Ozone water concentration estimation device, ozone water concentration estimation method, and membrane separation activated sludge system

Abstract

The purpose of the present invention is to achieve an ozone water concentration estimation device which is capable of monitoring ozone water concentration with a simpler configuration than before even if impurities are present in ozone water. This ozone water concentration estimation device comprises a first data acquisition unit, an estimation unit, and a data output unit. The first data acquisition unit acquires first generation environment data including at least one of the ozone gas concentration of an ozone gas that is supplied to the inside of an ozone water generation tower, the ozone gas flow rate of the ozone gas, the water temperature of the ozone water that is retained in the ozone water generation tower, the pH of the ozone water, and the retention amount of the ozone water. The estimation unit estimates, from the first generation environment data, an ozone water concentration estimate value, which is an estimated value of the ozone water concentration in the ozone water at the time of measurement of the first generation environment data input from the first data acquisition unit, using a numerical model that estimates the ozone water concentration, which is the concentration of ozone dissolved in the ozone water at the time of measurement of the first generation environment data. The data output unit outputs the ozone water concentration estimate value.

Description

Ozone water concentration estimation device, ozone water concentration estimation method and membrane separation activated sludge system

[0001] The present disclosure relates to an ozone water concentration estimation device for estimating the ozone concentration in water, an ozone water concentration estimation method, and a membrane separation activated sludge system.

[0002] Ozone water is used in a wide range of fields, including sterilization, deodorization, decolorization, algae removal from water, and as an oxidizer. In one example, ozone water is used for backwashing, a process in which cleaning water is injected into the separation membrane in the opposite direction to the filtration direction, to prevent a decline in filtration performance in a membrane bioreactor (MBR) process, which uses activated sludge to decompose organic matter in the water being treated and then performs solid-liquid separation by filtration using a separation membrane. This ozone water is generated in an ozone water generator. The ozone water concentration, which is the concentration of ozone dissolved in the ozone water generated by the ozone water generator, is monitored, and backwashing is performed with ozone water after the ozone water concentration reaches a predetermined value.

[0003] One method for measuring the concentration of ozone-treated water is ultraviolet absorption spectroscopy. Patent Document 1 discloses an ultraviolet absorbance measuring device that accurately measures the ultraviolet absorbance of organic matter in ozone-treated water by correcting for the effects of dissolved ozone in the ozone-treated water. The ultraviolet absorbance measuring device described in Patent Document 1 includes a dissolved ozone concentration measuring unit that measures the dissolved ozone concentration in the ozone-treated water, an absorbance measuring unit that measures a first ultraviolet absorbance of the ozone-treated water at an absorption wavelength λ of the organic matter, a conversion unit that converts the measured dissolved ozone concentration value into a second ultraviolet absorbance at wavelength λ, and a calculation unit that corrects the first ultraviolet absorbance value obtained by the absorbance measuring unit with the second ultraviolet absorbance obtained by the conversion unit to calculate the ultraviolet absorbance of the ozone-treated water due to absorption by the organic matter.

[0004] Japanese Patent Application Publication No. 6-221998

[0005] However, impurities such as organic matter exist in ozone-treated water, which can interfere with the measurement of the ozone-water concentration. For this reason, as shown in the ultraviolet absorbance measuring device described in Patent Document 1, the device configuration becomes complex and expensive in order to eliminate the influence of impurities in the ozone-treated water. As a result, it may not be easy to install an ultraviolet absorbance measuring device for measuring the ozone-water concentration in each ozone-water generating device. Therefore, there has been a demand for a technology that can accurately monitor the ozone-water concentration with a simple configuration, even when impurities that interfere with the measurement are present in the ozone-treated water.

[0006] The present disclosure has been made in consideration of the above, and aims to provide an ozone water concentration estimation device that can monitor the ozone water concentration with a simpler configuration than conventional devices, even if impurities are present in the ozone water.

[0007] To solve the above-mentioned problems and achieve the object, the ozonated water concentration estimation device of the present disclosure includes a first data acquisition unit, an estimation unit, and a data output unit. The first data acquisition unit acquires first generation environmental data including at least one of the following: the ozone gas concentration of ozone gas supplied to an ozonated water generation tower that dissolves ozone gas in water to generate ozonated water; the ozone gas flow rate of the ozone gas supplied to the ozonated water generation tower; the temperature of the ozonated water stored in the ozonated water generation tower; the hydrogen ion exponent (pH) of the ozonated water; and the amount of the ozonated water stored. The estimation unit uses a numerical model to estimate the ozonated water concentration, which is the concentration of ozone dissolved in the ozonated water at the time the first generation environmental data was measured, to estimate an ozonated water concentration estimate, which is an estimate of the ozone water concentration in the ozonated water at the time the first generation environmental data was measured. The data output unit outputs the ozonated water concentration estimate.

[0008] According to the present disclosure, it is possible to monitor the concentration of ozone water with a simpler configuration than conventional methods, even if impurities are present in the ozone water.

[0009] FIG. 1 is a diagram schematically showing an example of the configuration of an ozone water generation system equipped with an ozone water concentration estimation device according to embodiment 1; a flowchart showing an example of the procedure of an ozone water concentration estimation method according to embodiment 1; FIG. 2 is a diagram schematically showing an example of the configuration of a membrane separation activated sludge system having an ozone water generation system; FIG. 3 is a diagram showing an example of a computer system that realizes the ozone water concentration estimation device according to embodiment 1; FIG. 4 is a diagram schematically showing an example of the configuration of an ozone water generation system equipped with an ozone water concentration estimation device according to embodiment 2; FIG. 10 is a flowchart showing an example of the procedure for an ozone water concentration estimation method according to embodiment 3. FIG. 11 is a diagram showing a schematic example of the configuration of an ozone water generation system equipped with an ozone water concentration estimation device according to embodiment 4. FIG. 12 is a diagram showing a schematic example of the configuration of an ozone water generation system equipped with an ozone water concentration estimation device according to embodiment 5. FIG. 13 is a flowchart showing an example of the procedure for a method of constructing a numerical model in an ozone water concentration estimation device according to embodiment 5. FIG. 14 is a diagram showing a schematic example of the configuration of an ozone water generation system equipped with an ozone water concentration estimation device according to embodiment 6. FIG. 15 is a diagram showing a schematic example of a neural network used by a model generation unit.

[0010] An ozone water concentration estimation device, an ozone water concentration estimation method, and a membrane separation activated sludge system according to embodiments of the present disclosure will be described in detail below with reference to the drawings.

[0011] 1 is a diagram schematically illustrating an example of the configuration of an ozone water generation system including an ozone water concentration estimation device according to embodiment 1. The ozone water generation system 1 is a system for generating ozone water, which is water having ozone gas dissolved therein. The ozone water generation system 1 includes an ozone water generation device 10 that generates ozone water, and an ozone water concentration estimation device 30 that estimates the concentration of the ozone water generated by the ozone water generation device 10.

[0012] The ozone water generating apparatus 10 includes a dissolved water supply section 11, an ozone generator 12, an ozone gas concentration meter 13, an ozone gas flow meter 14, an ozone water generating tower 15, a thermometer 16, a pH meter 17, a water level meter 18, an exhaust ozone gas decomposition tower 19, an ozone water conveying section 20, and a control device 21.

[0013] The dissolving water supply unit 11 is, for example, a tank that stores dissolving water, which is water to dissolve ozone in. The dissolving water supply unit 11 supplies the dissolving water to the ozone water production tower 15 via a pipe.

[0014] The dissolution water supplied from the dissolution water supply unit 11 is not particularly limited. For example, tap water, industrial water, or filtered water obtained through a membrane bioreactor activated sludge system can be used. The dissolution water is preferably adjusted to a pH of 2 or higher and 6 or lower. Because the autolysis of ozone is more suppressed at lower pH levels, lowering the pH of the dissolution water to within the above range in advance can reduce the cost required for generating ozone water in the ozone water generator 10. Furthermore, the dissolution water may be temperature-adjusted. Because the solubility of ozone in dissolution water increases with lower water temperature, lowering the temperature of the dissolution water in advance can reduce the cost required for generating ozone water in the ozone water generator 10.

[0015] The ozone generator 12 generates ozone gas and supplies the ozone gas to the ozone water generation tower 15. The ozone generator 12 is, for example, an ozone generator that generates ozone gas using oxygen generated by a pressure swing adsorption (PSA) method or a pressure vacuum swing adsorption (PVSA) method, liquid oxygen, or the like as a raw material.

[0016] The ozone gas concentration meter 13 measures the ozone gas concentration, which is the concentration of the ozone gas generated by the ozone generator 12, and outputs the measurement result of the ozone gas concentration to the ozone water concentration estimation device 30. The ozone gas concentration meter 13 is one of the measuring units that measures the ozone gas concentration of the ozone gas supplied inside the ozone water production tower 15.

[0017] The ozone gas flow meter 14 measures the ozone gas flow rate, which is the flow rate of the ozone gas generated by the ozone generator 12, and outputs the measurement result of the ozone gas flow rate to the ozone water concentration estimation device 30. The ozone gas flow meter 14 is one of the measuring units that measures the ozone gas flow rate of the ozone gas supplied into the ozone water production tower 15.

[0018] The ozonated water production tower 15 stores the dissolving water supplied from the dissolving water supply unit 11 and dissolves ozone gas supplied from the ozone generator 12 into the stored dissolving water to produce ozonated water. For example, the ozonated water production tower 15 dissolves ozone gas into the dissolving water by an ejector system, an aeration system, a dissolution film system, or the like. For example, stainless steel or a fluorine-based resin compound is used as the material for the ozonated water production tower 15. Stainless steel and a fluorine-based resin compound are preferred materials for the ozonated water production tower 15 because they have excellent resistance to ozone. The surface of the container for the ozonated water production tower 15 may also be coated with a fluorine-based resin compound.

[0019] The thermometer 16 measures the water temperature, which is the temperature of the ozone water stored inside the ozone water generation tower 15, and outputs the measurement result of the water temperature to the ozone water concentration estimation device 30. The thermometer 16 is one of the measuring units that measures the water temperature of the ozone water stored inside the ozone water generation tower 15.

[0020] The pH meter 17 measures the pH of the ozone water stored inside the ozone water generation tower 15 and outputs the measurement result, that is, the measured pH value, to the ozone water concentration estimation device 30. The pH meter 17 is one of the measuring units that measures the pH of the ozone water stored inside the ozone water generation tower 15.

[0021] The water level meter 18 measures the water level of the ozone water stored inside the ozone water generation tower 15 and outputs the measurement result, that is, the measured water level, to the ozone water concentration estimation device 30. Note that here, the water level meter 18 measures the water level of the ozone water stored inside the ozone water generation tower 15, but the water level meter 18 may be any meter that can measure the amount of ozone water stored inside the ozone water generation tower 15. The water level measured by the water level meter 18 corresponds to the amount of ozone water stored inside the ozone water generation tower 15. The water level meter 18 is one of the measuring units that measures the amount of ozone water stored inside the ozone water generation tower 15.

[0022] In the exhaust ozone gas decomposition tower 19, ozone gas that has not been dissolved in the ozone water in the ozone water production tower 15 is brought into contact with a catalyst to be decomposed into oxygen, and the oxygen is discharged outside the system, i.e., into the atmosphere. Activated carbon, manganese oxide, etc. are used as the catalyst.

[0023] The ozonated water supply unit 20 supplies the ozonated water generated in the ozonated water generation tower 15 to the outside. The ozonated water supply unit 20 includes, for example, an electromagnetic or pneumatic automatic valve (not shown) and a pump (not shown). In one example, the ozonated water supply unit 20 controls the operation of the pump and the opening and closing of the valve in accordance with instructions from the control device 21, thereby starting and stopping the supply of ozonated water to the outside.

[0024] The control device 21 controls the operation of the dissolver water supply unit 11, the ozone generator 12, the ozonated water production tower 15, and the ozone water concentration estimation device 30. When starting to produce ozone water, the control device 21 starts the operation of the dissolver water supply unit 11, the ozone generator 12, the ozonated water production tower 15, and the ozone water concentration estimation device 30. When producing ozone water, the control device 21 issues an instruction to the ozonated water delivery unit 20 to deliver or stop delivering ozone water, and controls the supply of ozone water to the outside. When finishing producing ozone water, the control device 21 stops the operation of the dissolver water supply unit 11, the ozone generator 12, the ozone water production tower 15, and the ozone water concentration estimation device 30.

[0025] The ozone water concentration estimation device 30 estimates the ozone water concentration, which is the concentration of ozone water generated in the ozone water generation tower 15, from first generation environment data including at least one of ozone gas concentration, ozone gas flow rate, water temperature, pH, and water level. The ozone water concentration estimation device 30 has a data acquisition unit 31, an estimation unit 32, and a data output unit 33. The ozone water concentration is also referred to as dissolved ozone concentration.

[0026] The data acquisition unit 31 acquires first generation environment data, which is data indicating the environment during the generation of ozone water in the ozone water generation tower 15, and inputs the acquired first generation environment data to the estimation unit 32. The first generation environment data includes at least one of the ozone gas concentration and ozone gas flow rate of ozone gas supplied to the ozone water generation tower 15, which generates ozone water by dissolving ozone gas in water to be dissolved, and the water temperature, pH, and storage volume of ozone water stored in the ozone water generation tower 15. The first generation environment data is also online data obtained via wiring or the like from at least one of the ozone gas concentration meter 13, ozone gas flow meter 14, thermometer 16, pH meter 17, and water level meter 18. That is, the first generation environment data includes at least one of the ozone gas concentration, ozone gas flow rate, water temperature, pH, and water level. The data acquisition unit 31 corresponds to the first data acquisition unit.

[0027] When the data acquisition unit 31 acquires an ozone gas concentration from the ozone gas concentration meter 13 as the first generation environmental data, it inputs the ozone gas concentration to the estimation unit 32. When the data acquisition unit 31 acquires an ozone gas flow rate from the ozone gas flow meter 14 as the first generation environmental data, it inputs the ozone gas flow rate to the estimation unit 32. When the data acquisition unit 31 acquires a water temperature from the thermometer 16 as the first generation environmental data, it inputs the water temperature to the estimation unit 32. When the data acquisition unit 31 acquires a pH from the pH meter 17 as the first generation environmental data, it inputs the pH to the estimation unit 32. When the data acquisition unit 31 acquires a water level from the water level meter 18 as the first generation environmental data, it inputs the water level to the estimation unit 32.

[0028] The estimation unit 32 estimates an ozone water concentration estimate, which is an estimate of the ozone water concentration in the ozone water at the time of measurement of the first generating environmental data input from the data acquisition unit 31, using a numerical model that estimates the ozone water concentration, which is the concentration of ozone dissolved in the ozone water at the time of measurement of the first generating environmental data, from the first generating environmental data. The estimation unit 32 outputs the estimated ozone water concentration estimate to the data output unit 33. The numerical model is a function that indicates the relationship between the first generating environmental data, which includes at least one of data items of ozone gas concentration, ozone gas flow rate, water temperature, pH, and water level, and the ozone water concentration at the time the first generating environmental data was measured, and is constructed in advance. The ozone water concentration is obtained by an operator analyzing the ozone water obtained at the time the first generating environmental data was measured. The numerical model used in the estimation unit 32 is a function that explains the relationship between the first generation environment data, which is an explanatory variable that can be measured online by the ozone water generation device 10, and the ozone water concentration, which is a target variable that is measured offline of the ozone water generated by the ozone water generation device 10.

[0029] The numerical model may estimate the concentration of ozone water generated inside the ozone water generation tower 15 according to physical laws using at least one of ozone gas concentration, ozone gas flow rate, water temperature, pH, and water level, or may estimate the concentration of ozone water generated inside the ozone water generation tower 15 by machine learning using at least one of ozone gas concentration, ozone gas flow rate, water temperature, pH, and water level. In such a numerical model, the accuracy of the ozone water concentration estimate can be improved by using ozone gas concentration, ozone gas flow rate, water temperature, pH, and water level as variables. However, if the ozone water generation apparatus 10 is in an environment where some of the first generation environmental data remains constant over time, some of the variables that are the first generation environmental data can be treated as constants. For example, if the ozone gas concentration, ozone gas flow rate, water temperature, and pH remain constant over time and the water level fluctuates over time, a numerical model can be used in which the ozone gas concentration, ozone gas flow rate, water temperature, and pH are treated as constants and the water level is used as a parameter.

[0030] In this way, by using a numerical model that inputs the first generation environment data, which is an explanatory variable, the estimation unit 32 is able to accurately estimate the ozone water concentration, which is the target variable of the ozone water generated by the ozone water generation device 10.

[0031] The data output unit 33 outputs the ozone-water concentration estimated value estimated by the estimation unit 32. In one example, the data output unit 33 outputs the ozone-water concentration estimated value to a display (not shown) connected to a higher level of the ozone-water concentration estimation device 30 or the control device 21. The display displays the input ozone-water concentration estimated value or graphically displays the ozone-water concentration estimated value estimated within a predetermined period. In another example, the data output unit 33 outputs the ozone-water concentration estimated value to the control device 21. In still another example, the data output unit 33 outputs the ozone-water concentration estimated value to another device (not shown) that uses ozone water. The other device monitors the ozone-water concentration estimated value from the ozone-water concentration estimation device 30, and when the ozone-water concentration estimated value reaches a predetermined value, receives a supply of ozone water from the ozone-water supply unit 20 and performs processing using the ozone water.

[0032] Next, a method for estimating the concentration of ozone water in the ozone water generation system 1 will be described. Fig. 2 is a flowchart showing an example of the procedure of the method for estimating the concentration of ozone water according to the first embodiment. Note that, in this example, the operation of the ozone water generation system 1 is started and stopped by signals indicating the start and end of operation from the control device 21.

[0033] The data acquisition unit 31 of the ozone-water concentration estimation device 30 receives an operation start signal from the control device 21 indicating the start of operation of the ozone-water generation system 1 (step S11). Each component of the ozone-water generation device 10 starts operation in response to the operation start signal. Each measurement unit of the ozone-water generation device 10 also starts measuring first generation environmental data and transmits the measured first generation environmental data to the ozone-water concentration estimation device 30. The data acquisition unit 31 of the ozone-water concentration estimation device 30 then acquires the first generation environmental data from each measurement unit (step S12). The data acquisition unit 31 may acquire the first generation environmental data from at least one of the ozone gas concentration meter 13, ozone gas flow meter 14, thermometer 16, pH meter 17, and water level meter 18. The acquired first generation environmental data is determined based on parameters used in the numerical model held by the estimation unit 32. The processing of step S12 corresponds to a data acquisition step.

[0034] Next, the data acquisition unit 31 inputs the acquired first generation environmental data to the estimation unit 32 (step S13). The estimation unit 32 calculates an estimated ozone-water concentration value by inputting the first generation environmental data into a numerical model (step S14), and outputs the calculated estimated ozone-water concentration value to the data output unit 33 (step S15). The processing from step S13 to step S15 corresponds to an estimation step of estimating, from the first generation environmental data, an estimated ozone-water concentration value in the ozone-water at the time of measurement of the first generation environmental data acquired in the data acquisition step, using a numerical model that estimates the ozone-water concentration of the ozone-water at the time of measurement of the first generation environmental data.

[0035] The data output unit 33 transmits the estimated ozone water concentration value to the specified device (step S16). The processing of step S16 corresponds to a data output process. By receiving the estimated ozone water concentration value, the specified device can monitor the concentration of the ozone water being generated by the ozone water generation device 10.

[0036] Thereafter, the data acquisition unit 31 determines whether an operation end signal indicating the end of operation of the ozone water production system 1 has been received from the control device 21 (step S17). If the operation end signal has not been received (No in step S17), the process returns to step S12, and steps S12 to S16 are repeatedly executed until the operation end signal is received. If the operation end signal has been received (Yes in step S17), the ozone water concentration estimation method ends.

[0037] Next, an application example of the ozonated water generation system 1 will be described. Ozone water is used in a wide range of fields, including sterilization, deodorization, decolorization, algae removal in water, and as an oxidant. Here, we will describe the application of the ozonated water generation system 1 to an MBR. In an MBR, activated sludge is used to decompose organic matter in the water to be treated, and solid-liquid separation is performed by filtration using a separation membrane. However, with continued use, contaminants adhere to the surface or pores of the separation membrane, causing clogging and gradually deteriorating the filtration performance. For this reason, the ozonated water generation system 1 is installed in the MBR, and the separation membrane is cleaned by performing backflow cleaning, in which ozonated water is injected into the separation membrane in the direction opposite to the filtration direction.

[0038] Figure 3 is a diagram schematically showing an example of the configuration of a membrane separation activated sludge system having an ozone water production system. The same components as those described in Figure 1 are designated by the same reference numerals, and their description will be omitted. The membrane separation activated sludge system 70 includes an ozone water production system 1 and a membrane separation activated sludge device 71. The membrane separation activated sludge system 70 further includes an inlet pipe 72, an ozone water pipe 73, a filtered water pipe 74, and a filtration pump 75.

[0039] The membrane bioreactor 71 is an apparatus that performs solid-liquid separation of water to be treated 80 containing pollutants by filtration using a separation membrane 71b. The membrane bioreactor 71 has a membrane separation tank 71a and a separation membrane 71b that is a part to be cleaned. The membrane separation tank 71a stores the water to be treated 80 that flows in from an aeration tank (not shown) that performs biological treatment using activated sludge via an inlet pipe 72. The inlet pipe 72 is, for example, a pipe that connects the aeration tank that stores the water to be treated 80 to the membrane separation tank 71a.

[0040] The separation membrane 71b is disposed in the membrane separation tank 71a and is immersed in the water to be treated 80. The separation membrane 71b separates the water to be treated 80 into activated sludge and filtrate. The activated sludge captures pollutants in the water to be treated 80. The filtrate is the water to be treated 80 from which the pollutants have been removed.

[0041] The water to be treated 80 is supplied to the separation membrane 71b, where it is separated into activated sludge and filtrate, and then the filtrate is guided to the filtrate pipe 74 by the operation of the filtration pump 75. The filtrate pipe 74 connects the separation membrane 71b to a filtrate tank (not shown in the figure) outside the membrane separation tank 71a, and is a pipe that discharges the filtrate after being filtered by the separation membrane 71b to the outside of the membrane separation tank 71a. The filtration pump 75 uses pressure to promote the discharge of the filtrate.

[0042] As the filtration process of the water to be treated 80 continues, activated sludge and pollutants may adhere to the surface or pores of the separation membrane 71b, causing clogging. If the separation membrane 71b becomes clogged, the filtration rate of the water to be treated 80 through the separation membrane 71b decreases, and the water treatment efficiency using the membrane separation activated sludge system 70 may decrease. For this reason, in the membrane separation activated sludge system 70, the separation membrane 71b is periodically cleaned. In the membrane separation activated sludge system 70, the separation membrane 71b is cleaned using ozone water generated by the ozone water generator 10.

[0043] The ozonated water supply unit 20 of the ozonated water generator 10 supplies the ozonated water generated in the ozonated water generation tower 15 to the outside. The ozonated water supply unit 20 includes, for example, an electromagnetic or pneumatic automatic valve (not shown) and a pump (not shown). The ozonated water supply unit 20 supplies the ozonated water generated in the ozonated water generation tower 15 to the separation membrane 71b via an ozonated water pipe 73. The ozonated water pipe 73 connects the ozonated water supply unit 20 to a filtered water pipe 74 and supplies the ozonated water generated in the ozonated water generation tower 15 to the separation membrane 71b. The ozonated water supplied from the ozonated water supply unit 20 flows through the separation membrane 71b via the ozonated water pipe 73 and the filtered water pipe 74, cleaning the separation membrane 71b.

[0044] In such a membrane bioreactor activated sludge system 70, when the separation membrane 71b of the membrane bioreactor activated sludge apparatus 71 is cleaned, when the estimated ozone water concentration in the ozone water production tower 15 estimated by the ozone water concentration estimating device 30 reaches a predetermined value, the control device 21 controls the ozone water conveying unit 20 to supply ozone water to the separation membrane 71b. Specifically, the control device 21 controls a valve (not shown) of the ozone water conveying unit 20 to open and controls a pump (not shown) of the ozone water conveying unit 20 to turn on, so that the ozone water is supplied from the ozone water conveying unit 20 to the separation membrane 71b via the ozone water piping 73 and the filtered water piping 74. The separation membrane 71b is cleaned by the ozone water being conveyed to the separation membrane 71b.

[0045] In the above description, the ozone water concentration estimation device 30 acquires measurement data from each measurement unit of the ozone water generation apparatus 10 and estimates the ozone water concentration in the ozone water generation tower 15. However, the ozone water concentration estimation device 30 may be installed in the same installation location as the ozone water generation apparatus 10 or in a location physically separated from the installation location of the ozone water generation apparatus 10. In one example, a single ozone water concentration estimation device 30 may be configured to estimate the ozone water concentrations of multiple ozone water generation apparatuses 10. Specifically, measurement data from each measurement unit of the ozone water generation apparatus 10 may be transmitted from a transmitter of the ozone water generation apparatus 10 to the ozone water concentration estimation device 30 via a communication line such as a network. The estimated ozone water concentration value estimated by the ozone water concentration estimation device 30 may be received by a receiver of the ozone water generation apparatus 10 via a communication line such as a network and input to the control device 21. The control device 21 then controls the operation of the ozone water supply unit 20 based on the estimated ozone water concentration value. With this configuration, there is no need to provide an ozone water concentration estimation device 30 for each ozone water generation device 10, and it is possible to monitor the ozone water concentration of ozone water generated by multiple ozone water generation devices 10 using a single ozone water concentration estimation device 30.

[0046] Next, the hardware configuration of the ozone-water concentration estimation device 30 will be described. The ozone-water concentration estimation device 30 is realized by a computer system. Fig. 4 is a diagram showing an example of a computer system that realizes the ozone-water concentration estimation device according to the first embodiment. The computer system shown in Fig. 4 includes a processor 101, a memory 102, a communication circuit 103, a display unit 104, and an input unit 105.

[0047] The processor 101, which is an arithmetic device, is, for example, a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a microprocessor, a microcontroller, a DSP (Digital Signal Processor), etc. The memory 102, which is a storage unit, is, for example, a semiconductor memory such as a RAM (Random Access Memory), a ROM (Read Only Memory), a flash memory, an EPROM (Erasable Programmable Read Only Memory), an EEPROM (Electrically Erasable Programmable Read Only Memory), a magnetic disk, a flexible disk, etc. The communication circuit 103 is a transceiver capable of communication.

[0048] The display unit 104 is a display, a monitor, etc., and the input unit 105 is a button, a switch, a keyboard, a mouse, etc. A touch panel in which the display unit 104 and the input unit 105 are integrated may also be used.

[0049] The ozone-water concentration estimation device 30 is realized by executing a computer program that describes the processes to be executed by the ozone-water concentration estimation device 30. Specifically, the program is installed in the memory 102. When the program is executed, the program is read from the memory 102 and stored in a primary storage area of ​​the memory 102. In this state, the processor 101 executes the processes of the ozone-water concentration estimation device 30 according to the first embodiment in accordance with the program stored in the memory 102. The program may be provided by a recording medium or by a transmission medium via the communication circuit 103. The data acquisition unit 31 and the data output unit 33 shown in FIG. 1 are realized by the communication circuit 103 or the input unit 105 shown in FIG. 4.

[0050] The ozone water concentration estimation device 30 shown in Fig. 1 is realized by a processor 101 executing a program stored in a memory 102 shown in Fig. 4. The memory 102 is also used to realize the ozone water concentration estimation device 30.

[0051] The ozone water concentration estimation device 30 according to the first embodiment includes a data acquisition unit 31 that inputs first generation environmental data to an estimation unit 32, the data including at least one of the ozone gas concentration and ozone gas flow rate of the ozone gas supplied to the ozone water generation tower 15, and the temperature, pH, and water level of the ozone water stored in the ozone water generation tower 15, the estimation unit 32 that estimates an estimated value of the ozone water concentration in the ozone water at the time of measurement of the first generation environmental data input from the data acquisition unit 31 using a numerical model that estimates the ozone water concentration at the time of measurement of the first generation environmental data, and a data output unit 33 that outputs the estimated value of the ozone water concentration. This allows the ozone water concentration to be estimated from at least one measured value of the ozone gas concentration and ozone gas flow rate of the ozone gas supplied to the ozone water generation tower 15, and the temperature, pH, and storage volume of the ozone water stored inside the ozone water generation tower 15, so that the ozone water concentration can be accurately estimated even if impurities are present in the ozone water.

[0052] Furthermore, when estimating the ozone water concentration, at least one of the measured values ​​of the ozone gas concentration supplied to the ozone water generation tower 15, the ozone gas flow rate, and the temperature, pH, and water level of the ozone water stored in the ozone water generation tower 15 are normally measured when ozone water is generated in the ozone water generation apparatus 10. Therefore, simply adding the ozone water concentration estimation device 30 to the configuration of the conventional ozone water generation apparatus 10 makes it possible to estimate the ozone water concentration. In particular, these measured values ​​are data referenced when dissolving ozone gas generated by the ozone generator 12 in water to be dissolved, and are considered to be closely related to the ozone water concentration. For this reason, in the first embodiment, these measured values ​​are used to estimate the ozone water concentration.

[0053] Furthermore, even if impurities such as suspended particles are present in the ozonated water, a numerical model generated using a combination of the first generation environment data measured online in this state and the ozonated water concentration measured offline in this state, i.e., by analysis by an operator, is used to estimate the ozonated water concentration. In other words, a numerical model showing the relationship between the first generation environment data in a state in which impurities are present in the ozonated water and the actual ozonated water concentration of the ozonated water stored inside the ozonated water generation tower 15 is used to estimate the ozonated water concentration. This has the effect of enabling the ozonated water concentration to be monitored more accurately than before, even when impurities are present in the ozonated water.

[0054] Furthermore, if any of the ozone gas concentration, ozone gas flow rate, ozone water temperature, pH, and water level does not change over time or is controlled to be constant, the constant measured values ​​in the numerical model can be treated as constants rather than variables. Alternatively, the measured values ​​of the first generation environmental data that do not change over time are treated as constants rather than variables, and the measured values ​​that change over time are treated as variables. This allows an ozone water concentration estimate to be estimated by acquiring at least one piece of first generation environmental data. This reduction in the number of variables reduces the computational load of the ozone water concentration estimate in the ozone water concentration estimation device 30. Even with a reduced number of variables, the numerical model is constructed using these measured values, so the ozone water concentration can be estimated with the same accuracy as when five variables (ozone gas concentration, ozone gas flow rate, ozone water temperature, pH, and water level) are used.

[0055] Embodiment 2. Fig. 5 is a diagram showing a schematic diagram of an example of the configuration of an ozone water production system including an ozone water concentration estimation device according to embodiment 2. Note that the same components as those in embodiment 1 are given the same reference numerals, and their description will be omitted.

[0056] In the ozone water generation system 1a according to the second embodiment, the configuration of the ozone water generation device 10a is different from that of the first embodiment. That is, the ozone water generation device 10a further includes an organic matter concentration meter 22. The organic matter concentration meter 22 measures the organic matter concentration, which is the concentration of organic matter in the ozone water stored in the ozone water generation tower 15. The organic matter concentration meter 22 transmits the measured organic matter concentration to the data acquisition unit 31 of the ozone water concentration estimation device 30. The organic matter is a measurement object that affects the ozone water concentration, and the organic matter concentration is a measurement value of the measurement object and corresponds to the second generation environment data.

[0057] When measuring total organic carbon (TOC) as the organic matter concentration, a TOC meter can be used as the organic matter concentration meter 22. When measuring the concentration of humic substances that exhibit significant absorption at a wavelength of around 260 nm in ultraviolet light as the organic matter concentration, an absorptiometer can be used as the organic matter concentration meter 22. When measuring turbidity as the organic matter concentration, a turbidimeter can be used as the organic matter concentration meter 22.

[0058] The data acquisition unit 31 of the ozone-water concentration estimation device 30 acquires, in addition to first generation environment data of at least one of ozone gas concentration, ozone gas flow rate, temperature, pH, and water level of the ozone water, second generation environment data of organic matter concentration. That is, the data acquisition unit 31 acquires measurements from at least one of the ozone gas concentration meter 13, ozone gas flow rate meter 14, thermometer 16, pH meter 17, and water level meter 18, as well as measurements from the organic matter concentration meter 22. The data acquisition unit 31 inputs the acquired first generation environment data and organic matter concentration to the estimation unit 32.

[0059] The estimation unit 32 holds a numerical model for estimating the ozone-water concentration at the time of measurement of the first generation environmental data and the second generation environmental data from the first generation environmental data and the second generation environmental data, and uses the numerical model to estimate an estimated value of the ozone-water concentration at the time of measurement of the first generation environmental data and the second generation environmental data input from the data acquisition unit 31. That is, the estimation unit 32 holds a numerical model showing the relationship between the first generation environmental data including at least one of the ozone gas concentration, ozone gas flow rate, temperature, pH, and water level of the ozone-water measured online, and the organic matter concentration, which is the second generation environmental data, and the ozone-water concentration of the ozone-water stored inside the ozone-water generation tower 15, measured offline. The estimation unit 32 inputs the first generation environmental data and the organic matter concentration from the data acquisition unit 31 into the numerical model, thereby being able to estimate the ozone-water concentration for a combination of the first generation environmental data and the organic matter concentration.

[0060] In the second embodiment, organic matter concentration is added as a variable in the numerical model. This is because organic matter in ozonated water reacts with ozone, resulting in a decrease in ozonated water concentration due to the consumption of ozone, and organic matter concentration is a parameter that reflects the quality of the ozonated water. Furthermore, while the first generation environmental data, i.e., ozone gas concentration, ozone gas flow rate, ozonated water temperature, pH, and water level, shown in the first embodiment can be adjusted by the ozonated water generator 10a, organic matter concentration cannot be adjusted. Therefore, organic matter concentration is used as a variable in the numerical model in addition to the first generation environmental data. By using a numerical model that takes organic matter concentration into account in this way, the reaction between organic matter and ozone in ozonated water is taken into account, thereby improving the accuracy of estimating the ozonated water concentration compared to the first embodiment, where organic matter concentration is not used as a variable.

[0061] Fig. 6 is a flowchart showing an example of the procedure of the ozone water concentration estimation method according to embodiment 2. Note that the same steps as those in the flowchart of Fig. 2 according to embodiment 1 are given the same step numbers, and their explanations will be omitted.

[0062] In the second embodiment, after step S12, the data acquisition unit 31 of the ozone water concentration estimation device 30 acquires the organic matter concentration from the organic matter concentration meter 22 (step S21). Here, the processing of step S21 is performed after step S12, but it may be performed before step S12 or in parallel with the processing of step S12.

[0063] In the second embodiment, the process of step S13 is replaced with the process of step S13a, and the process of step S14 is replaced with the process of step S14a. That is, the data acquisition unit 31 inputs the acquired first generation environment data and organic substance concentration to the estimation unit 32 (step S13a). Thereafter, the estimation unit 32 inputs the first generation environment data and organic substance concentration into a numerical model to calculate an estimated ozone water concentration (step S14a). Thereafter, the processes from step S15 onward are performed.

[0064] In the second embodiment, the estimation unit 32 calculates the estimated ozone-water concentration by inputting the input first generation environment data and the organic matter concentration into a numerical model. In this way, by adding the measurement data of the organic matter concentration to the estimation of the ozone-water concentration using the numerical model, it is possible to reduce the error between the estimated ozone-water concentration and the actual ozone-water concentration that occurs due to changes in water quality.

[0065] Embodiment 3. Fig. 7 is a diagram showing a schematic diagram of an example of the configuration of an ozone water generation system including an ozone water concentration estimation device according to embodiment 3. Note that the same components as those in embodiment 1 are given the same reference numerals, and their description will be omitted.

[0066] In an ozone water generation system 1b according to the third embodiment, the configuration of an ozone water generation apparatus 10b is different from that of the first embodiment. That is, the ozone water generation apparatus 10b further includes an exhaust ozone gas concentration meter 23. The exhaust ozone gas concentration meter 23 is provided between the ozone water generation tower 15 and the exhaust ozone gas decomposition tower 19. The exhaust ozone gas concentration meter 23 measures the exhaust ozone gas concentration, which is the ozone gas concentration of the exhaust ozone gas discharged from the ozone water generation tower 15. The exhaust ozone gas concentration is the concentration of the exhaust ozone gas discharged from the ozone water generation tower 15 without dissolving in ozone water. The exhaust ozone gas concentration meter 23 transmits the measured exhaust ozone gas concentration to a data acquisition unit 31 of an ozone water concentration estimation device 30. The exhaust ozone gas is a measurement object that affects the ozone water concentration, and the exhaust ozone gas concentration is a measurement value of the measurement object and corresponds to the second generation environment data. As an example of a method for measuring the concentration of exhausted ozone gas by the exhausted ozone gas concentration meter 23, a method for measuring the concentration of exhausted ozone gas by measuring the absorbance of ultraviolet light absorbed by ozone may be used.

[0067] The data acquisition unit 31 of the ozone water concentration estimation device 30 acquires, in addition to first generation environment data of at least one of ozone gas concentration, ozone gas flow rate, temperature, pH, and water level of the ozone water, second generation environment data of exhaust ozone gas concentration. That is, the data acquisition unit 31 acquires a measurement value of at least one of the ozone gas concentration meter 13, ozone gas flow rate meter 14, thermometer 16, pH meter 17, and water level meter 18, as well as a measurement value of the exhaust ozone gas concentration meter 23. The data acquisition unit 31 inputs the acquired first generation environment data and exhaust ozone gas concentration to the estimation unit 32.

[0068] The estimation unit 32 holds a numerical model for estimating the ozone water concentration at the time of measurement of the first generation environmental data and the second generation environmental data from the first generation environmental data and the second generation environmental data, and uses the numerical model to estimate an estimated value of the ozone water concentration at the time of measurement of the first generation environmental data and the second generation environmental data input from the data acquisition unit 31. That is, the estimation unit 32 holds a numerical model showing the relationship between the first generation environmental data including at least one of the ozone gas concentration, ozone gas flow rate, water temperature, pH, and water level of the ozone water measured online, and the exhaust ozone gas concentration, which is the second generation environmental data, and the ozone water concentration of the ozone water stored inside the ozone water generation tower 15, measured offline. The estimation unit 32 inputs the first generation environmental data and the exhaust ozone gas concentration from the data acquisition unit 31 into the numerical model, thereby being able to estimate the estimated concentration of ozone water for a combination of the first generation environmental data and the exhaust ozone gas concentration.

[0069] In the third embodiment, the exhaust ozone gas concentration is further added as a variable of the numerical model. This is because the exhaust ozone gas concentration is a parameter that reflects the water quality of the ozonated water generated in the ozonated water generation tower 15. Specifically, when the dissolved ozone concentration of the ozonated water generated in the ozonated water generation tower 15 is low, the amount of exhaust ozone gas discharged without being dissolved in the ozonated water is small, and the exhaust ozone gas concentration is low. On the other hand, when the dissolved ozone concentration of the ozonated water generated in the ozonated water generation tower 15 is high, the amount of exhaust ozone gas discharged without being dissolved in the ozonated water increases, and the exhaust ozone gas concentration becomes high. Because such a relationship exists between the exhaust ozone gas concentration and the dissolved ozone concentration, using the exhaust ozone gas concentration as a variable of the numerical model makes it possible to estimate the ozonated water concentration with even greater accuracy than in the first embodiment.

[0070] Furthermore, while the first generation environmental data shown in embodiment 1, namely, ozone gas concentration, ozone gas flow rate, temperature, pH, and water level of the ozone water, can be adjusted by the ozone water generation apparatus 10b, the exhaust ozone gas concentration cannot be adjusted, and therefore the exhaust ozone gas concentration is used as a variable in the numerical model in addition to the first generation environmental data. The level of ozone gas taken into the ozone water changes depending on the level of the exhaust ozone gas concentration, and this is reflected in the quality of the ozone water. Therefore, compared to when the exhaust ozone gas concentration is not used as a variable as in embodiment 1, the use of a numerical model that takes the exhaust ozone gas concentration into account can improve the accuracy of estimating the ozone water concentration.

[0071] Fig. 8 is a flowchart showing an example of the procedure of the ozone water concentration estimation method according to embodiment 3. Note that the same steps as those in the flowchart of Fig. 2 of embodiment 1 are given the same step numbers, and their explanations will be omitted.

[0072] In the third embodiment, after step S12, the data acquisition unit 31 of the ozone water concentration estimation device 30 acquires the exhausted ozone gas concentration from the exhausted ozone gas concentration meter 23 (step S31). Here, the processing of step S31 is performed after step S12, but it may be performed before step S12 or in parallel with the processing of step S12.

[0073] In the third embodiment, the process of step S13 is replaced with the process of step S13b, and the process of step S14 is replaced with the process of step S14b. That is, the data acquisition unit 31 inputs the acquired first generation environment data and the discharged ozone gas concentration to the estimation unit 32 (step S13b). Thereafter, the estimation unit 32 inputs the first generation environment data and the discharged ozone gas concentration into a numerical model to calculate an estimated ozone water concentration (step S14b). Thereafter, the processes from step S15 onward are performed.

[0074] In the third embodiment, the estimation unit 32 calculates an estimated ozonated water concentration by inputting the input first generation environment data and the exhaust ozone gas concentration into a numerical model. In this way, by adding the measurement data of the exhaust ozone gas concentration to the estimation of the ozonated water concentration using the numerical model, it is possible to reduce the error between the estimated ozonated water concentration and the actual ozonated water concentration caused by changes in water quality.

[0075] Embodiment 4. Fig. 9 is a diagram showing a schematic diagram of an example of the configuration of an ozone water generation system including an ozone water concentration estimation device according to embodiment 4. Note that the same components as those in embodiment 1 are given the same reference numerals, and their description will be omitted.

[0076] In an ozone water generation system 1c according to the fourth embodiment, the configuration of an ozone water generation apparatus 10c is different from that of the first embodiment. That is, the ozone water generation apparatus 10c further includes an exhaust ozone gas concentration measuring device 24. The exhaust ozone gas concentration measuring device 24 measures the exhaust ozone gas concentration of the exhaust ozone gas discharged from the ozone water generation tower 15 by measuring the temperature before and after decomposition of the exhaust ozone gas in the exhaust ozone gas decomposition tower 19. It is known that there is a correlation between the difference in the temperature of the exhaust ozone gas before and after ozone gas decomposition and the exhaust ozone gas concentration, and the exhaust ozone gas concentration can be measured by measuring the difference in the temperature of the exhaust ozone gas. That is, the exhaust ozone gas concentration used in the fourth embodiment is a value calculated using the temperatures before and after the decomposition treatment of the ozone gas discharged from the ozone water generation tower 15.

[0077] 9 , the exhaust ozone gas concentration measuring device 24 has a resistance temperature detector 241, a resistance temperature detector 242, and a concentration measuring unit 243. The resistance temperature detector 241 measures the temperature of the exhaust ozone gas flowing into the exhaust ozone gas decomposition tower 19 and outputs the measurement result, a pre-decomposition temperature, to the concentration measuring unit 243. The resistance temperature detector 242 measures the temperature of the exhaust ozone gas flowing out from the exhaust ozone gas decomposition tower 19 and outputs the measurement result, a post-decomposition temperature, to the concentration measuring unit 243. The concentration measuring unit 243 measures the exhaust ozone gas concentration from the pre-decomposition temperature input from the resistance temperature detector 241 and the post-decomposition temperature input from the resistance temperature detector 242. The exhausted ozone gas concentration measuring device 24, in one example, holds an exhausted ozone gas concentration estimation model, which is a numerical model that pre-calculates the relationship between the temperature of the exhausted ozone gas before and after ozone gas decomposition and the exhausted ozone gas concentration, and calculates the exhausted ozone gas concentration by inputting the pre-decomposition temperature and the post-decomposition temperature into this exhausted ozone gas concentration estimation model. The concentration measuring unit 243 outputs the calculated measurement value of the exhausted ozone gas concentration to the ozone water concentration estimating device 30.

[0078] The functions of the data acquisition unit 31 and estimation unit 32 of the ozone water concentration estimation device 30 are the same as those described in embodiment 3, and therefore a description thereof will be omitted. The ozone water concentration estimation method is also the same as that described in embodiment 3, and therefore a description thereof will be omitted. However, in embodiment 4, the exhausted ozone gas concentration meter 23 in step S31 corresponds to the exhausted ozone gas concentration measurement device 24, and the exhausted ozone gas concentration obtained in step S31 is obtained by using an exhausted ozone gas concentration estimation model from the pre-decomposition temperature obtained from the resistance temperature detector 241 and the post-decomposition temperature obtained from the resistance temperature detector 242.

[0079] In the fourth embodiment, even if the ozone water generation apparatus 10c is not provided with an exhaust ozone gas concentration meter 23, the exhaust ozone gas concentration measuring device 24 measures the exhaust ozone gas concentration from the temperatures before and after decomposition of the exhaust ozone gas in the exhaust ozone gas decomposition tower 19. The estimation unit 32 of the ozone water concentration estimation device 30 calculates an estimated ozone water concentration value by inputting the input first generation environment data and the exhaust ozone gas concentration into a numerical model. In this way, by adding the measurement data of the exhaust ozone gas concentration to the estimation of the ozone water concentration using the numerical model, it is possible to reduce the error between the estimated ozone water concentration value and the actual ozone water concentration that occurs due to changes in water quality. Furthermore, even if the exhaust ozone gas concentration meter 23 is not provided, there is also the effect that the exhaust ozone gas concentration can be measured with a simple configuration.

[0080] Embodiment 5. Fig. 10 is a diagram showing a schematic diagram of an example of the configuration of an ozone water generation system including an ozone water concentration estimation device according to embodiment 5. Note that the same components as those in embodiment 1 are given the same reference numerals, and their description will be omitted.

[0081] 10 includes an ozone water concentration estimation device 30d instead of the ozone water concentration estimation device 30. The ozone water concentration estimation device 30d according to the fifth embodiment further includes a numerical model construction unit 34 that constructs a numerical model used in the estimation unit 32. The numerical model construction unit 34 includes a data acquisition unit 341, a construction unit 342, and a numerical model storage unit 343.

[0082] The data acquisition unit 341 acquires model generation data, which is a plurality of data associating first generation environmental data with the ozone water concentration at the time of measurement of the first generation environmental data. This model generation data includes at least one of the first generation environmental data and the ozone water concentration actually measured in the past, and the first generation environmental data and the ozone water concentration obtained from the results of a simulation of the change in the ozone water concentration over time.

[0083] Specifically, the data acquisition unit 341 acquires model generation data including a plurality of data correlating first generation environmental data measured online during past ozone water generation in the ozone water generation tower 15 with the ozone water concentration previously measured offline based on the first generation environmental data. Alternatively, the data acquisition unit 341 acquires model generation data including a plurality of data correlating the output results of a simulation of changes in ozone water concentration over time, i.e., the first generation environmental data with the ozone water concentration obtained as a result of a simulation using the first generation environmental data. Here, the model generation data is data correlating the first generation environmental data with the ozone water concentration, more specifically, data correlating the first generation environmental data with the ozone water concentration over time. The data acquisition unit 341 inputs the acquired model generation data to the construction unit 342. The data acquisition unit 341 corresponds to the second data acquisition unit.

[0084] The model generation data does not need to use the output results of the simulation of the change in ozone water concentration over time if a numerical model can be generated using a combination of the first generation environment data and the ozone water concentration measured during past ozone water generation in the ozone water generation tower 15. The output results of the simulation of the change in ozone water concentration over time are used when there is no past model generation data, when there is past model generation data but some of it is missing, or when model generation data under various conditions is desired.

[0085] The simulation of the change in ozone water concentration over time is performed by a simulation unit (not shown). The simulation unit calculates a reaction rate coefficient and the like using past data or data obtained by experiment, and performs a simulation using a physical model that conforms to the laws of physics so that the ozone water concentration matches the ozone water concentration measured, for example, by an operator's analysis, when the first generation environment data was measured during past ozone water generation in the ozone water generation tower 15. The simulation unit is, for example, realized by an information processing device such as a personal computer.

[0086] The first generation environment data includes at least one of ozone gas concentration, ozone gas flow rate, water temperature, pH, and water level.

[0087] The construction unit 342 constructs a numerical model based on physical laws such that the ozone water concentration of the model generation data can be obtained when the first generation environment data of the model generation data is input. In other words, the construction unit 342 constructs a numerical model showing the relationship between the first generation environment data and the ozone water concentration based on physical laws, using the model generation data input by the data acquisition unit 341.

[0088] When the first generation environmental data, which is data including at least one of ozone gas concentration, ozone gas flow rate, water temperature, pH, and water level, is used as the explanatory variable x, and the ozone-water concentration is used as the response variable y, the numerical model is expressed as y = f(x). Then, a function f(x) is calculated using a physical model so that the ozone-water concentration, which is the response variable y, can be obtained when the first generation environmental data, which is the explanatory variable x, is input. In other words, the numerical model y = f(x) can be said to be a function that uses a physical model to express the relationship between the first generation environmental data, which is data including at least one of ozone gas concentration, ozone gas flow rate, water temperature, pH, and water level, which can be easily measured online, and the ozone-water concentration, which is difficult to measure and is measured offline. Using such a numerical model y = f(x), it becomes possible to use the first generation environmental data measured in real time to predict an estimated value of the ozone-water concentration, which is difficult to measure, in real time.

[0089] In constructing the numerical model in the constructing unit 342, it is desirable to use model generation data acquired under conditions similar to those under which the ozone water generation system 1d is actually used. For example, when the ozone water concentration estimation device 30d uses a numerical model that outputs an ozone water concentration estimate every few minutes, it is desirable to acquire the first generation environmental data and the ozone water concentration at a frequency similar to the output frequency of the ozone water concentration estimate, and use the acquired data as model generation data. In other words, in this case, it is desirable to use time-series data of the first generation environmental data from the start of operation of the ozone water generation system 1d, and time-series data of the ozone water concentration associated with each of the time-series data of the first generation environmental data.

[0090] The numerical model storage unit 343 stores the numerical model constructed by the construction unit 342. The numerical model stored in the numerical model storage unit 343 is read into the estimation unit 32 of the ozone water concentration estimation device 30d.

[0091] The estimation unit 32 of the ozone-water concentration estimation device 30d estimates the ozone-water concentration using a numerical model stored in the numerical model storage unit 343. That is, the estimation unit 32 calculates an ozone-water concentration estimate using a numerical model generated based on physical laws. At this time, the first generation environment data, specifically, at least one of the ozone gas concentration, ozone gas flow rate, water temperature, pH, and water level, is input into the numerical model, and the current ozone-water concentration is output.

[0092] Next, a method for constructing a numerical model will be described. Fig. 11 is a flowchart showing an example of the procedure of the method for constructing a numerical model in the ozone-water concentration estimation device according to the fifth embodiment. First, the data acquisition unit 341 of the numerical model construction unit 34 acquires model generation data (step S51). The model generation data is a plurality of data that associates first generation environmental data with the ozone-water concentration at the time of measurement of the first generation environmental data. The first generation environmental data and the ozone-water concentration may be data obtained from a process that was actually performed in the past, or may be the output result of a simulation.

[0093] Next, the construction unit 342 constructs a numerical model based on physical laws using the model generation data (step S52). In one example, the construction unit 342 constructs a numerical model based on physical laws such that the ozone water concentration of the model generation data is obtained when the first generation environment data of the model generation data is input. The construction unit 342 stores the constructed numerical model in the numerical model storage unit 343 (step S53).

[0094] The estimation unit 32 then reads the numerical model (step S54). This completes the method for constructing the numerical model. The estimation unit 32 then inputs the first generation environment data measured online into the constructed numerical model, thereby outputting an estimated ozone water concentration value, as described in the first embodiment.

[0095] In the above description, the numerical model construction unit 34 constructs a numerical model using model generation data that associates the first generation environmental data with the ozone water concentration at the time the first generation environmental data was measured. However, the model generation data may further include second generation environmental data at the time the first generation environmental data was measured. Specifically, the model generation data may further include the organic substance concentration at the time the first generation environmental data was measured. In this case, a numerical model is constructed that represents the first generation environmental data and the relationship between the organic substance concentration and the ozone water concentration based on the laws of physics. Furthermore, the model generation data may further include the exhaust ozone gas concentration at the time the first generation environmental data was measured. In this case, a numerical model is constructed that represents the first generation environmental data and the relationship between the exhaust ozone gas concentration and the ozone water concentration based on the laws of physics.

[0096] In other words, the data acquisition unit 341 acquires model generation data, which is a plurality of data associating the first and second generating environmental data with the ozone water concentrations at the time of measurement of the first and second generating environmental data. The model generation data at this time includes at least one of the first generating environmental data, second generating environmental data, and ozone water concentrations actually measured in the past, and the first generating environmental data, second generating environmental data, and ozone water concentrations obtained from simulation results of changes in the ozone water concentration over time. The construction unit 342 then constructs a numerical model based on physical laws such that the ozone water concentration of the model generation data can be obtained when the first and second generating environmental data of the model generation data are input.

[0097] In the fifth embodiment, a numerical model used by the estimation unit 32 is constructed in accordance with the laws of physics using model generation data. This allows the ozone water concentration to be estimated using a numerical model that follows the laws of physics. Furthermore, as the model generation data, not only the first generation environment data obtained from processing that was actually performed in the past and the measurement data of the ozone water concentration at the time of measurement of this first generation environment data are used, but also the output results of a simulation of the change in the ozone water concentration over time. This makes it possible to construct a numerical model even if there is a lack of past measurement data.

[0098] Embodiment 6. Fig. 12 is a diagram showing a schematic diagram of an example of the configuration of an ozone water generation system including an ozone water concentration estimation device according to embodiment 6. Note that the same components as those in embodiment 1 are given the same reference numerals, and their description will be omitted.

[0099] 12 includes an ozone water concentration estimation device 30e instead of the ozone water concentration estimation device 30. The ozone water concentration estimation device 30e according to the sixth embodiment further includes a learning device 35 that constructs a numerical model used in the estimation unit 32. The learning device 35 includes a data acquisition unit 351, a model generation unit 352, and a learned model storage unit 353.

[0100] The learning data used by the learning device 35 may include at least one of first generation environmental data and ozone water concentration actually measured in the past and first generation environmental data and ozone water concentration obtained from a simulation result of the change in ozone water concentration over time. That is, the data acquisition unit 351 acquires learning data including a plurality of data correlating first generation environmental data measured online during past ozone water generation in the ozone water generation tower 15 with the ozone water concentration, which is the correct data measured offline in the past for the first generation environmental data. Alternatively, the data acquisition unit 351 acquires learning data including a plurality of data correlating the output result of a simulation of the change in ozone water concentration over time, i.e., the first generation environmental data, with the correct data, which is the ozone water concentration, obtained as a result of the simulation using the first generation environmental data. Alternatively, the data acquisition unit 351 acquires these two types of learning data. The first generation environmental data is data including at least one of ozone gas concentration, ozone gas flow rate, temperature, pH, and water level of the ozone water. The data acquisition unit 351 corresponds to a third data acquisition unit.

[0101] The model generation unit 352 learns the ozone water concentration estimate based on learning data generated based on a combination of the first generation environmental data and the ozone water concentration output from the data acquisition unit 351. That is, the model generation unit 352 creates a trained model that infers an optimal ozone water concentration estimate from the first generation environmental data and the ozone water concentration of the ozone water generation device 10. More specifically, the model generation unit 352 uses the learning data to generate a trained model for inferring the ozone water concentration estimate from the first generation environmental data. Here, the learning data is data that associates the first generation environmental data and the ozone water concentration with each other, more specifically, data that associates the first generation environmental data and the ozone water concentration with each other over time. The trained model is an example of a numerical model.

[0102] A known algorithm such as supervised learning can be used as the learning algorithm used by the model generation unit 352. As an example, a case where a neural network is applied will be described.

[0103] In one example, the model generation unit 352 learns the estimated ozone water concentration value by so-called supervised learning in accordance with a neural network model. Here, supervised learning refers to a technique in which a set of data consisting of inputs and results, also called labels, is provided to the learning device 35, and the learning device 35 learns the features of the learning data and infers the results from the inputs.

[0104] A neural network is composed of an input layer consisting of multiple neurons, an intermediate layer also called a hidden layer consisting of multiple neurons, and an output layer consisting of multiple neurons. The intermediate layer may be one layer or two or more layers.

[0105] FIG. 13 is a diagram schematically illustrating an example of a neural network used by the model generation unit. In one example, in a three-layer neural network such as that shown in FIG. 13, when multiple inputs are input to input layers X1-X3, these values ​​are multiplied by weights indicated by w11-w16 and then input to intermediate layers Y1-Y2. When weights w11-w16 are not individually distinguished, they are referred to as weight w1. Furthermore, the results of intermediate layers Y1-Y2 are further multiplied by weights indicated by w21-w26 and then output to output layers Z1-Z3. When weights w21-w26 are not individually distinguished, they are referred to as weight w2. The output results of output layers Z1-Z3 vary depending on the values ​​of weights w1 and w2.

[0106] In this example, the neural network learns the ozone water concentration estimate value through so-called supervised learning according to learning data created based on a combination of the first generation environment data acquired by the data acquisition unit 351 and the ozone water concentration.

[0107] That is, the neural network learns by inputting the first generation environment data to the input layers X1-X3 and adjusting the weights w1 and w2 so that the results output from the output layers Z1-Z3 approach the ozone water concentration.

[0108] The model generation unit 352 generates and outputs a trained model by performing the above-described learning.

[0109] 12 , the trained model storage unit 353 stores the trained model output from the model generation unit 352. The trained model stored in the trained model storage unit 353 is read and used by the estimation unit 32 of the ozone water concentration estimation device 30 e.

[0110] In constructing the trained model in the model generation unit 352, it is desirable to use training data acquired under conditions similar to those under which the ozone water generation system 1e is actually used. For example, if the ozone water concentration estimation device 30e uses a numerical model that outputs an ozone water concentration estimate every few minutes, it is desirable to acquire the first generation environmental data and the ozone water concentration at a frequency similar to the output frequency of the ozone water concentration estimate, and use this as training data. In other words, in this case, it is desirable to use the time series data of the first generation environmental data from the start of operation of the ozone water generation system 1e and the time series data of the ozone water concentration associated with each of the time series data of the first generation environmental data.

[0111] Next, the learning process performed by the learning device 35 will be described with reference to Fig. 14. Fig. 14 is a flowchart showing an example of the processing procedure of the learning method performed by the learning device.

[0112] First, the data acquisition unit 351 acquires learning data including first generating environment data and ozone water concentration (step S71). In one example, the data acquisition unit 351 acquires at least one set of data: data combining first generating environment data measured in a process actually performed in the past with ozone water concentration that is correct data at the time of measurement of the first generating environment data; and data combining first generating environment data obtained by simulating changes in ozone water concentration over time with ozone water concentration that is correct data.

[0113] Next, the model generation unit 352 learns the estimated ozone-water concentration value by so-called supervised learning in accordance with the learning data created based on a combination of the first generation environment data and the ozone-water concentration acquired by the data acquisition unit 351, and generates a trained model (step S72). The trained model generated here corresponds to the numerical model.

[0114] The model generation unit 352 then stores the generated trained model in the trained model storage unit 353 (step S73). The estimation unit 32 then reads the trained model (step S74). This completes the learning method for training a trained model.

[0115] The estimation process of the ozone water concentration estimate value by the estimation unit 32 using the trained model is the same as that described in Fig. 2 of embodiment 1. That is, the estimation unit 32 outputs the current ozone water concentration estimate value as described in embodiment 1 by loading the first generation environment data measured online, specifically at least one of the ozone gas concentration, ozone gas flow rate, water temperature, pH, and water level, into the trained model generated by the model generation unit 352.

[0116] Furthermore, in embodiment 6, the estimation unit 32 has been described as outputting an ozone water concentration estimation value using a trained model trained by the model generation unit 352 of the ozone water concentration estimation device 30e, but it may also be configured to acquire a trained model from an external source, such as another ozone water concentration estimation device 30e, and output an ozone water concentration estimation value based on this trained model.

[0117] Furthermore, in the sixth embodiment, a case has been described in which supervised learning is applied to the learning algorithm used by the model generating unit 352, but the present invention is not limited to this.

[0118] The model generation unit 352 may also learn the ozone water concentration estimate based on learning data created for multiple ozone water production devices 10. The model generation unit 352 may acquire learning data from multiple ozone water production devices 10 used in the same area, or may learn the ozone water concentration estimate using learning data collected from multiple ozone water production devices 10 operating independently in different areas. It is also possible to add or remove ozone water production devices 10 from which learning data is collected during the process. Furthermore, the learning device 35 that learned the ozone water concentration estimate for one ozone water production device 10 may be applied to another ozone water production device 10, and the ozone water concentration estimate for the other ozone water production device 10 may be re-learned and updated.

[0119] Furthermore, the learning algorithm used in the model generation unit 352 may be deep learning, which learns to extract the features themselves, or machine learning may be performed according to other known methods, such as genetic programming, inductive logic programming, or support vector machines.

[0120] Furthermore, the learning device 35 and the ozone water concentration estimation device 30e, which is an inference device, are used to learn the ozone water concentration estimation value of the ozone water generation apparatus 10. The learning device 35 and the ozone water concentration estimation device 30e may be provided external to the ozone water generation apparatus 10 or may be built into the ozone water generation apparatus 10. As another example, the learning device 35 and the ozone water concentration estimation device 30e may be connected to the ozone water generation apparatus 10 via a network and may be separate devices from the ozone water generation apparatus 10. Furthermore, the learning device 35 and the ozone water concentration estimation device 30e may exist on a cloud server.

[0121] Furthermore, in the above description, the model generation unit 352 constructs a trained model using training data that associates first generating environment data measured online with the ozone water concentration at the time the first generating environment data was measured. However, the training data may further include second generating environment data at the time the first generating environment data was measured. Specifically, the training data may further include the organic matter concentration at the time the first generating environment data was measured. In this case, a trained model is constructed that represents the first generating environment data and the relationship between the organic matter concentration and the ozone water concentration. Furthermore, the training data may further include the exhaust ozone gas concentration in the first generating environment data. In this case, a trained model is constructed that represents the relationship between the first generating environment data and the exhaust ozone gas concentration and the ozone water concentration.

[0122] That is, the data acquisition unit 351 acquires training data, which is a plurality of data associating the first and second generating environmental data with the ozone-water concentrations at the time of measurement of the first and second generating environmental data. The training data includes at least one of the first and second generating environmental data and ozone-water concentrations actually measured in the past, and the first and second generating environmental data and ozone-water concentrations obtained from simulation results of changes in the ozone-water concentration over time. The model generation unit 352 then uses the training data to generate a trained model as a numerical model for inferring an estimated ozone-water concentration value from the first and second generating environmental data.

[0123] In the sixth embodiment, the learning device 35 generates a trained model for inferring an estimated ozone-water concentration value from the first generating environmental data through machine learning using at least one of multiple sets of data: data combining first generating environmental data measured in a process actually performed in the past with ozone-water concentration data that is ground truth data at the time of measurement of the first generating environmental data; and data combining first generating environmental data obtained by simulating changes in ozone-water concentration over time with ozone-water concentration data that is ground truth data. In this way, using machine learning to construct the numerical model improves the estimation accuracy of the ozone-water concentration.

[0124] The configurations shown in the above embodiments are merely examples, and may be combined with other known technologies, or different embodiments may be combined with each other. It is also possible to omit or modify parts of the configurations as long as they do not deviate from the gist of the invention.

[0125] 1, 1a, 1b, 1c, 1d, 1e Ozone water generation system, 10, 10a, 10b, 10c Ozone water generation device, 11 Dissolved water supply section, 12 Ozone generator, 13 Ozone gas concentration meter, 14 Ozone gas flow meter, 15 Ozone water generation tower, 16 Thermometer, 17 pH meter, 18 Water level meter, 19 Exhaust ozone gas decomposition tower, 20 Ozone water conveyance section, 21 Control device, 22 Organic matter concentration meter, 23 Exhaust ozone gas concentration meter, 24 Exhaust ozone gas concentration measuring device, 30, 30d, 30e Ozone water concentration estimation device, 31, 341, 351 Data acquisition section, 32 Estimation section, 33 Data output section, 34 Numerical model construction section, 35 Learning device, 70 Membrane separation activated sludge system, 71 Membrane separation activated sludge device, 71a Membrane separation tank, 71b Separation membrane, 72 inlet piping, 73 ozone water piping, 74 filtered water piping, 75 filtration pump, 80 water to be treated, 101 processor, 102 memory, 103 communication circuit, 104 display unit, 105 input unit, 241, 242 resistance temperature detector, 243 concentration measurement unit, 342 construction unit, 343 numerical model storage unit, 352 model generation unit, 353 learned model storage unit.

Claims

1. An ozone water concentration estimation device comprising: a first data acquisition unit that acquires first generation environmental data including at least one of the ozone gas concentration of the ozone gas supplied to an ozone water generation tower that generates ozone water by dissolving ozone gas in water to be dissolved, the ozone gas flow rate of the ozone gas supplied to the ozone water generation tower, the water temperature of the ozone water stored in the ozone water generation tower, the hydrogen ion exponent of the ozone water, and the amount of the ozone water stored; an estimation unit that estimates an ozone water concentration estimate value that is an estimate of the ozone water concentration in the ozone water at the time the first generation environmental data input from the first data acquisition unit is measured, using a numerical model that estimates the ozone water concentration, which is the concentration of ozone dissolved in the ozone water at the time the first generation environmental data was measured; and a data output unit that outputs the ozone water concentration estimate value.

2. The ozone water concentration estimation device described in claim 1, characterized in that the first data acquisition unit further acquires, in addition to the first generation environment data, second generation environment data which is a measurement value of a measurement object that affects the ozone water concentration, the numerical model is a model that estimates the ozone water concentration at the time of measurement of the first generation environment data and the second generation environment data from the first generation environment data and the second generation environment data, and the estimation unit uses the numerical model to estimate the estimated ozone water concentration value at the time of measurement of the first generation environment data and the second generation environment data input from the first data acquisition unit.

3. The ozone water concentration estimation device according to claim 2, wherein the second generation environment data is the organic matter concentration of the ozone water.

4. An ozone water concentration estimation device as described in claim 2 or 3, characterized in that the second generation environment data is the exhaust ozone gas concentration, which is the concentration of the exhaust ozone gas discharged from the ozone water generation tower.

5. The ozone water concentration estimation device described in claim 4, characterized in that the exhaust ozone gas concentration is a value calculated using the temperatures before and after the ozone gas discharged from the ozone water generation tower is decomposed.

6. The ozone water concentration estimation device described in claim 1, further comprising: a second data acquisition unit that acquires model generation data, which is a plurality of data that associates the first generation environment data with the ozone water concentration at the time of measurement of the first generation environment data; and a construction unit that constructs the numerical model based on physical laws such that the ozone water concentration of the model generation data can be obtained when the first generation environment data of the model generation data is input.

7. The ozone water concentration estimation device described in claim 6, characterized in that the data for model generation includes at least one of the first generation environment data and the ozone water concentration actually measured in the past, and the first generation environment data and the ozone water concentration obtained from the simulation results of the change in the ozone water concentration over time.

8. An ozone water concentration estimation device as described in any one of claims 2 to 5, further comprising: a second data acquisition unit that acquires model generation data, which is a plurality of data that associates the first generation environment data and the second generation environment data with the ozone water concentration at the time of measurement of the first generation environment data and the second generation environment data; and a construction unit that constructs the numerical model based on physical laws such that the ozone water concentration of the model generation data can be obtained when the first generation environment data and the second generation environment data of the model generation data are input.

9. The ozone water concentration estimation device described in claim 8, characterized in that the model generation data includes at least one of the first generation environment data, the second generation environment data, and the ozone water concentration actually measured in the past, and the first generation environment data, the second generation environment data, and the ozone water concentration obtained from the simulation results of the change in the ozone water concentration over time.

10. The ozone water concentration estimation device described in claim 1, further comprising: a third data acquisition unit that acquires learning data, which is a plurality of data that associates the first generation environment data with the ozone water concentration at the time of measurement of the first generation environment data; and a model generation unit that uses the learning data to generate, as the numerical model, a trained model for inferring the ozone water concentration estimation value from the first generation environment data.

11. The ozone water concentration estimation device described in claim 10, characterized in that the learning data includes at least one of the first generation environment data and the ozone water concentration actually measured in the past, and the first generation environment data and the ozone water concentration obtained from simulation results of the change in the ozone water concentration over time.

12. An ozone water concentration estimation device as described in any one of claims 2 to 5, further comprising: a third data acquisition unit that acquires learning data, which is a plurality of data that associates the first generation environment data and the second generation environment data with the ozone water concentration at the time of measurement of the first generation environment data and the second generation environment data; and a model generation unit that uses the learning data to generate, as the numerical model, a trained model for inferring the ozone water concentration estimated value from the first generation environment data and the second generation environment data.

13. The ozone water concentration estimation device described in claim 12, characterized in that the learning data includes at least one of the first generation environment data, the second generation environment data, and the ozone water concentration actually measured in the past, and the first generation environment data, the second generation environment data, and the ozone water concentration obtained from simulation results of the change in the ozone water concentration over time.

14. A method for estimating an ozone water concentration, comprising: a data acquisition step of acquiring first generation environmental data including at least one of the ozone gas concentration of the ozone gas supplied into an ozone water generation tower that generates ozone water by dissolving ozone gas in water to be dissolved, the ozone gas flow rate of the ozone gas supplied into the ozone water generation tower, the water temperature of the ozone water stored in the ozone water generation tower, the hydrogen ion exponent of the ozone water, and the amount of the ozone water stored; an estimation step of estimating an ozone water concentration estimate, which is an estimate of the ozone water concentration in the ozone water at the time of measurement of the first generation environmental data acquired in the data acquisition step, from the first generation environmental data using a numerical model that estimates the ozone water concentration, which is the concentration of ozone dissolved in the ozone water at the time of measurement of the first generation environmental data; and a data output step of outputting the ozone water concentration estimate.

15. A membrane bioreactor that separates solids and liquids from water to be treated that contains pollutants by filtration using a separation membrane; an ozonated water production tower that produces ozone water by dissolving ozone gas in water to be dissolved; an ozone generator that produces the ozone gas and supplies the ozone gas to the ozonated water production tower; an ozonated water supply unit that supplies the ozonated water produced in the ozonated water production tower to the separation membrane; a measurement unit that measures first production environment data including at least one of the ozone gas concentration of the ozone gas supplied to the inside of the ozonated water production tower, the ozone gas flow rate of the ozone gas supplied to the inside of the ozonated water production tower, the water temperature of the ozonated water stored in the ozonated water production tower, the hydrogen ion exponent of the ozonated water, and the storage amount of the ozonated water; and a control device that controls the ozonated water production tower, the ozone generator, and the ozonated water supply unit. an ozone water concentration estimation device that estimates an ozone water concentration estimate value, which is an estimate of the concentration of ozone dissolved in the ozone water inside the ozone water generation tower, using the first generation environmental data acquired from the measurement unit; wherein the ozone water concentration estimation device has: a data acquisition unit that acquires the first generation environmental data from the measurement unit via a network; an estimation unit that estimates the ozone water concentration estimate value in the ozone water at the time of measurement of the first generation environmental data input from the data acquisition unit, using a numerical model that estimates the ozone water concentration, which is the concentration of ozone dissolved in the ozone water at the time of measurement of the first generation environmental data, from the first generation environmental data; and a data output unit that transmits the ozone water concentration estimate value to the control device via the network; wherein the control device controls the ozone water delivery unit to supply the ozone water to the separation membrane when the ozone water concentration estimate value reaches a predetermined value.

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