiment, duplicate explanations are omitted.

[0074] (7) Moisture content prediction unit 140 Since the functions of the moisture content prediction unit 140 according to the second embodiment are the same as those of the moisture content prediction unit 140 according to the first embodiment, duplicate explanations are omitted.

[0075] (8) Moisture content calculation unit 141 The moisture content calculation unit 141 has a function of calculating the moisture content. For example, the moisture content calculation unit 141 calculates the moisture content of the dewatered sludge based on the revenue and expenditure information acquired by the revenue and expenditure information acquisition unit 123. For example, the moisture content calculation unit 141 calculates the moisture content of the sludge by further subtracting the amount of moisture generated during sludge combustion from the value obtained by removing the amount of dry gas in the combustion gas and the exhaust gas. Specifically, the moisture content calculation unit 141 calculates the sludge moisture content from the following formula. Sludge moisture content = (Combustion exhaust gas volume - Dry gas volume - Moisture content brought in by combustion air - Moisture generated during sludge combustion - Moisture generated during auxiliary fuel combustion) / Input sludge volume

[0076] (9) Moisture content optimization unit 150a Since the functions of the moisture content optimization unit 150a according to the second embodiment are the same as those of the moisture content optimization unit 150 according to the first embodiment, duplicate explanations are omitted. Note that the moisture content optimization unit 150a optimizes the moisture content (predicted value) using the moisture content (calculated value).

[0077] For example, the moisture content optimization unit 150a optimizes the moisture content (predicted value) predicted by the moisture content prediction unit 140 by model predictive control based on at least the furnace internal temperature (set value) acquired by the furnace internal temperature acquisition unit 122 and the moisture content (calculated value) calculated by the moisture content calculation unit 141. Further, the moisture content optimization unit 150a may optimize the moisture content (predicted value) by model predictive control based on at least the furnace internal temperature (set value), disturbance information, and the moisture content (calculated value). In this way, the moisture content optimization unit 150a predicts the future response considering the moisture content (calculated value) by the optimizer and the prediction model in model predictive control, and can output a more optimal moisture content (set value) compared to the case where the moisture content (calculated value) is not used as in the first embodiment.

[0078] (10) Moisture content control unit 160 Since the function of the moisture content control unit 160 according to the second embodiment is the same as the function of the moisture content control unit 160 according to the first embodiment, duplicate explanations are omitted.

[0079] <2-3. Input and output in the control panel> As described above, the functional configuration of the control panel 100a according to the second embodiment has been described. Subsequently, with reference to FIG. 6, the input and output in the control panel 100a according to the second embodiment will be described. FIG. 6 is a diagram showing an example of the input and output in the control panel 100a according to the second embodiment. FIG. 6 shows an example in which the shaft torque, sludge temperature, and sludge mixing ratio are acquired as dehydration condition information, and the furnace internal temperature (set value), disturbance information, and moisture content (calculated value) are used for the optimization of the moisture content (predicted value).

[0080] As shown in FIG. 6, first, the shaft torque, sludge temperature, and sludge mixing ratio acquired by the dehydration condition information acquisition unit 120 are input as dehydration condition information to the moisture content prediction unit 140 of the control panel 100a. The shaft torque and the sludge temperature are measured values measured by the heating dehydrator 20. Based on the input dehydration condition information, the moisture content prediction unit 140 outputs the moisture content (predicted value) of the dewatered sludge. The moisture content (predicted value) output from the moisture content prediction unit 140 is input to the moisture content optimization unit 150a.

[0081] The balance information acquired by the balance information acquisition unit 123 is input to the moisture content calculation unit 141. Based on the input balance information, the moisture content calculation unit 141 outputs the moisture content (calculated value) of the dewatered sludge. The moisture content (calculated value) output from the moisture content calculation unit 141 is input to the moisture content optimization unit 150a.

[0082] In addition to the moisture content (predicted value) and the moisture content (calculated value), the disturbance information acquired by the disturbance information acquisition unit 121 and the furnace internal temperature (set value) acquired by the furnace internal temperature acquisition unit 122 are also input to the moisture content optimization unit 150a. Based on the input moisture content (predicted value), moisture content (calculated value), disturbance information, and furnace internal temperature (set value), the moisture content optimization unit 150a outputs the moisture content (set value). The moisture content (set value) output from the moisture content optimization unit 150a is input to the moisture content control unit 160.

[0083] Based on the input moisture content (set value), the moisture content control unit 160 outputs an operation amount for controlling the dehydration in the heating dehydrator 20. The operation amount output from the moisture content control unit 160 is input to the heating dehydrator 20 or the equipment related to the heating dehydrator 20.

[0084] The heating dehydrator 20 discharges the dewatered sludge that has undergone dehydration based on the input operation amount. The dewatered sludge discharged from the heating dehydrator 20 is supplied to the incinerator 30. The incinerator 30 incinerates the supplied dewatered sludge and outputs the furnace internal temperature (measured value) measured after incineration. The furnace internal temperature (measured value) output from the incinerator 30 is input to the moisture content optimization unit 150a. Based on the input furnace internal temperature (measured value), the moisture content optimization unit 150a updates the prediction model.

[0085] <2-4. Operation of the control panel> The input / output in the control panel 100a according to the second embodiment has been described above. Subsequently, with reference to FIG. 7, the operation of the control panel 100a according to the second embodiment will be described. FIG. 7 is a flowchart showing an example of the operation of the control panel 100a according to the second embodiment. FIG. 7 shows an example in which the shaft torque, the sludge temperature, and the sludge mixing ratio are acquired as dehydration condition information, and the furnace temperature (set value), the disturbance information, and the water content (calculated value) are used for the optimization of the water content (predicted value).

[0086] The processes of step S201 and step S202 shown in FIG. 7 are the same as the processes of step S101 and step S102 described with reference to FIG. 4, so redundant explanations will be omitted.

[0087] After the process of step S202, the balance information acquisition unit 123 of the control panel 100a acquires balance information (step S203).

[0088] Next, the water content calculation unit 141 calculates the water content of the dehydrated sludge (step S204). Specifically, the water content calculation unit 141 calculates the water content (calculated value) based on the balance information acquired by the balance information acquisition unit 123.

[0089] The processes of step S205 and step S206 shown in FIG. 7 are the same as the processes of step S103 and step S104 described with reference to FIG. 4, so redundant explanations will be omitted.

[0090] After the process of step S206, the water content optimization unit 150a of the control panel 100a optimizes the water content (predicted value) (step S207). Specifically, the water content optimization unit 150a optimizes the water content (predicted value) predicted by the water content prediction unit 140 by model predictive control based on the disturbance information acquired by the disturbance information acquisition unit 121, the furnace temperature (set value) acquired by the furnace temperature acquisition unit 122, and the water content (calculated value) calculated by the water content calculation unit 141, and outputs the water content (set value).

[0091] The process of step S208 shown in FIG. 7 is the same as the process of step S106 described with reference to FIG. 4, so duplicate explanations are omitted.

[0092] As described above, the control panel 100a (moisture content control device) according to the second embodiment includes a dehydration condition information acquisition unit 120, a furnace interior temperature acquisition unit 122, a moisture content prediction unit 140, and a moisture content optimization unit 150a. The dehydration condition information acquisition unit 120 acquires dehydration condition information indicating the conditions for dehydrating sludge in the heating dehydrator 20 for dehydrating sludge. The furnace interior temperature acquisition unit 122 acquires the furnace interior temperature (set value) of the incinerator 30 that incinerates the dehydrated sludge dehydrated by the heating dehydrator 20. The moisture content prediction unit 140 predicts the moisture content (predicted value) of the dehydrated sludge using a learned model obtained by machine learning the relationship between the dehydration condition information and the moisture content of the dehydrated sludge. The moisture content optimization unit 150a optimizes the predicted moisture content (predicted value) by model predictive control based on at least the acquired furnace interior temperature (set value).

[0093] With such a configuration, when the sludge dehydration conditions (for example, sludge temperature) in the heating dehydrator 20 change, the control panel 100a according to the first embodiment can predict the moisture content of the dehydrated sludge discharged according to the dehydration conditions, and optimize the moisture content according to the incineration status (for example, furnace interior temperature) of the incinerator 30. That is, even when it is difficult to stably measure the moisture content of the dehydrated sludge due to changes in the sludge temperature, the control panel 100a can control the moisture content of the dehydrated sludge based on the predicted and optimized moisture content, thereby stably controlling the furnace interior temperature of the incinerator.

[0094] Therefore, the control panel 100a according to the second embodiment can stabilize the furnace interior temperature of the incinerator by controlling the moisture content of the dehydrated sludge even when the properties of the sludge change in the sludge dehydrator.

[0095] In addition, the control panel 100a according to the second embodiment further includes a revenue and expenditure information acquisition unit 123 that acquires revenue and expenditure information, and a moisture content calculation unit 141 that calculates the moisture content of dehydrated sludge based on the revenue and expenditure information. The moisture content optimization unit 150a also uses the moisture content (calculated value) calculated by the moisture content calculation unit 141 to optimize the moisture content (predicted value) predicted by the moisture content prediction unit 140 by model predictive control. As a result, the moisture content optimization unit 150a predicts the future response considering the moisture content (calculated value) by the optimizer and the prediction model in model predictive control, and can output a more optimal moisture content (set value) compared to the case where the moisture content (calculated value) is not used as in the first embodiment.

[0096] The embodiments of the present invention have been described above. Note that part or all of the control panel 100 (moisture content control device) in the above-described embodiments may be implemented by a computer. In that case, a program for realizing this function may be recorded on a computer-readable recording medium, and the program recorded on this recording medium may be read into a computer system and executed to realize it. Here, the “computer system” shall include hardware such as an OS and peripheral devices. Further, the “computer-readable recording medium” refers to a portable medium such as a flexible disk, a magneto-optical disk, a ROM, a CD-ROM, or the like, and a storage device such as a hard disk incorporated in a computer system. Furthermore, the “computer-readable recording medium” also includes, like a communication line when transmitting a program via a network such as the Internet or a communication line such as a telephone line, a medium that dynamically holds a program for a short time, and a volatile memory inside a computer system that becomes a server or a client in that case and holds a program for a certain period of time. Also, the above program may be for realizing a part of the above-described functions, and may further be realized in combination with a program already recorded in the computer system for the above-described functions, or may be realized using a programmable logic device such as an FPGA (Field Programmable Gate Array).

[0097] As described above, the embodiments of the present invention have been described in detail with reference to the drawings. However, the specific configuration is not limited to the above, and various design changes and the like can be made without departing from the gist of the present invention.

Explanation of Signs

[0098] 1... Incineration equipment system, 10... Concentrator, 20... Heating and dewatering machine, 21... Torque sensor, 22... Thermometer, 23... Dewatered sludge hopper, 24... Sludge flow meter, 30... Incinerator, 31... High-pressure water flow meter, 32... Fuel flow meter, 40... Waste heat boiler, 60... Dust collector, 61... Supercharger, 62... Oxygen concentration meter, 63... White smoke prevention preheater, 64... Fan, 65... Hot water boiler, 70... Flue gas treatment tower, 100, 100a... Control panel, 110... Communication unit, 120... Dewatering condition information acquisition unit, 121... Disturbance information acquisition unit, 122... Furnace temperature acquisition unit, 123... Balance information acquisition unit, 130... Memory unit, 140... Moisture content prediction unit, 141... Moisture content calculation unit, 150, 150a... Moisture content optimization unit, 160... Moisture content control unit, A... Coagulated sludge, B... Concentrated sludge, C... Dewatered sludge, D... Combustion air, E... High-pressure water, F... Fuel, G... Combustion exhaust gas, H... Heat medium, I... White smoke prevention air, K... Hot water

Claims

1. A dehydration condition information acquisition unit that acquires dehydration condition information indicating dehydration conditions in a sludge dehydrator for dehydrating sludge; A furnace internal temperature acquisition unit that acquires the internal temperature of an incinerator that incinerates the dehydrated sludge dehydrated by the sludge dehydrator; A moisture content prediction unit that predicts the moisture content of the dehydrated sludge discharged from the sludge dehydrator according to the dehydration conditions indicated by the acquired dehydration condition information by using a learned model obtained by machine learning the relationship between the dehydration condition information and the moisture content of the dehydrated sludge; A moisture content optimization unit that outputs the moisture content at which the furnace internal temperature stabilizes by model predictive control using at least the acquired furnace internal temperature and the predicted moisture content as inputs; A moisture content control unit that controls the operation of the sludge dehydrator so that the moisture content of the dehydrated sludge discharged from the sludge dehydrator becomes the moisture content output from the moisture content optimization unit; A moisture content control device comprising the above.

2. An external disturbance information acquisition unit that acquires external disturbance information indicating an external disturbance that affects the change in the furnace internal temperature; further comprising; The moisture content optimization unit outputs the moisture content at which the furnace internal temperature stabilizes by model predictive control using at least the acquired furnace internal temperature and external disturbance information and the predicted moisture content as inputs. The moisture content control device according to Claim 1.

3. The external disturbance information acquisition unit acquires at least one of the sludge temperature of the dehydrated sludge or the sludge conveyance time for conveying the dehydrated sludge from the sludge dehydrator to the incinerator as the external disturbance information. The moisture content control device according to Claim 2.

4. The dehydration condition information acquisition unit acquires at least one of the shaft torque of a rotational drive unit that rotates when the sludge dehydrator dehydrates the sludge, the sludge temperature of the dehydrated sludge, or the sludge properties of the sludge as the dehydration condition information. The moisture content control device according to any one of Claims 1 to 3.

5. A balance information acquisition unit that acquires balance information regarding the material balance and heat balance in the dehydration of the sludge by the sludge dehydrator; A moisture content calculation unit that calculates the moisture content of the dehydrated sludge based on the acquired balance information; further comprising; The moisture content optimization unit outputs the moisture content at which the furnace internal temperature stabilizes by model predictive control using at least the acquired furnace internal temperature, the calculated moisture content, and the predicted moisture content as inputs. The moisture content control device according to any one of Claims 1 to 4.

6. The in-furnace temperature acquisition unit acquires the in-furnace temperature of the incinerator measured after the sludge dehydrator is controlled based on the moisture content output from the moisture content optimization unit, The moisture content optimization unit updates the prediction model used for the model predictive control based on the acquired in-furnace temperature. The moisture content control device according to any one of claims 1 to 5.

7. An incineration facility system including the moisture content control device according to any one of claims 1 to 6.

8. A dehydration condition information acquisition process in which a dehydration condition information acquisition unit acquires dehydration condition information indicating the conditions of dehydration in a sludge dehydrator for dehydrating sludge, An in-furnace temperature acquisition process in which an in-furnace temperature acquisition unit acquires the in-furnace temperature of an incinerator that incinerates the dehydrated sludge dehydrated by the sludge dehydrator, A moisture content prediction process in which a moisture content prediction unit predicts the moisture content of the dehydrated sludge discharged from the sludge dehydrator according to the dehydration conditions indicated by the acquired dehydration condition information using a learned model obtained by machine learning the relationship between the dehydration condition information and the moisture content of the dehydrated sludge, A moisture content optimization process in which a moisture content optimization unit outputs a moisture content at which the in-furnace temperature is stabilized by model predictive control using at least the acquired in-furnace temperature and the predicted moisture content as inputs, A moisture content control process in which a moisture content control unit controls the operation of the sludge dehydrator so that the moisture content of the dehydrated sludge discharged from the sludge dehydrator becomes the moisture content output from the moisture content optimization unit, A moisture content control method including the above.

9. A computer, A dehydration condition information acquisition means for acquiring dehydration condition information indicating the conditions of dehydration in a sludge dehydrator for dehydrating sludge, An in-furnace temperature acquisition means for acquiring the in-furnace temperature of an incinerator that incinerates the dehydrated sludge dehydrated by the sludge dehydrator, A moisture content prediction means for predicting the moisture content of the dehydrated sludge discharged from the sludge dehydrator according to the dehydration conditions indicated by the acquired dehydration condition information using a learned model obtained by machine learning the relationship between the dehydration condition information and the moisture content of the dehydrated sludge, A moisture content optimization means for outputting a moisture content at which the in-furnace temperature is stabilized by model predictive control using at least the acquired in-furnace temperature and the predicted moisture content as inputs, A moisture content control means for controlling the operation of the sludge dehydrator so that the moisture content of the dehydrated sludge discharged from the sludge dehydrator becomes the moisture content output from the moisture content optimization means, A program for causing it to function as such.

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