JP2020168618A - Operation support device, operation support method, and computer program - Google Patents

Abstract

To provide an operation support device, an operation support method, and a computer program capable of providing information properly to assist in judging an adequacy of control by a system in the system for automatically controlling an injection amount of a chemical injected in a process of water treatment.SOLUTION: An operation support device includes a prediction part and a support information formation part. The prediction part predicts, with respect to a water treatment process having a process of injecting a chemical into a water to be treated, an injection amount of the chemical into the water to be treated based on forecast information about a raw water flowing into the water treatment process. The support information formation part forms information indicating a prediction result of the injection amount and support information which corresponds to the information used to predict the injection amount.SELECTED DRAWING: Figure 2

Description

Embodiments of the present invention relate to driving assistance devices, driving assistance methods and computer programs.

Waterworks are required to have water quality that meets the standards stipulated by the Waterworks Law and to supply the required amount of water to consumers regardless of changes in the water quality of the water intake source. Therefore, strict water quality control is carried out at the waterworks. For such water quality management, at the waterworks, the injection amount of chemicals, etc. injected into the water to be treated in the process of water treatment is determined based on the know-how such as the knowledge and experience of skilled operation managers. Was common. In recent years, there has been a demand for more efficient and cost-effective operation operations, reduction of the load on operation managers, etc., and the introduction of a control system that automatically controls the injection amount of chemicals by creating a database of operation management know-how. Is underway. However, in the operation by the conventional control system, sufficient information is not provided to the operation manager, and it may be difficult for the operation manager to judge the validity of the control by the system.

JP-A-2018-18116

The problem to be solved by the present invention is to more appropriately provide information that supports the determination of the appropriateness of control by the system in a system that automatically controls the injection amount of chemicals or the like injected in the process of water treatment. It is to provide a driving support device, a driving support method, and a computer program that can be used.

The driving support device of the embodiment has a prediction unit and a support information generation unit. Regarding the water treatment process having a step of injecting a chemical into the water to be treated, the prediction unit predicts the injection amount of the chemical into the water to be treated based on the prediction information about the raw water flowing into the water treatment process. The support information generation unit generates support information that displays the information indicating the prediction result of the injection amount and the information used for the prediction of the injection amount in association with each other.

The figure which shows the specific example of the system structure of the water treatment system in 1st Embodiment. The block diagram which shows the specific example of the functional structure of the operation support device of 1st Embodiment. The flowchart which shows the specific example of the flow of the process which the driving support apparatus of 1st Embodiment displays support information. The figure which shows the display example of the support information in 1st Embodiment. The figure which shows the display example of the support information in 1st Embodiment. The figure which shows the display example of the support information in 1st Embodiment. The block diagram which shows the specific example of the functional structure of the operation support device of 2nd Embodiment. The flowchart which shows the specific example of the flow of the process which the driving support apparatus of 2nd Embodiment displays support information. The figure which shows the display example of the support information in the 2nd Embodiment. The figure which shows the specific example of the system structure of the water treatment system in 3rd Embodiment. The block diagram which shows the specific example of the functional structure of the operation support device of 3rd Embodiment. The flowchart which shows the specific example of the flow of the process which the driving support apparatus of 3rd Embodiment displays support information.

Hereinafter, the driving support device, the driving support method, and the computer program of the embodiment will be described with reference to the drawings.

(First Embodiment)
FIG. 1 is a diagram showing a specific example of the system configuration of the water treatment system according to the first embodiment. The water treatment system in the first embodiment has a function of predicting the future water quality of the water to be treated flowing into the water treatment process, and a chemical or the like (for example, chlorine or a flocculant) to be injected into the water to be treated in the water treatment process. , Activated carbon, etc.) has a function of determining the injection amount and a function of presenting information (hereinafter referred to as "support information") for determining the validity of the determined injection amount to the operation manager.

For example, the water treatment system 100 shown in FIG. 1 includes an injection control device 1 and an operation support device 2. The injection control device 1 determines the injection rate when the chemical injection pump 3 injects chemicals or the like into the water to be treated in the water treatment process WP, and the injection amount of the chemicals or the like determined by the operation support device 2 (hereinafter, “chemical injection amount”). It is controlled based on. Although one chemical injection pump 3 is shown in FIG. 1 for simplicity, there may be a plurality of chemical injection pumps 3 to be controlled.

Specifically, the operation support device 2 has a water quality (hereinafter referred to as "inflow water quality") and a flow rate (hereinafter "inflow") of raw water (that is, water to be treated in the initial state in the water treatment process WP) flowing into the water treatment process WP. "Flow rate"), the water quality of the treated water (hereinafter referred to as "treated water") that has undergone the water treatment process WP (hereinafter referred to as "treated water quality"), and various matters related to the water treatment process WP indicated by the process data. The future inflow water quality is predicted based on the amount, and the chemical injection amount is determined based on the prediction result. The injection control device 1 determines the injection rate of the chemicals or the like to be injected into the water to be treated (hereinafter referred to as "chemical injection rate") based on the chemical injection amount determined in this way, and uses the determined chemical injection rate. The injection operation is instructed to the chemical injection pump 3.

In the water treatment system 100, in order to control such a water treatment process WP, a raw water quality meter 31 for measuring the inflow water quality, an inflow flowmeter 32 for measuring the inflow flow, and a treated water quality for measuring the treated water quality are provided. A total of 33 are provided. For example, the raw water quality meter 31 measures odor, chromaticity, turbidity, etc. as the inflow water quality. Further, for example, the treated water quality meter 33 measures the odor, chromaticity, turbidity, etc., which are the same as the inflow water quality, as well as the residual chlorine concentration of the treated water stored in the distribution reservoir or the like.

Further, the water treatment system 100 is provided with various sensors (not shown) for measuring various quantities related to the water treatment process WP, and the measurement data of the various sensors is supplied to the operation support device 2 as process data. For example, the process data includes information on the operation of the chemical injection pump 3 and information indicating the water quality of the water to be treated.

Further, the operation support device 2 has a function of generating support information regarding the amount of chemical injection determined by itself based on various information regarding the water treatment process WP and presenting the generated support information to the operation manager. For example, the driving support device 2 generates information as support information in which the determined chemical injection amount is associated with various information related to the determination of the chemical injection amount (hereinafter referred to as “related information”). The related information may be information on the state of the water treatment process WP or the actual results of its control (hereinafter referred to as “actual information”), or various information predicted regarding the water treatment process WP (hereinafter referred to as “expected information”). ), Or information indicating various prediction results regarding the water treatment process WP predicted based on the actual information and the prediction information (hereinafter referred to as “prediction information”).

For example, the actual information may include information indicating measured values such as inflow water quality, inflow flow rate, and treated water quality, or may include process data acquired for the water treatment process WP. In addition, for example, the forecast information includes the weather forecast of the basin where the raw water flows down before flowing into the water treatment process WP, the expected flow rate of the raw water flowing into the water treatment process WP, the demand amount of the treated water, and the decomposition of dissolved chlorine. Information (for example, an ultraviolet index) indicating the amount of solar radiation that correlates with the amount may be included.

Further, the display of the support information is an example of a mode in which the support information is presented to the operation manager, and the support information may be presented in a mode other than the display. For example, support information may be presented by audio output. In addition, the correspondence between each information (actual information, forecast information, forecast information) in the display of support information may be realized by displaying both at the same time, or an operation of directly or indirectly selecting one of them. It may be realized by displaying the other according to.

In the conventional water treatment system, the operation manager makes full use of such various performance information, forecast information, know-how cultivated through many years of experience, etc. to determine an appropriate chemical injection amount. In some cases, it was difficult for the operation manager to judge the appropriateness of the control by the system because sufficient information was not provided to the operation manager. However, in the water treatment system 100 of the first embodiment, the operation manager of the water treatment process WP is determined by the system by presenting the above-mentioned support information to the operation manager by the operation support device 2. The validity of the chemical injection rate can be judged more easily. Hereinafter, the configuration of the driving support device 2 that exhibits such an effect will be described in detail.

FIG. 2 is a block diagram showing a specific example of the functional configuration of the driving support device 2 of the first embodiment. The driving support device 2 includes a CPU (Central Processing Unit), a memory, an auxiliary storage device, and the like connected by a bus, and executes a program. The operation support device 2 includes a communication unit 201, an input unit 202, a storage unit 203, a display unit 204, a performance information acquisition unit 21, a forecast information acquisition unit 22, a prediction unit 23, a chemical injection amount determination unit 24, and a support unit by executing a program. It functions as a device including the information generation unit 25. In addition, all or a part of each function of the driving support device 2 may be realized by using hardware such as ASIC (Application Specific Integrated Circuit), PLD (Programmable Logic Device) and FPGA (Field Programmable Gate Array). .. The program may be recorded on a computer-readable recording medium. The computer-readable recording medium is, for example, a flexible disk, a magneto-optical disk, a portable medium such as a ROM or a CD-ROM, or a storage device such as a hard disk built in a computer system. The program may be transmitted over a telecommunication line.

The communication unit 201 is a communication interface that connects the operation support device 2 to the injection control device 1 in a communicable manner. The communication unit 201 may be based on wired communication or wireless communication. Further, the network to which the communication unit 201 connects may be a LAN (Local Area Network) or a WAN (Wide Area Network). When the communication unit 201 connects to the WAN, the WAN may be a public network such as the Internet or a private network such as a dedicated line.

The input unit 202 is configured by using an input device such as a touch panel, a mouse, and a keyboard. Further, the input unit 202 may be configured as an external input interface for connecting an external input device to the driving support device 2. Specifically, the input unit 202 inputs forecast information. The forecast information may be received from another device via the communication unit 201 instead of being input via the input unit 202.

The storage unit 203 is configured by using a storage device such as a magnetic hard disk device or a semiconductor storage device. The storage unit 203 stores the actual result information, the forecast information, and the forecast information. For example, in the example of FIG. 1, information indicating inflow water quality, inflow flow rate, treated water quality, and process data are acquired as actual information. In addition, information indicating the expected flow rate and the weather forecast is acquired as the forecast information. The prediction information is predicted by the prediction unit 23.

The display unit 204 is configured by using a display device such as a CRT (Cathode Ray Tube) display, a liquid crystal display, or an organic EL (Electro-Luminescence) display. Alternatively, the display unit 204 may be configured as an interface for connecting these display devices to the own device. The display unit 204 is used for displaying the support information generated by the support information generation unit 25.

The performance information acquisition unit 21 acquires the performance information via the communication unit 201, and stores the acquired performance information in the storage unit 203. Specifically, the performance information acquisition unit 21 accumulates the performance information in the storage unit 203 by repeatedly acquiring the performance information at a predetermined timing.

The forecast information acquisition unit 22 acquires forecast information via the input unit 202, and stores the acquired forecast information in the storage unit 203. Here, the forecast information acquisition unit 22 shall acquire forecast information about the period to be predicted by the forecast unit 23.

The prediction unit 23 generates prediction information regarding the control of the water treatment process WP based on the actual information and the prediction information stored in the storage unit 203. The prediction unit 23 stores the generated prediction information in the storage unit 203 and outputs it to the chemical injection amount determination unit 24.

The chemical injection amount determination unit 24 determines the chemical injection amount for the water to be treated based on the prediction information generated by the prediction unit 23. The chemical injection amount determination unit 24 instructs the injection control device 1 via the communication unit 201 and notifies the support information generation unit 25 of the determined chemical injection amount.

The support information generation unit 25 generates support information based on the actual result information, the prediction information, and the prediction information stored in the storage unit 203. The support information generation unit 25 outputs the generated support information to the display unit 204.

FIG. 3 is a flowchart showing a specific example of the flow of processing in which the driving support device 2 of the first embodiment displays support information. First, the performance information acquisition unit 21 acquires the performance information, and stores the acquired performance information in the storage unit 203. The performance information acquisition unit 21 accumulates the performance information in the storage unit 203 by repeatedly acquiring the performance information at a predetermined timing (step S101).

Subsequently, the forecast information acquisition unit 22 acquires the forecast information (step S102). The prediction information acquisition unit 22 stores the acquired prediction information in the storage unit 203 and outputs it to the prediction unit 23. For example, here, the forecast information acquisition unit 22 inputs forecast information indicating the forecast flow rate and the weather forecast.

Subsequently, the prediction unit 23 makes a prediction regarding the control of the water treatment process WP based on the actual information and the prediction information stored in the storage unit 203. Specifically, the prediction unit 23 predicts the future inflow water quality in the water treatment process WP (step S103). The prediction unit 23 records the prediction information indicating the prediction result of the inflow water quality in the storage unit 203 (step S104), and outputs the prediction information to the chemical injection amount determination unit 24.

Subsequently, the chemical injection amount determination unit 24 determines the predicted value of the future chemical injection amount based on the prediction information (step S105). Specifically, the chemical injection amount determination unit 24 determines the chemical injection amount according to the future inflow water quality indicated by the prediction information. The chemical injection amount determination unit 24 outputs the determined chemical injection amount to the support information generation unit 25.

Here, an example will be given in which the chemical injected into the water to be treated is chlorine for the purpose of sterilization, disinfection, etc. Generally, the amount of chlorine injected into the water to be treated in the water treatment process is determined according to the amount of water to be treated and the water quality. Of these, the amount of treated water to be treated is determined according to the amount of demand for treated water and the amount of inflow of raw water, and the quality of the water to be treated varies depending on the quality of the inflowing raw water. In addition, when the demand for treated water is for domestic use, the inflow pattern of raw water and the fluctuation pattern of water quality often do not fluctuate significantly in a short period of time on a daily basis. Therefore, if the predicted future is in the near future from the present, the inflow pattern of treated water in the future can be obtained by applying the expected demand for treated water to the past inflow pattern of raw water and the fluctuation pattern of water quality. It is possible to predict fluctuation patterns of water quality. The prediction unit 23 calculates the amount of chlorine injection required at each future time point based on the predicted values of the flow rate and water quality of the water to be treated thus obtained.

The chlorine injection amount may be determined by any method as long as the quality of the treated water meets a predetermined standard and a required amount of treated water can be obtained. For example, the chlorine injection amount may be a calculation model that determines the required chlorine injection amount based on the flow rate and water quality of the water to be treated (including raw water), or may be a prediction target based on actual information. It may be a method of selecting a past time point in a situation similar to the time point and using the chlorine injection amount adopted at that time as a future prediction value. Further, the above calculation model may be constructed based on the know-how obtained through many years of operational experience, or such a model may be generated or updated based on actual results information. May be good. Further, the future chlorine injection amount may be predicted based on the learning result of the past data by machine learning. Further, in machine learning, the relationship between the chlorine injection amount and arbitrary information included in the actual information or the forecast information may be learned.

In addition, since the amount of water to be treated depends on the weather of the day, it is desirable that the future chlorine injection amount is predicted in consideration of the future weather. For example, future weather may be included in the variables of the calculation model of chlorine injection amount, or past data including past weather information may be learned in machine learning.

Subsequently, the support information generation unit 25 generates support information based on the drug injection amount determined by the drug injection amount determination unit 24 and various related information stored in the storage unit 203 (step S106). , The generated support information is output to the display unit 204 (step S107). For example, the support information generation unit 25 generates support information displayed in the modes illustrated in FIGS. 4 to 6 below.

4 to 6 are diagrams showing a display example of support information in the first embodiment. FIG. 4 shows one continuous value of the chlorine injection amount adopted in the past 24 hours in the water treatment process WP and the predicted value of the chlorine injection amount calculated for the period from the present time to 24 hours ahead. It is displayed as a graph as time series data. The horizontal axis of the graph represents time, and the vertical axis represents the amount of chlorine injected. Further, time 0 on the horizontal axis represents the present time, -24 represents a time point 24 hours before the present time, and 24 represents a time point 24 hours after the present time.

Here, the fact that the chlorine injection amount repeatedly increases and decreases in FIG. 4 is a result of considering fluctuations in the demand amount and the amount of chlorine decomposition due to solar radiation (specifically, ultraviolet rays). For example, it reflects the know-how of reducing chlorine injection at night when demand is low and the effect of solar radiation is small, and increasing chlorine injection during daytime when demand is high and the effect of solar radiation is large. The result.

In addition, the Water Supply Law stipulates that the residual chlorine concentration of water supplied at the end of the consumer side (for example, a tap) must be 0.1 mg / L or more. On the other hand, since chlorine in water decreases from the distribution reservoir to the end, it is necessary to control the value in the distribution reservoir to a value higher than 0.1 mg / L (for example, 0.4 mg / L). There is. For example, one of the methods for observing such a control standard is to set a lower limit for the residual chlorine concentration value to be adopted. Therefore, it is desirable that the prediction unit 23 predicts the chlorine injection amount by a method incorporating know-how for complying with such management standards.

Further, the two series A and B shown in FIG. 4 show the results of predicting the chlorine injection amount under different conditions. For example, series B represents a prediction result obtained by adding a condition that prioritizes cost reduction related to chlorine injection to the prediction condition, and series A is a prediction result without adding such a condition. Represents. As described above, the support information may display the results predicted under different conditions in a comparable manner in consideration of cost, safety, and the like. Further, the support information generation unit 25 may determine the priority of a plurality of prediction results obtained by prediction under different conditions, and generate support information for displaying the prediction results together with the priority. Moreover, the priority may be determined by any criteria. In addition, the support information generation unit 25 predicts the chemical cost and power consumption based on the prediction result of the chlorine injection amount, and generates support information for displaying the prediction result in association with the prediction result of the chlorine injection amount. May be good.

In this way, the predicted control value for the water treatment process WP (chlorine injection amount in this embodiment) is displayed together with the actual value of the control value adopted in the past, so that the operation manager can obtain the predicted value. It will be easier to judge the validity. In addition, for control targets such as the water treatment process WP whose control patterns are similar day by day, the prediction results are displayed together with the past results of the same period as the prediction period, thereby further operating management. The person will be able to judge the validity of the predicted value more intuitively. Further, by displaying the results predicted under different conditions together with the past results at the same time, the operation manager can further select a more appropriate one from a plurality of control pattern candidates.

On the other hand, FIG. 5 is a graph showing the inflow pattern of raw water during the period in which the actual value and the expected value of the chlorine injection amount are shown in FIG. 4, and similarly to FIG. 4, the present time is set to 0 and 24 hours before the present time. It shows the actual value of (-24) and the expected value from the present time to 24 hours later (24). Further, FIG. 6 is a graph showing the fluctuation pattern of the ultraviolet index during the period in which the actual value and the expected value of the chlorine injection amount are shown in FIG. 4, and similarly to FIG. 4, the present time is set to 0 and 24 hours from the present time. It shows the actual value of the previous (-24) and the expected value from the present time to 24 hours later (24).

In this way, other values corresponding to the predicted value of the chlorine injection amount are displayed corresponding to the same period as the display of the prediction result, so that the operation manager can improve the validity of the predicted value of the chlorine injection amount. You will be able to easily judge. When a plurality of different fluctuation patterns are expected for the inflow pattern of raw water and the ultraviolet index, the support information generation unit 25 of the driving support device 2 displays the plurality of fluctuation patterns in different series as in FIG. It may be configured to generate support information.

For example, by checking the graph of FIG. 4, the operation manager can judge that the future chlorine injection amount is appropriate to some extent as compared with the fluctuation pattern of the chlorine injection amount in the past. Furthermore, by checking the graph in FIG. 4, the operation manager determines which of the chlorine injection amounts of series A and B predicted under different conditions is the more appropriate predicted value as the planned value based on the actual value. can do. Further, the operation manager can judge the validity of the chlorine injection amount with respect to the inflow pattern of the raw water by comparing FIGS. 4 and 5. Similarly, the operation manager can judge the validity of the chlorine injection amount with respect to the fluctuation pattern of the ultraviolet index by comparing FIGS. 4 and 6.

(Second Embodiment)
FIG. 7 is a block diagram showing a specific example of the functional configuration of the driving support device 2a according to the second embodiment. The driving support device 2a is different from the driving support device 2 of the first embodiment in that the prediction unit 23a is provided instead of the prediction unit 23. Other configurations are the same as those of the driving support device 2 of the first embodiment. Therefore, in FIG. 7, the same components as those in FIG. 2 are designated by the same reference numerals, and the description thereof will be omitted as appropriate. The configuration of the water treatment system in the second embodiment is the same as that of the water treatment system 100 in the first embodiment except that the operation support device 2a is provided instead of the operation support device 2.

Specifically, the prediction unit 23a is similar to the prediction unit 23 in the first embodiment in that it generates prediction information regarding the control of the water treatment process WP based on the prediction information and the actual information. It differs from the prediction unit 23 in the first embodiment in that it predicts the concentration of chlorine remaining in the treated water (hereinafter referred to as “residual chlorine concentration”) in addition to the future inflow water quality.

Here, the method in which the prediction unit 23a predicts the residual chlorine concentration based on the prediction information and the actual information may be a method using a predetermined calculation model as in the case of the chlorine injection amount, or is based on the actual information. It may be a method of selecting a past time point in a situation similar to the time point of the prediction target and using the value of the residual chlorine concentration at that time point as the future prediction value. Further, the calculation model may be a model prepared in advance, or may be generated or updated based on actual information. Further, the future residual chlorine concentration may be predicted based on the learning result of the past data by machine learning. Further, in machine learning, the relationship between the residual chlorine concentration and arbitrary information contained in the actual information or the forecast information may be learned.

FIG. 8 is a flowchart showing a specific example of the flow of processing in which the driving support device 2a of the second embodiment displays the support information. In FIG. 8, the same processing as in FIG. 3 is designated by the same reference numerals as those in FIG. 3, and the description thereof will be omitted as appropriate. The prediction unit 23a predicts the future inflow water quality in step S103, and then predicts the future residual chlorine concentration of the treated water in the water treatment process WP based on the actual information and the prediction information (step S201). Then, the prediction unit 23 records the prediction information indicating the prediction result of the inflow water quality and the residual chlorine concentration in the storage unit 203 (step S202).

FIG. 9 is a diagram showing a display example of support information in the second embodiment. FIG. 9 shows one continuous value of the residual chlorine concentration measured in the past 24 hours in the water treatment process WP and the predicted value of the residual chlorine concentration calculated for the period from the present time to 24 hours ahead. It is displayed as a graph as time series data. The horizontal axis of the graph represents time, and the vertical axis represents residual chlorine concentration. Further, similarly to FIGS. 4 to 6, in FIG. 9, time 0 on the horizontal axis represents the present time, -24 represents a time point 24 hours before the present time, and 24 represents a time point 24 hours after the present time.

In addition, the two series A and B shown in FIG. 9 show the results of predicting the residual chlorine concentration under different conditions. For example, the series B represents the prediction result obtained by adding the condition that the target value of the residual chlorine concentration is set higher to the prediction condition, and the series A is predicted without adding such a condition. Represents the result. For example, due to the addition of such conditions, even if the quality of the raw water flowing into the water treatment process WP changes suddenly, it is possible to secure a time margin until the residual chlorine concentration reaches the predetermined control lower limit value. It is possible to make a forecast in consideration.

In this way, the predicted control value for the water treatment process WP (residual chlorine concentration in this embodiment) is displayed together with the actual value of the control value adopted in the past, so that the operation manager can obtain the predicted value. It will be easier to judge the validity. In addition, for control targets such as the water treatment process WP whose control patterns are similar day by day, the prediction results are displayed together with the past results of the same period as the prediction period, thereby further operating management. The person will be able to judge the validity of the predicted value more intuitively. Further, by displaying the results predicted under different conditions together with the past results at the same time, the operation manager can further select a more appropriate one from a plurality of control pattern candidates.

For example, by checking the graph of FIG. 9, the operation manager can judge that the future residual chlorine concentration is appropriate to some extent as compared with the past fluctuation pattern of the residual chlorine concentration. Further, the operation manager can determine which of the residual chlorine concentrations of the series A and B predicted under different conditions is the more appropriate predicted value as the planned value by checking the graph of FIG.

Further, in the second embodiment, the support information regarding the residual chlorine concentration is related to the chlorine injection amount, the inflow pattern of raw water, the fluctuation pattern of the ultraviolet index, etc., as in the display of the chlorine injection amount in the first embodiment. It may be displayed in association with the actual value, expected value, or predicted value of the event.

Further, in the second embodiment, the support information regarding the residual chlorine concentration may be generated so that the predicted results under different conditions are displayed together with the priority as in the first embodiment.

(Third Embodiment)
FIG. 10 is a diagram showing a specific example of the system configuration of the water treatment system according to the third embodiment. The water treatment system 100b shown in FIG. 10 is different from the water treatment system in the second embodiment in that the operation support device 2b is provided in place of the operation support device 2a. Other configurations are similar to the water treatment system in the second embodiment. Therefore, in FIG. 10, description thereof will be omitted as appropriate by assigning the same reference numerals to those similar configurations as in FIG.

The driving support device 2b is different from the driving support device 2a of the second embodiment in that the residual chlorine concentration is predicted based on the value of the designated chemical injection amount (hereinafter referred to as “designated value”). Therefore, the driving support device 2b has a function of accepting an input of a designated value.

FIG. 11 is a block diagram showing a specific example of the functional configuration of the driving support device 2b according to the third embodiment. The driving support device 2b is the first in that it includes an input unit 202b instead of the input unit 202, a prediction unit 23b instead of the prediction unit 23a, and a support information generation unit 25b instead of the support information generation unit 25. It is different from the driving support device 2a of the second embodiment. Other configurations are the same as those of the driving support device 2a of the second embodiment. Therefore, in FIG. 11, the same components as those in FIG. 7 are designated by the same reference numerals, and the description thereof will be omitted as appropriate.

The input unit 202b has a function of inputting a specified value of the chlorine injection amount in addition to inputting the forecast information. The input unit 202b outputs the input designated value to the prediction unit 23b.

The prediction unit 23b is similar to the prediction unit 23a in the second embodiment in that it predicts the future inflow water quality and the residual chlorine concentration in the water treatment process WP based on the prediction information and the actual information, but the same residue It differs from the prediction unit 23a in the second embodiment in that the chlorine concentration can be predicted based on the specified value of the chlorine injection amount. Hereinafter, the prediction of the residual chlorine concentration by the same method as in the second embodiment is referred to as the first prediction, and the prediction of the residual chlorine concentration based on the specified value of the chlorine injection amount is referred to as the second prediction.

For example, the prediction unit 23b may make a second prediction by calculating the residual chlorine concentration corresponding to the specified value using a predetermined calculation model that describes the relationship between the chlorine injection amount and the residual chlorine concentration. .. Further, for example, in the first prediction, the prediction unit 23b may make a second prediction by predicting the chlorine injection amount by adding a constraint condition that sets the chlorine injection amount at a certain point in the future as a specified value. Good. In this case, the prediction unit 23b may predict future fluctuations in the residual chlorine concentration based on the prediction result. Further, in this case, the input unit 202b may accept an input that specifies a time point for applying the specified value. The prediction unit 23b records information indicating the result of the first prediction or the second prediction in the storage unit 203 as prediction information.

The support information generation unit 25b generates support information based on the drug injection amount determined by the drug injection amount determination unit 24 and the prediction information generated by the prediction unit 23b, and the generated support information is displayed on the display unit 204. Display it. Similar to the first embodiment or the second embodiment, the support information generation unit 25b may generate support information that displays the forecast information or the forecast information in association with the actual information, or compares various information. Support information to be displayed may be generated in a possible manner.

FIG. 12 is a flowchart showing a specific example of the flow of processing in which the driving support device 2b of the third embodiment displays the support information. In FIG. 12, the same processing as in FIG. 8 is designated by the same reference numerals as those in FIG. 8, and the description thereof will be omitted as appropriate. After the prediction information is acquired in step S102, the prediction unit 23b determines whether or not the chemical injection amount is specified (step S301). For example, whether or not the chemical injection amount is specified may be determined by whether or not the input operation of the specified value is performed. When the chemical injection amount is not specified (step S301: none), the prediction unit 23b proceeds to step S103. On the other hand, when the chemical injection amount is specified (step S301: yes), the prediction unit 23b inputs the specified value (step S302), and the inflow water quality and the residual chlorine concentration are determined using the actual information, the forecast information and the specified value. Prediction (second prediction) is performed (steps S103 and S201).

The prediction unit 23b may predict the residual chlorine concentration based on the prediction result of the chemical injection amount in step S105. The prediction unit 23b records information indicating the prediction results of the inflow water quality and the residual chlorine concentration in the storage unit 203 as prediction information (step S202).

The operation support device 2b of the third embodiment configured in this way predicts a future fluctuation pattern of the residual chlorine concentration based on the value (designated value) of the designated chemical injection amount. Further, the driving support device 2b displays information indicating a fluctuation pattern of the residual chlorine concentration predicted based on the designated value as support information. As a result, the operation manager decides that the operation with a predetermined chemical injection amount is planned, or that the chlorine injection amount or residual chlorine concentration predicted based on the actual information and the forecast information is not appropriate. In, the prediction conditions can be changed, and the validity of the predicted chlorine injection amount or residual chlorine concentration under the changed conditions can be judged.

(Modification example)
In each of the above embodiments, for the sake of simplicity, the case where the target of prediction is the chlorine injection amount or the residual chlorine concentration has been described as an example, but it is a value determined for the control of the water treatment process WP, and is actual information or prediction. Any value that can be predicted based on information may be the target of prediction. For example, the injection amount of the flocculant may be predicted instead of the chlorine injection amount, and in this case, the turbidity of the treated water may be predicted instead of the residual chlorine concentration. Further, for example, the injection amount of activated carbon may be predicted instead of the chlorine injection amount, and in this case, the concentration of activated carbon remaining in the treated water may be predicted.

According to at least one embodiment described above, with respect to a water treatment process having a step of injecting a chemical into the water to be treated, the chemical with respect to the water to be treated is based on expected information about raw water flowing into the water treatment process. A prediction unit that predicts the injection amount, and a support information generation unit that generates support information that displays the information indicating the prediction result of the injection amount and the information used for predicting the injection amount in association with each other. By having it, in a system that automatically controls the injection amount of chemicals and the like injected in the process of water treatment, it is possible to more appropriately provide information that supports the judgment of the appropriateness of the control by the system.

The raw water flow rate and the weather forecast in each of the above embodiments are examples of forecast information, and are not necessarily limited to such information. Other information may be used for the prediction information as long as it is information indicating a future prediction required for predicting the amount of chemical injection.

Further, the chlorine injection amount in each of the above embodiments is an example of the chemical injection amount, and is not necessarily limited to this. As long as the amount of chemicals injected into the water to be treated in the water treatment process is used, the amount of chemicals injected may be an amount other than chlorine (for example, a coagulant, activated carbon, etc.).

Further, the residual chlorine concentration in each of the above embodiments is an example of the water quality of the treated water, and is not necessarily limited to this. The water quality of the treated water may be represented by any index value as long as it correlates with the injection amount of the chemical in the water treatment process.

Although some embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the gist of the invention. These embodiments and modifications thereof are included in the scope and gist of the invention as well as the invention described in the claims and the equivalent scope thereof.

100, 100b ... Water treatment system, 1 ... Injection control device, 2, 2a, 2b ... Driving support device, 201 ... Communication unit, 202, 202b ... Input unit, 203 ... Storage unit, 204 ... Display unit, 21 ... Actual information Acquisition unit, 22 ... Forecast information acquisition unit, 23, 23a, 23b ... Prediction unit, Chemical injection amount determination unit 24, 25, 25b ... Support information generation unit, 3 ... Chemical injection pump, 31 ... Raw water quality meter, 32 ... Inflow Flow meter, 33 ... Treated water quality meter

Claims (11)

Regarding a water treatment process having a step of injecting a chemical into the water to be treated, a prediction unit that predicts the injection amount of the chemical into the water to be treated based on the prediction information about the raw water flowing into the water treatment process.
A support information generation unit that generates support information that displays information indicating the prediction result of the injection amount in association with the information used for predicting the injection amount.
A driving support device equipped with. The prediction unit predicts the water quality of the treated water based on the prediction result of the injection amount.
The support information generation unit generates support information for displaying the information indicating the prediction result of the water quality and the information indicating the prediction result of the injection amount in association with each other.
The driving support device according to claim 1. The prediction unit predicts the water quality of the treated water based on a predetermined specified value of the chlorine injection amount.
The driving support device according to claim 2. The support information generation unit generates support information that displays the prediction result of the injection amount and the actual value of the injection amount in time series.
The driving support device according to any one of claims 1 to 3. The support information generation unit generates support information that displays the prediction result of the water quality and the actual value of the water quality in chronological order.
The driving support device according to any one of claims 1 to 4. The support information generation unit generates support information that displays the actual value of the raw water flowing into the water treatment process and the expected value indicated by the forecast information in chronological order.
The driving support device according to any one of claims 1 to 5. The forecast information includes forecast information regarding the flow rate or water quality of the raw water.
The driving support device according to any one of claims 1 to 6. The prediction unit predicts the injection amount under a plurality of different conditions.
The support information generation unit determines the priority of a plurality of prediction results of the injection amount predicted under the plurality of different conditions, and displays support information indicating the prediction results together with the priority of each prediction result. Generate,
The driving support device according to any one of claims 1 to 7. The prediction unit predicts the injection amount of the chemical into the water to be treated based on the prediction information and the actual value related to the control of the water treatment process.
The driving support device according to any one of claims 1 to 8. Regarding a water treatment process having a step of injecting a chemical into the water to be treated, a step of predicting the injection amount of the chemical into the water to be treated and a step of predicting the injection amount of the chemical into the water to be treated based on the prediction information about the raw water flowing into the water treatment process.
A step of generating support information for displaying the information indicating the prediction result of the injection amount in association with the information used for the prediction of the injection amount.
Driving support method with. Regarding a water treatment process having a step of injecting a chemical into the water to be treated, a step of predicting the injection amount of the chemical into the water to be treated and a step of predicting the injection amount of the chemical into the water to be treated based on the prediction information about the raw water flowing into the water treatment process.
A step of generating support information for displaying the information indicating the prediction result of the injection amount in association with the information used for the prediction of the injection amount.
A computer program that lets a computer run.

Images