Ph adjusting agent injection control device, ph adjusting agent injection control method, and computer program

Abstract

To provide a pH adjustment agent injection control device that minimizes the operating costs of a water treatment plant while maintaining appropriate water quality.SOLUTION: A pH adjustment agent injection control device 1 calculates an evaluation value of an evaluation function, which changes with the first manipulated variable of the control object and indicates an indicator related to the optimization of the control object, using multiple measured values acquired from the control object. The device then calculates an estimated phase lag value from the first manipulated variable to the evaluation value in the control object, using the respective values of the multiple components contained in the evaluation value and the phase compensation parameters corresponding to the multiple components, respectively. Using the estimated phase lag value and the evaluation value information, the device calculates gradient information of the evaluation function, which is the rate of change of the evaluation value with respect to the first manipulated variable. By integrating the gradient information, the device determines the optimal manipulation value of the first manipulated variable. The device acquires the controlled variable from the control object and calculates and outputs a second manipulated variable such that the controlled variable follows the target value, using the optimal manipulation value as the target value.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] FIELD Embodiments of the present invention relate to a pH adjuster injection control device, a pH adjuster injection control method, and a computer program. [Background technology]

[0002] In water treatment plants, coagulation and sedimentation are carried out by injecting a coagulant into the raw water to be treated. To promote successful coagulation and sedimentation, a pH adjuster injection control device is used. Conventionally, for example, in water purification plants, sewage treatment plants, or industrial wastewater treatment facilities, a coagulant is injected into the raw water to flocculate suspended solids contained in the raw water. The resulting flocs are then separated by settling to remove suspended solids. Furthermore, the sedimented water after this process is filtered through a sand filter to remove fine flocs that were not removed by settling. Here, pH adjustment using a pH adjuster is important to generate good flocs and properly remove suspended solids.

[0003] In pH adjuster injection control, if the pH adjuster injected into the raw water is an acidic agent, if the amount of acidic agent is too small, the pH of the water in the coagulation reaction will be biased toward the alkaline side, and the water quality after water treatment will not be maintained at an appropriate value. If the amount of acidic agent injected into the raw water is too large, the pH will be biased toward the acidic side, and the water quality after water treatment will not be maintained at an appropriate value. If the pH adjuster is an alkaline agent, if the amount of alkaline agent is too small, the pH of the water in the coagulation reaction will be biased toward the acidic side, and the water quality after water treatment will not be maintained at an appropriate value. If the amount of alkaline agent injected into the raw water is too large, the pH will be biased toward the alkaline side, and the water quality after water treatment will not be maintained at an appropriate value. For this reason, it is desirable to appropriately control the injection rate of the pH adjuster.

[0004] In controlling the pH adjuster injection rate, for example, methods have been proposed in which the pH of the water during coagulation is set as a target value, and the injection rate of the pH adjuster is adjusted manually or automatically to track that target pH value. Control methods include the use of tables for manual control, and general feedback control for automatic control, such as P control, PI control, and PID control. In feedback control, a target pH value during coagulation is set. However, the appropriate pH value of the water during coagulation varies depending on the raw water quality, seasonal fluctuations, and each water purification plant. In many cases, the target pH is determined based on past operational management experience, and it is difficult to say that it is always being managed appropriately.

[0005] In recent years, the use of "Extremum Seeking Control (ESC)," which has been used as a solution for plant optimal control, has been proposed to set the target pH value for the treated water during coagulation. Extremum-value control (ESC) calculates the value of the performance index to be optimized from online sensor information of the plant, which can directly measure the value, and adaptively searches for the optimal value (minimum or maximum) by changing the manipulated variable so as to maintain the performance index at the optimal value. Extremum-value control has attracted attention as a practical optimal control technique for processes involving complex phenomena that are difficult to model mathematically (e.g., water purification plant processes, sewage treatment processes, combustion processes, petrochemical processes, etc.). [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Japanese Patent Application Publication No. 2017-033104 Summary of the Invention [Problem to be solved by the invention]

[0007] The embodiments of the present invention have been made in consideration of the above circumstances, and aim to provide a pH adjuster injection control device, a pH adjuster injection control method, and a computer program that minimize the operating costs of a water treatment plant while maintaining appropriate water quality. [Means for solving the problem]

[0008] A pH adjuster injection control device according to an embodiment of the present invention includes a process measurement value acquisition unit that acquires multiple measurement values ​​from a controlled object including a pH adjuster injection process; a process evaluation value calculation unit that uses the measurement values ​​to calculate an evaluation value of an evaluation function that changes depending on a first manipulated variable of the controlled object and indicates an index related to optimization of the controlled object; a phase lag estimation unit that calculates an estimated phase lag from the first manipulated variable to the evaluation value in the controlled object using values ​​of each of multiple components included in the evaluation value and phase compensation parameters corresponding to each of the multiple components; an evaluation function gradient estimator that uses information on the phase lag estimate and the evaluation value to calculate gradient information of the evaluation function, which is the rate of change of the evaluation value with respect to the first manipulated variable; an extremum search unit that determines an optimal manipulated variable for the first manipulated variable by integrating the gradient information; and a control unit that acquires a controlled variable from the controlled object, sets the optimal manipulated variable as a target value of the controlled variable of the controlled object, and calculates and outputs a second manipulated variable so that the controlled variable follows the target value of the controlled variable. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram showing an example of a water treatment plant in which the pH adjuster injection rate is controlled by a pH adjuster injection control device of the first embodiment. [Figure 2] Figure 2 shows a schematic diagram of the mechanism by which the state of aggregation of suspended solids and the like differs depending on the pH of the water to be treated. [Figure 3] FIG. 3 is a diagram schematically illustrating an example of the configuration of the control target value determination unit shown in FIG. [Figure 4] FIG. 4 is a diagram showing in more detail a part of the control target value determination unit shown in FIG. [Figure 5]FIG. 5 is a diagram illustrating an example of a system for adjusting the target pH value of water to be treated during coagulation by extreme value control in feedback control of the pH adjuster injection rate in a water purification plant. [Figure 6] FIG. 6 is a diagram for explaining an example of an application example of the system shown in FIG. [Figure 7] FIG. 7 is a diagram for explaining the principle of extremum search by extremum control. [Figure 8] FIG. 8 is a diagram showing an example of changes in each cost included in the operating cost of the controlled plant and the control target value of the pH adjuster injection control device of one embodiment. [Figure 9] FIG. 9 is a diagram showing an example of changes in each cost included in the operating cost of the controlled plant and the control target value of the pH adjuster injection control device of one embodiment. [Figure 10] FIG. 10 is a diagram showing an example of changes in the operating cost (evaluation value) and the control target value of the controlled plant of the pH adjuster injection control device of one embodiment. [Figure 11] FIG. 11 is a diagram showing an example of changes in the operating cost (evaluation value) and the control target value of the controlled plant of the pH adjuster injection control device of one embodiment when the coagulant injection rate is changed. [Figure 12] FIG. 12 is a diagram showing an example of changes in the operating cost (evaluation value) and the control target value of the controlled plant of the pH adjuster injection control device of one embodiment when the coagulant injection rate is changed. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, a pH adjuster injection control device, a pH adjuster injection control method, and a computer program according to embodiments will be described with reference to the drawings. FIG. 1 is a diagram showing an example of a water treatment plant in which the pH adjuster injection rate is controlled by a pH adjuster injection control device of the first embodiment.

[0011] A water treatment plant is a facility that performs a solid-liquid separation process in which solids, such as suspended solids, contained in water to be treated are flocculated using a flocculant and then separated from the water by gravity settling. Hereinafter, the water to be treated by the water treatment plant or the water being treated by the water treatment plant will be referred to as "water to be treated," and water that has been treated by the water treatment plant and is ready for release or reuse will be referred to as "treated water." Hereinafter, the water to be treated that flows into the solid-liquid separation process from inside or outside the water treatment plant will be referred to as "raw water." In other words, raw water is water to be treated in its initial state, before any chemicals, such as flocculants, have been injected.

[0012] Furthermore, the application of the pH adjuster injection control device of the embodiment described below is not limited to a specific water treatment plant or water treatment facility as long as it realizes the solid-liquid separation process described above. For example, the application of the pH adjuster injection control device of the embodiment may be a water treatment plant such as a water purification plant, or may be a water treatment facility installed in various factories such as a paper factory or a food factory. For example, in a water purification plant, the raw water may be river water, dam lake water, groundwater, rainwater, sewage, etc. Furthermore, in industrial plants such as a paper factory or a food factory, the raw water may be industrial wastewater.

[0013] FIG. 1 shows a water treatment plant 100 that realizes a solid-liquid separation process using a flocculant in a water purification plant, as an example of an application of the pH adjuster injection control device. The water treatment plant 100 includes various facilities for realizing the solid-liquid separation process and a pH adjuster injection control device 1. For example, the water treatment plant 100 includes, as facilities for realizing the solid-liquid separation process, a receiving well 3, a rapid mixing basin (mixing basin) 4, a flocculation basin 5, a settling basin 6, a filtration basin 7, a pH adjuster injection device 10, a pH measurement sensor 9 during coagulation, a flocculation agent injection device 8, and a sludge basin 62. The water to be treated is conveyed from a river or a dam lake, and first arrives at the receiving well 3, after which it is sent in this order to the rapid mixing basin 4, the flocculation basin 5, the settling basin 6, and the filtration basin 7. That is, the facility located most upstream in the flow of the water to be treated is the receiving well 3, and the facility located most downstream is the filtration basin 7.

[0014] The receiving well 3 is a reservoir that stores raw water flowing into the water treatment plant 100. In the receiving well 3, solids with a relatively high specific gravity, such as plants and soil, settle due to gravity, and the supernatant water is sent to the subsequent rapid mixing basin 4 as the water to be treated.

[0015] The receiving well 3 is equipped with a raw water quality meter 31. The raw water quality meter 31 measures the quality of the raw water that has arrived at the receiving well 3. Specifically, the raw water quality meter 31 measures water quality indicators that may affect the treatment results of the solid-liquid separation process. For example, the raw water quality meter 31 measures various quantities of the raw water, such as turbidity, color, water temperature, conductivity, pH (hydrogen ion concentration index), alkalinity, and ultraviolet absorbance. The ultraviolet absorbance of the raw water can be used as an indicator of the amount of organic matter contained in the raw water, and ultraviolet light with a wavelength of 260 nm, for example, is used for this measurement. All or part of the various indicators measured by the raw water quality meter 31 are input to the pH adjuster injection control device 1 as plant data.

[0016] A flow meter 32 is provided in the distribution pipe between the receiving well 3 and the rapid mixing basin 4. The flow meter 32 measures the flow rate of the water to be treated sent from the receiving well 3 to the rapid mixing basin 4. The flow rate measured by the flow meter 32 is input to the pH adjuster injection control device 1 as plant data.

[0017] In the rapid mixing basin 4, a flocculant is injected into the water being treated delivered from the receiving well 3 using a flocculant injector 8. The rapid mixing basin 4 is a water storage tank for rapidly mixing the water being treated (hereinafter also referred to as "mixed water") into which the flocculant has been injected. Commonly used flocculants are weakly acidic, so adding a flocculant lowers the pH. Therefore, the pH of the water being treated during flocculation is lower on the acidic side compared to the pH of the raw water. The flocculant injector 8 injects a flocculant, such as a chemical agent such as polyaluminum chloride (PAC) or aluminum sulfate, into the mixed water in the rapid mixing basin 4.

[0018] Aluminum-based inorganic flocculants, such as polyaluminum chloride (PAC) and aluminum sulfate (band sulfate), are widely used as flocculants. Iron-based flocculants and cationic and anionic organic polymer flocculants may also be used. For example, suspended solids in raw water from rivers and dam lakes used in water purification plants are typically negatively charged and exist in the water, electrically repelling each other. Adding a positively charged flocculant and stirring the water creates an electrical attraction between the suspended solids and the flocculant, neutralizing the negative charge of the suspended solids. This reduces the repulsive force, facilitating the bonding of suspended solids and promoting flocculation. The pH of the water being treated during flocculation significantly affects the efficacy of the flocculant and flocculation.

[0019] The pH adjuster injector 10 injects a pH adjuster into the water being treated between the receiving well 3 and the rapid mixing basin 4. pH adjusters include, for example, acids such as sulfuric acid and hydrochloric acid, carbon dioxide, and bases such as caustic soda and hydrated lime. By injecting the pH adjuster into the water being treated as it is sent from the receiving well 3 to the rapid mixing basin 4, the pH of the water being treated is adjusted so that the pH of the water being treated during coagulation after the injection of coagulant in the downstream rapid mixing basin 4 is within an appropriate pH range. The pH of the water being treated during coagulation is measured by the coagulation pH sensor 9 and input to the pH adjuster injection control device 1.

[0020] The rapid mixing basin 4 is equipped with a rapid mixer 41. The rapid mixer 41 agitates the water to be treated into which a coagulant has been injected in the rapid mixing basin 4. For example, the rapid mixer 41 is a flash mixer. The rapid mixer 41 may operate at a constant agitation speed, or may be capable of adjusting the agitation speed by controlling a motor. In the rapid mixing basin 4, minute flocs are formed in the water being treated by the injection of a coagulant and the agitation by the rapid mixer 41. The water being treated containing these minute flocs is sent to the subsequent flocculation basin 5, where further agglomeration of the flocs is promoted in the facilities following the flocculation basin 5.

[0021] A blended water quality meter 42 is provided in the distribution pipe between the rapid blending basin 4 and the flocculation basin 5. The blended water quality meter 42 measures the quality of the blended water. Specifically, the blended water quality meter 42 measures the electrophoretic velocity of flocs in the blended water as an index value of the flocculation state. The value measured by the blended water quality meter 42 is input to the pH adjuster injection control device 1 as plant data.

[0022] Typically, suspended solids in water have a negatively charged surface, and the repulsive force between the negatively charged solids allows them to remain stable in the water. This causes the water to become turbid. On the other hand, flocculants are positively charged in water. Therefore, when a flocculant is injected into water containing suspended solids, the flocculant adheres to the suspended solids. The flocculant that adheres to the suspended solids neutralizes (hereinafter referred to as "neutralizes") the negative charge of the suspended solids, bringing the surface charge of the suspended solids closer to 0 mV. As the surface charge of the suspended solids approaches 0 mV, the zeta potential also approaches 0 mV. Therefore, the flocculant weakens the repulsion between suspended solids and increases the number of collisions. Due to the action of the flocculant, colliding flocs gradually agglomerate, forming larger flocs. During this flocculation reaction, the pH of the water during flocculation affects the charge state of the flocculant itself and the rate of agglomeration. It is recommended that a near-neutral pH of around 7.0 is desirable for water purification plants, but the exact appropriate value varies depending on the raw water quality and treatment conditions, so in many cases each water purification plant sets an appropriate range. Therefore, even with the same coagulant injection rate, the pH of the treated water at the time of coagulation will differ, resulting in different floc formation, so it is important to properly manage the pH of the treated water at the time of coagulation.

[0023] Flocculation basin 5 is a water storage tank for forming larger flocs in the water to be treated. Flocculation basin 5 is equipped with slow speed agitators 54, 55, and 56, and agitation of the water to be treated by the slow speed agitators 54, 55, and 56 promotes further agglomeration of the flocs. For example, as shown in Figure 1, flocculation basin 5 is divided into three agitation basins 51, 52, and 53, and a slow speed agitator 54, 55, and 56 is installed in each of the agitation basins 51, 52, and 53. For example, the slow speed agitators 54, 55, and 56 are flocculators. Of the three agitation basins 51, 52, and 53, agitation basin 51 is located most upstream in terms of the flow of the water to be treated, and agitation basin 53 is located most downstream.

[0024] The water to be treated sent from the rapid mixing basin 4 flows into the agitation basin 51. In the agitation basin 51, the water to be treated is agitated by the slow speed agitator 54, causing fine flocs to repeatedly collide with each other, resulting in the formation of flocs with larger particle sizes. The water to be treated in the agitation basin 51 is sent to the downstream agitation basin 52 after being agitated for a predetermined period of time.

[0025] The water to be treated sent from agitation basin 51 flows into agitation basin 52. In agitation basin 52, the water to be treated is agitated by slow speed agitator 55, resulting in the formation of flocs with larger particle sizes. Here, if the agitation intensity is too strong, the agglomerated flocs will be destroyed, so slow speed agitator 55 agitates the water to be treated with a weaker intensity than slow speed agitator 54. This promotes further agglomeration of the flocs. The water to be treated in agitation basin 52 is sent to a downstream agitation basin 53 after being agitated for a predetermined period of time.

[0026] The water to be treated sent from agitation basin 52 flows into agitation basin 53. In agitation basin 53, the water to be treated is agitated by slow speed agitator 56, thereby forming flocs with larger particle sizes. Here too, slow speed agitator 56 agitates the water to be treated with less strength than slow speed agitator 55 so as not to destroy the agglomerated flocs. This promotes further agglomeration of the flocs. The water to be treated in agitation basin 53 is sent to settling basin 6 at the downstream stage after being agitated for a predetermined period of time.

[0027] The settling basin 6 is a water storage tank that stores the water to be treated that flows in from the flocculation basin 5. By storing the water to be treated in the settling basin 6 for a predetermined time, large flocs formed in the flocculation basin 5 settle by gravity. For example, the water to be treated is stored in the settling basin 6 for about three hours. This separates the flocs from the water to be treated, and the supernatant water is sent to the subsequent filtration basin 7. The most downstream part of the settling basin 6 may be equipped with equipment that performs additional treatments such as ozonation and biological activated carbon treatment on the water to be sent to the filtration basin 7. The flocs that have settled in the settling basin 6 are extracted as sludge by a sludge extraction pump 63 and sent to a sludge treatment facility such as a wastewater treatment basin 62.

[0028] In addition, a sedimentation basin water quality meter 61 is provided downstream of the sedimentation basin 6. The sedimentation basin water quality meter 61 measures the quality of the water to be treated sent to the filtration basin 7. Specifically, the sedimentation basin water quality meter 61 measures various index values ​​related to the treatment results of the solid-liquid separation process. For example, the sedimentation basin water quality meter 61 measures the turbidity of the water to be treated as an index value related to the treatment results of the solid-liquid separation process. The various index values ​​measured by the sedimentation basin water quality meter 61 are input to the pH adjuster injection control device 1 as plant data.

[0029] The filter basin 7 is a reservoir equipped with a filtration system that filters the water to be treated that flows in from the sedimentation basin 6. In the filter basin 7, minute solids remaining in the water to be treated are separated by filtration. The filtered water to be treated is stored in a clean water reservoir or the like as treated water and used as tap water. A filter basin water quality meter 71 is provided downstream of the filter basin 7. The filter basin water quality meter 71 measures the quality of the water to be treated filtered by the filter basin 7. Specifically, the filter basin water quality meter 71 measures various indicators related to the results of solid-liquid separation of the water to be treated in the final stage of treatment in the water treatment plant 100. For example, the filter basin water quality meter 71 measures the turbidity of the water to be treated as an indicator related to the results of the solid-liquid separation process. The various indicators measured by the filter basin water quality meter 71 are input as plant data to the pH adjuster injection control device 1.

[0030] The filter basin 7 is also equipped with a water level gauge 72 that measures clogging of the filter basin. When the filter basin becomes clogged, the water level at the top of the filter basin rises, and when it reaches a certain level, filtration is stopped and cleaning is performed. Therefore, the faster the water level rises in the filter basin, the more frequently cleaning is required, and the higher the cleaning costs. Factors that promote clogging of the filter basin include the turbidity at the outlet of the settling basin and the aluminum concentration. Aluminum exists as a result of residual aluminum-based coagulants, such as polyaluminum chloride, which are added as a coagulant. The water level information measured by the filter basin water level gauge 72 is input into the pH adjuster injection control device 1 as plant data.

[0031] For example, in water purification plants, the pH of the treated water during coagulation is controlled to ensure effective coagulation. River water and dam lake water, which are typically used as raw water at water purification plants, often have a slightly alkaline pH due to the influence of the plants living there. Specifically, the pH ranges from 7.5 to 8.0. Furthermore, during the summer, when plant activity increases, the pH of the raw water can rise to nearly 8.5. When attempting to coagulate such raw water, the weak acidity of the coagulant results in a slight decrease in pH, but because the original raw water pH is high, the pH of the treated water during coagulation may only decrease to near 8.0. If the pH of the treated water during coagulation is kept on the alkaline side, good flocs will not form, and the turbidity at the outlet of the sedimentation basin, which is an indicator of treated water quality, may not fall below the specified management target value and may even deteriorate.

[0032] Figure 2 shows a schematic diagram of the mechanism by which the state of aggregation of suspended solids and the like differs depending on the pH of the water to be treated. When aluminum-based flocculants are added to water, they undergo hydrolysis and exist in various forms within the water. Each form of hydrolyzed flocculant has different effects on the flocculation reaction, and the ratio of each form changes depending on the pH of the water being treated when suspended solids are flocculating. Figure 2 shows a schematic diagram of an example of the ratio of various forms of hydrolyzed flocculant over a range of pH levels in the water being treated.

[0033] Aluminum-based flocculants added to water come in three main forms: The first is a monomeric form that is not very large in size, and this form does not contribute significantly to the aggregation reaction. The second type is aluminum, which contributes to charge neutralization. This type of flocculant contributes to the charge neutralization reaction, and its proportion varies depending on the pH of the water being treated when suspended solids and other substances are flocculated. Specifically, the more acidic the pH of the water being treated when suspended solids and other substances are flocculated, the greater the proportion of the type that contributes to the charge neutralization reaction. The third form is aluminum, which is mentioned in the bridging action. This form of flocculant contributes to the enlargement of flocs, and its proportion changes depending on the pH of the water being treated when suspended solids are flocculating. Specifically, the more acidic the pH of the water being treated becomes during flocculation, the less of the form of flocculant that contributes to the bridging action decreases.

[0034] Because the form of the flocculant after hydrolysis has the above characteristics, in the flocculation reaction, in order to proceed with appropriate charge neutralization and simultaneously coarsen the flocs through cross-linking, it is necessary to control the pH of the water to be treated so that it is appropriate for coagulation. Even if charge neutralization progresses, if the cross-linking action does not progress, the flocs will not coarsen and many small particles will remain, and even if there is a lot of cross-linking flocculant, fine particles whose charges have not been neutralized will remain.

[0035] Furthermore, this effect of the coagulation reaction is also affected by the type, components, and composition of the turbidity in the raw water. Therefore, it is necessary to adjust the pH of the water to be treated according to the type, components, and composition of the turbidity in the raw water. Specifically, for raw water containing many particles that require charge neutralization, it is desirable to have a large amount of the form of coagulant that contributes to charge neutralization in the water to be treated, so the coagulation reaction is carried out on the acidic side. On the other hand, when charge neutralization is not as necessary but flocs are difficult to grow, it is desirable to have a large amount of the form of coagulant that contributes to cross-linking in the water to be treated, so the coagulation reaction is carried out on the alkaline side.

[0036] When adjusting the pH of the treated water to the appropriate pH during coagulation, pH adjustment can sometimes be done using both an acid and a coagulant. For example, if the raw water pH is 8.0 and the treated water pH is 7.0 after appropriate coagulation, adding an acid will significantly lower the pH, but the coagulant will also lower the pH. That is, there are two cases: using an acid to lower the pH of the treated water from 8.0 to 7.2, and then using a coagulant to lower it from 7.2 to 7.0; or using a small amount of acid to lower the pH of the treated water from 8.0 to around 7.5, and then using a coagulant to lower it from 7.5 to 7.0. Generally, acid agents have a higher unit cost than chemicals, so there is a desire to limit the amount of acid agent used, but adding too much coagulant will result in a large amount of sludge, which will increase sludge disposal costs.As such, it is desirable to consider the injection ratio of acid agent and coagulant, including other costs, but such adjustments are difficult to make in real time.For these reasons, the appropriate pH of the treated water during coagulation cannot be determined in general, and is often set empirically at each water purification plant.

[0037] Here, the pH adjuster injection control device 1 of this embodiment employs a feedback control method to control the pH adjuster injection rate based on the measurement value of the pH measurement sensor 9 during coagulation and a set coagulation pH target value. The coagulation pH target value is set arbitrarily to a recommended value for each water purification plant. The pH measurement sensor 9 during coagulation outputs the pH of the water to be treated during coagulation to the pH adjuster injection control unit 12. This pH of the water to be treated during coagulation is used as the controlled variable (PV) in the feedback control.

[0038] In a water treatment plant 100 having such various water treatment facilities, the pH adjuster injection control device 1 controls the injection rate of the pH adjuster injected by the pH adjuster injection device 10 (hereinafter referred to as the "pH adjuster injection rate") based on input plant data. Generally, the pH adjuster injection rate is the amount of pH adjuster injected per unit time relative to the flow rate of the water to be treated per unit time. The pH adjuster injection rate is also converted into the injection amount of the pH adjuster (hereinafter referred to as the "pH adjuster injection amount") using the flow rate of the water to be treated per unit time. The configuration of the pH adjuster injection control device 1 of this embodiment will be described in detail below, but the pH adjuster injection rate can be replaced with the pH adjuster injection amount as appropriate.

[0039] The pH adjuster injection control device 1 of this embodiment controls the pH adjuster injection rate to a target value for the pH of the treated water during coagulation, taking into account the pH decrease caused by the acid agent and the pH decrease caused by the coagulant. This control method is a feedback control method that adjusts the pH adjuster injection rate based on the pH of the treated water during coagulation after the injection of the pH adjuster.

[0040] Next, we will describe the setting of the coagulant injection rate by the coagulant injection device 8 when controlling pH adjuster injection in this embodiment. Typically, the quality of raw water used at a water purification plant is stable under normal conditions, except for sudden changes in the quality of raw water from rivers or dam lakes due to heavy rain or typhoons. Therefore, water purification plants are often operated without frequent changes to the coagulant injection rate. In water purification plants, operators typically conduct beaker tests to determine the coagulant injection rate offline, and then set the resulting injection rate (the offline determined injection rate) as the actual injection rate for the water purification plant. Alternatively, the coagulant injection rate is set in proportion to the turbidity of the raw water. Even with these methods, the coagulant injection rate is not significantly changed under normal conditions because the quality of the raw water is stable. In other words, water purification plants are operated at a nearly constant coagulant injection rate. In this embodiment, the coagulant injection rate is kept almost constant, and the present invention relates to a pH adjuster injection control device that determines the pH of the water to be treated during coagulation at which cost reduction is most achieved.

[0041] The pH adjuster injection control device 1 includes at least one processor such as a CPU (Central Processing Unit) and a storage unit (memory or auxiliary storage device) storing a program executed by the processor, and executes the program. By executing the program, the pH adjuster injection control device 1 functions as a device including a control target value determination unit 11 and a pH adjuster injection control unit 12. Note that all or part of the functions of the pH adjuster injection control device 1 may be realized using hardware such as an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), or an FPGA (Field Programmable Gate Array). The program may be recorded on a computer-readable recording medium. Examples of the computer-readable recording medium include portable media such as a flexible disk, a magneto-optical disk, a ROM, and a CD-ROM, and storage devices such as a hard disk built into a computer system. The program may be transmitted via a telecommunications line.

[0042] The control target value determination unit 11 determines a control target value for the pH of the water to be treated during coagulation when determining the pH adjuster injection rate of the pH adjuster injector 10 using a feedback control method. In this embodiment, the control target value determination unit 11 determines the pH of the water to be treated during coagulation as the control target value, with the aim of maintaining the turbidity of the settling basin 6 at or below a predetermined management target value while minimizing operating costs even when the raw water quality gradually changes seasonally. The control target value determination unit 11 outputs the determined target value for the pH during coagulation to the pH adjuster injection control unit 12.

[0043] The pH adjuster injection control unit 12 acquires a control variable (the pH of the water to be treated during coagulation) from the controlled plant (the coagulation pH measurement sensor 9) and performs feedback control, which is a control method for varying a manipulated variable (second manipulated variable) based on the deviation between the control variable and a control target value, thereby making the controlled variable follow the control target value. For example, the pH adjuster injection control unit 12 performs feedback control such as P control (Proportional Controller), PI control (Proportional-Integral Controller), or PID control (Proportional-Integral-Differential Controller). The pH adjuster injection control unit 12 determines the pH adjuster injection rate of the pH adjuster injector 10 as the manipulated variable (second manipulated variable, MV) of the controlled plant based on the coagulation pH target value (control target value) determined by the control target value determination unit 11 and input plant data (specifically, measurement data from the flow meter 32 and the coagulation pH measurement sensor 9). The pH adjuster injection control unit 12 notifies the determined pH adjuster injection rate to the pH adjuster injector 10. The pH adjuster injection rate is controlled, for example, every 5 minutes.

[0044] FIG. 3 is a diagram schematically illustrating an example of the configuration of the control target value determination unit shown in FIG. FIG. 4 is a diagram showing in more detail a part of the control target value determination unit shown in FIG. The control target value determination unit 11 sets the control target value of the pH of the water to be treated during coagulation using a technique called extreme value control. First, the plant to be controlled is any plant having an operation input U and a process output Z. In this embodiment, a water purification plant including a pH adjuster injection process will be described as an example of the plant to be controlled.

[0045] The manipulated variable input U is the control target value for the pH adjuster injection process, i.e., the target value for the pH of the treated water during coagulation. The actual manipulated variable (second manipulated variable) is the pH adjuster injection rate, but because this control system has a two-stage cascade configuration, the first manipulated variable (manipulated variable command value) adjusted by extreme value control is the target value (control target value) for the pH of the treated water during coagulation. The outputs Y are the pH adjuster injection rate, PAC (coagulant) injection rate, sludge generation volume, filter cleaning frequency, turbidity at the sedimentation basin outlet, and turbidity at the filter basin outlet.

[0046] The control target value determination unit 11 includes a process measurement value acquisition unit 110, a process evaluation value calculation unit 120, a demodulation dither signal generation unit 130, an evaluation function gradient estimation unit 140, a gradient estimation amount normalization unit 160, an extremum search unit (optimal operation amount adaptive adjustment unit) 170, a modulation dither signal generation unit 180, an output unit 190, and a phase lag estimation unit 1000.

[0047] The process measurement value acquisition unit 110 first acquires measurement information required to calculate evaluation quantities and constraint conditions for the controlled plant. For example, when an operating cost including the total cost of chemicals, sludge disposal, and filter cleaning is set as an evaluation function, the process measurement value acquisition unit 110 acquires, as measurement information, the pH adjuster injection rate, PAC (coagulant) injection rate, sludge generation volume, and filter cleaning frequency. Furthermore, when constraints are set on the turbidity at the outlets of the settling basin 6 and the filtration basin 7, the process measurement value acquisition unit 110 also measures the turbidity values ​​at the outlets of the settling basin 6 and the filtration basin 7 at predetermined intervals and saves them as time-series data in a predetermined format.

[0048] The process evaluation value calculation unit 120 calculates the evaluation value of a preset evaluation function in real time using the information acquired by the process measurement value acquisition unit 110. As an evaluation function, for example, the operating cost is defined as the sum of the chemical cost (pH adjuster cost and PAC cost), the sludge disposal cost, and the filter basin cleaning cost. The chemical cost is calculated by multiplying the time-series data of the injection amounts of the pH adjuster and PAC by coefficients such as the chemical unit price and dilution rate, and can be obtained as time-series data. The sludge disposal cost is calculated by multiplying the amount of sludge generated by the sludge disposal unit cost, and time-series data of the sludge disposal cost can be obtained.

[0049] The filter basin 7 is usually cleaned when the water level or resistance (filtration resistance) of the filter basin exceeds a predetermined threshold, so the timing of cleaning can be determined from the time series data of the resistance and cleaning history data, and the cleaning cost at the time of cleaning can be obtained from the history data. The cost of cleaning the filter basin 7 is considered to be the cost from the previous cleaning to the next cleaning, so by distributing this cleaning cost evenly over that period, the cleaning cost can be converted into time series data. However, while this conversion can be easily performed on past performance data, in real time the timing of the next cleaning is uncertain, so some ingenuity is required, such as estimating the timing of the next cleaning while monitoring the rate of change in the water level (resistance) and then converting this into time series data of the cleaning cost in real time. The sum of the various costs converted into time-series data as described above is the operating cost, and the process evaluation value calculation unit 120 can obtain the operating cost in real time using the above method.

[0050] The process evaluation value calculation unit 120 uses water quality constraints as constraint conditions for the evaluation function. In this case, the process measurement value acquisition unit 110 acquires turbidity at the outlet of the settling basin 6 and the outlet of the filtration basin 7 of the water treatment plant shown in Figure 1 as time-series data, and the process evaluation value calculation unit 120 incorporates these turbidity constraint values, such as a constraint condition of 0.8 degrees or less for the turbidity of the settling basin, into the evaluation function. These constraints can be set arbitrarily for each water purification plant.

[0051] In extremum control, constraints cannot be handled directly, but they can be converted so that they can be handled as an evaluation function. Here, they are treated as an evaluation function using the concept of a penalty function, which is well known in the field of optimization. That is, for example, the following conversion is performed. Wcost=max(0,a×(exp(T-Tlim)-1)) (1) Here, T is the measured turbidity value, Tlim is the upper limit of turbidity, and a (>0) is a design parameter. Wcost is a converted evaluation function, and the water quality cost is calculated by converting equation (1) for the turbidity at the outlet of the settling basin 6 and the turbidity at the outlet of the filtration basin 7, and then adding them up. Equation (1) means that when turbidity T is less than or equal to the upper limit of turbidity Tlim, the result is 0, and when turbidity T exceeds the upper limit of turbidity Tlim, the cost Wcost rises rapidly (exponentially), making it a type of penalty function.

[0052] By adding the water quality cost considered by the penalty function to the operating cost mentioned above, the total cost J can be defined as shown in the following equation (2). Total cost J = Chemical cost + Sludge disposal cost + Filter basin cleaning cost + Water quality cost (2) Once the evaluation function is properly set by the above process, the value of the evaluation function can be measured and calculated in real time at a predetermined control cycle, thereby obtaining evaluation quantities that change from moment to moment.

[0053] The demodulation dither signal generator 130 generates a periodic signal (demodulation dither signal) required to estimate the gradient (first-order differential, Jacobian) of the evaluation function through extremum control. A sine wave is typically used as the demodulation dither signal, but it does not necessarily have to be a sine wave; any periodic signal, such as a square wave or a triangular wave, may be used. The demodulation dither signal must have the same period and waveform shape as the modulation dither signal, which will be described later. Here, the demodulation dither signal D(t) when a sine wave is used as the dither signal, is shown in equation (3). D(t)=sin(ωt+φ) (3)

[0054] Here, ω is the frequency of the dither signal, and φ is the phase lag (phase lag estimate value). In this embodiment, the demodulation dither signal D(t) has a phase shift of φ due to the phase lag estimate value φ calculated by the phase lag estimator 1000 (described later), as shown in equation (3). By performing phase compensation as shown in equation (3), the performance of extremum control is improved.

[0055] The evaluation function gradient estimator 140 estimates the gradient of the evaluation value with respect to the manipulated variable (first manipulated variable). As shown in Fig. 5, the evaluation function gradient estimator 140 includes a high-pass filter HPF and a low-pass filter LPF, and an adder between the blocks of the high-pass filter HPF and the low-pass filter LPF that adds the demodulation dither signal D(t) generated by the demodulation dither signal generator 130 to the output of the high-pass filter HPF.

[0056] The extreme value (local optimum) of the evaluation index value is the object of search, so it is naturally unknown. The high-pass filter HPF is a configuration introduced to forcibly set the extreme value to approximately 0 on the assumption that this unknown extreme value will not change, or if it does change, it will change very slowly. The high-pass filter HPF is not essential in the theoretical configuration of extreme value control, but it is usually incorporated because it contributes to improving control performance.

[0057] Furthermore, it is known from theoretical analysis that the periodic average value of the evaluation value, or the signal obtained by passing the evaluation value through a high-pass filter, adding a demodulation dither signal to the signal, and then passing it through a low-pass filter, is a signal (gradient information) proportional to the gradient of the evaluation function. Therefore, the output signal of the low-pass filter (LPF) can be regarded as the gradient of the evaluation function value (more precisely, a signal proportional to the gradient).

[0058] The gradient estimator normalization unit 160 normalizes the gradient estimate using a normalization signal generated by an included normalization signal generator. That is, the gradient estimator normalization unit 160 generates G_n(t) by applying the normalization signal to the gradient estimate G(t) estimated by the evaluation function gradient estimator 140. Note that in this embodiment, the gradient estimator normalization unit 160 is not an essential component and may be omitted.

[0059] The extremum search unit 170 searches for an extremum of the evaluation function using an integrator. That is, the extremum search unit 170 can perform extremum search by multiplying the gradient estimate G(t) or the normalized gradient estimate G_n(t) by a constant called integral gain K through an integrator. The integral gain K is an important parameter that determines the convergence speed of the extremum search. By introducing phase lag compensation using the phase lag estimate φ, the value of the integral gain K can be increased. Increasing the integral gain K can improve the control performance (convergence speed). In other words, if the value of the integral gain K is made too large without phase lag compensation, the stability of the extremum control system may be impaired. In contrast, by performing phase lag compensation, it is possible to increase the integral gain K, thereby improving the control performance.

[0060] The configuration of multiplying the integral gain K through an integrator is almost the same as an optimization algorithm called the gradient method, and the integral gain K corresponds to a parameter called the learning rate in the gradient method.

[0061] The modulation dither signal generator 180 generates a modulation dither signal. This modulation dither signal may be any periodic signal, but it must have the same waveform as the demodulation dither signal generated by the demodulation dither signal generator 130. When a sine wave signal corresponding to equation (3) is used, the modulation dither signal M(t) of equation (4) is used. M(t)=Asin(ωt) (4)

[0062] Here, A is the amplitude of the dither signal, and is an adjustable parameter. This dither signal M(t) for modulation is an essential element in dither signal-driven extremum-seeking control. In this embodiment, there is a phase difference φ between the signal in equation (3) and the signal in equation (4), and the phase delay is compensated for. The effect of this phase compensation makes it possible to set the frequency ω of the dither signal higher (i.e., the dither period shorter) or the integral gain K described above higher, thereby improving the control performance (convergence speed) of extremum-seeking.

[0063] The output unit 190 outputs an operation amount command (control target value) for the controlled object to the pH adjuster injection control unit 12. This operation amount command signal is obtained by adding the output (optimum operation amount) of the extreme value search unit 170 to the modulation dither signal M(t), so that the first operation amount is periodically driven by the modulation dither signal M(t) and an extreme value is simultaneously searched for.

[0064] Next, an example will be described in which extreme value control (ESC) is applied, in which the pH of the water to be treated during coagulation is set to a control target value. Fig. 5 is a schematic diagram showing an example of a system that adjusts the target pH value of the water to be treated during coagulation using extreme value control in feedback control of the pH adjuster injection rate at a water purification plant. The example shown in Fig. 5 is a system that can switch between a manually set target pH value (SV) of the water to be treated during coagulation and a target value (SV) of the charge state that is automatically adjusted in real time using extreme value control.

[0065] At water purification plants, coagulants called PAC are injected to coagulate and sediment the turbidity in rivers and other waters, and there is a demand for operations that maintain the turbidity of the water flowing out of tanks called settling basins and sand filters at or below a certain standard value, while minimizing the costs of injecting pH adjusters and PAC (e.g., chemical costs) and other operational costs as much as possible.

[0066] In the application of extreme value control in Figure 5, the dosage amount (injection rate) of the pH adjuster is not directly determined by extreme value control, but rather the pH of the treated water during coagulation is used as the controlled variable (PV), and the pH adjuster injection rate is adjusted using PI (proportional-integral) control so that it follows the target pH value of the treated water during coagulation. The control target value for PI control (the target pH value of the treated water during coagulation) is adjusted using extreme value control. By automatically adjusting the control target value (SV) in real time using extreme value control, the pH adjuster injection rate is controlled so that the controlled variable (PV) follows the real-time target value (SV) using feedback control, making it possible to respond more quickly to changes in the coagulation pH due to fluctuations in raw water quality.

[0067] In addition, the evaluation function is set as the total cost, which is the sum of costs (components) related to operation, such as the cost of coagulant chemicals, cleaning costs for downstream filtration basins, and sludge disposal costs, and is designed as an extreme value control system with constraints set to maintain the turbidity of the settling basin and filtration basin below the control value.By applying extreme value control to setting the control target value for the floc charge state in this way, it is possible to achieve automatic plant control that minimizes operating costs while maintaining the control value (constraint) of water quality.

[0068] FIG. 6 is a diagram for explaining an example of an application example of the system shown in FIG. An example in which the coagulant is injected at a constant injection rate is shown in Figure 6, and the injection rate of the coagulant is not shown here. When the target value (SV) of the pH of the water to be treated during coagulation is periodically varied by extreme value control, the sulfuric acid injection rate changes periodically, and as a result, the chemical cost fluctuates, and therefore the evaluation value fluctuates periodically.

[0069] Here, increasing the SV—that is, shifting the treated water toward alkaline—decreases the sulfuric acid injection rate and the evaluation value, indicating that the SV and evaluation value are inversely related. In this case, increasing the SV to lower the evaluation value is the action of extreme value control. Here, increasing the SV decreases the sulfuric acid injection rate and reduces costs, but the pH of the treated water during coagulation shifts toward alkaline, changing the coagulation state and gradually increasing the turbidity at the sedimentation basin outlet. After that, when the turbidity rises to the settling basin outlet turbidity management target value, extreme value control causes the evaluation value to rise sharply, so turbidity is controlled (feedback control) to stick to the management target value. The pH of the treated water during coagulation at this point is the appropriate pH of the treated water during coagulation that minimizes costs under current operations.

[0070] FIG. 7 is a diagram for explaining the principle of extremum search by extremum control. In FIG. 7, the left side shows an example of the relationship between the manipulated variable and the evaluation variable in the principle of extreme value control, and the right side shows an example of the relationship between the manipulated variable and the evaluation variable when extreme value control is applied to an actual control target. In the left and right diagrams of Figure 7, the horizontal axis represents the manipulated variable operated by extreme value control, which in this embodiment is the target value (first manipulated variable) of the pH of the water to be treated during coagulation. The vertical axis represents the evaluation function (evaluation variable) to be optimized (minimized) by extreme value control, which in this embodiment is the operating cost (total cost). It is assumed in advance that there is some kind of relationship between the manipulated variable and the evaluation function, and that there will be extreme values ​​(local minimum values), and in Figure 7, it is assumed that there is a functional relationship that is convex downward.

[0071] Here, for the sake of explanation, Figure 7 shows the function shape as a graph, but when extremum control is actually performed, although the evaluation value for each manipulated variable at the time of control can be obtained in real time, the shape of the evaluation function cannot be grasped in real time and is in an unknown state. In other words, it is known that an extremum exists somewhere, but control is performed without knowing where that extremum is. In such a situation, extremum control provides a control algorithm for searching for the extremum (local optimum, global minimum in Figure 7) of a function with a downward convex shape, for example.

[0072] When the manipulated variable is driven by a periodic dither signal such as a sine wave, if the manipulated variable is to the right of the extreme value, the movement of the manipulated variable driven by the dither signal and the movement of the evaluation variable (evaluation value) acquired in real time will move in sync with each other, i.e., they will move in phase, to the right of the extreme value of the downward-convex function. In other words, if the target pH value of the treated water during coagulation is increased, operating costs will increase. On the other hand, if the manipulated variable is to the left of the extreme value of the downward-convex function, the movement of the manipulated variable driven by the dither signal and the movement of the evaluation variable (evaluation value) acquired in real time will move in opposite directions, i.e., they will move in antiphase. In other words, if the target pH value of the treated water during coagulation is increased, operating costs will decrease.

[0073] By using this information, it is possible to determine which side of the extreme value the manipulated variable is during control operation, and accordingly, if it is to the right of the extreme value, the manipulated variable is decreased, and if it is to the left of the extreme value, the manipulated variable is increased, thereby enabling extremum search.

[0074] More precisely, the relationship between the manipulated variable and the evaluation variable is actually as shown on the right side of Figure 7. That is, when the controlled object has dynamics, there is a time lag (a phase lag if driven by a sine wave) between the manipulated variable on the horizontal axis and the evaluation variable on the vertical axis. Therefore, even if the manipulated variable is on the right side of the extreme value of a downward-convex function, the manipulated variable and the evaluation variable do not move in phase; rather, the evaluation variable changes with a phase lag relative to the manipulated variable. Specifically, in water purification plants, for example, there is a lag time until the change is transmitted by the water flow, so after the target pH value of the treated water during coagulation is changed, that change changes the coagulation state, and the change in coagulation state changes the turbidity at the outlet of the settling basin, causing a delay before the change is transmitted downstream.

[0075] Similarly, when the manipulated variable is on the left side of the extremum of a downward-convex function, the manipulated variable and the evaluation variable do not move in antiphase, but the evaluation variable changes with a phase lag relative to the manipulated variable. According to the diagram on the right side of Figure 7, when the phase lag becomes 90 degrees, the relationship that was in phase in the diagram on the left becomes antiphase, and the relationship that was antiphase in the diagram on the left becomes inphase.

[0076] This phase delay needs to be minimized. To achieve this, the controlled object needs to be a fast enough process that it can be considered to be almost a static process. If the controlled object responds quickly enough and can be considered to be an approximately static process, the relationship between the manipulated variable and the evaluation variable shown in the left diagram of Figure 7 can be realized in an actual control algorithm.

[0077] However, actual controlled objects do not necessarily respond quickly enough to be considered static. To address this issue, focusing on the fact that response speed is relative, it is possible to approximately treat the actual controlled object as static by adjusting the frequency of the dither signal. Since the response speed of an actual controlled object is a characteristic specific to that controlled object, it is necessary to design the dither signal frequency slowly so that the response speed can be determined to be sufficiently fast. By doing so, even if there is a response delay due to the controlled object, it can be made sufficiently small in terms of phase delay. This leads to the trade-off between "stability" and "control performance" mentioned earlier. In other words, if stability is achieved by designing the dither signal frequency slowly to eliminate the influence of phase shift, the control performance will be degraded due to slow control.

[0078] In order to achieve both "stability" and "control performance (convergence speed)" in extremum control, for example, phase compensation can be used to search for a (local) optimum that maximizes convergence speed while maintaining stability. For example, by estimating the phase lag in extremum control and compensating for it, it is possible to improve the convergence speed while maintaining the stability of extremum control. This can improve the degradation of extremum search performance caused by slow convergence speed. For example, simply by conducting a simple response test such as a step response test used when adjusting PID control, which is widely used in industry, it is possible to configure a phase compensation device and create an extremum control system that can be used in practice.

[0079] In a control system that continuously measures the pH of the water being treated during coagulation after the coagulant and pH adjuster have been injected as described above, and feedback controls the pH adjuster injection rate, it is effective to determine the target pH value of the water being treated during coagulation using extreme value control, and to use a phase compensation function to improve the stability and control performance (convergence speed) that are issues with extreme value control.

[0080] Here, the breakdown (components) of operating costs, such as pH adjuster cost, coagulant cost, sludge disposal cost, and filter basin cleaning cost, have different delay times after changing the target pH value of the treated water during coagulation, which is the manipulated variable (first manipulated variable) for extreme value control. In other words, they each have different phase shifts, so it is desirable to consider these multiple phase shifts simultaneously. For example, the locations where each cost included in the operating cost of the controlled plant (chemical cost, filter basin cleaning cost, sludge disposal cost, water quality constraints) occurs can be upstream or downstream in the flow of treated water within the treatment plant. Therefore, the time it takes for a response to a change in the control target value (SV) to appear also differs for each cost.

[0081] 8 and 9 are diagrams showing an example of changes in the control target value and each cost included in the operating cost of the controlled plant of the pH adjuster injection control device of one embodiment. 8 and 9, the target value of the pH of the water to be treated during coagulation is shown as a value corresponding to the control target value SV. The pH adjuster injection rate, which corresponds to the chemical cost, responds relatively quickly to changes in the control target value SV operated by extreme value control, with a delay of, for example, 10 to 30 minutes. Therefore, fluctuations in the cost of the pH adjuster (sulfuric acid in this case) do not have a large phase delay from the SV.

[0082] The filter basin cleaning cost is measured after the effect on the water level of filter basin 7 has been felt, so it responds with a delay of, for example, 3 to 6 hours to a change in the control target value SV. Note that Figure 9 shows the change in the filter basin cleaning cost, but since the effect on filter clogging occurs after a change in the turbidity at the outlet of settling basin 6 occurs, the delay between the two is roughly the same. This is because the water level of filter basin 7 is affected by the turbidity at the outlet of settling basin 6, and the change in the control target value SV only changes after the effect on the turbidity at the outlet of settling basin 6 has been felt.

[0083] The sludge disposal cost (amount of sludge generated) responds to a change in the control target value SV with a delay midway between the response time of the pH adjuster injection rate and the response time of the filter basin cleaning cost, depending on the timing of sludge removal from the settling basin 6. Alternatively, if the sludge generation amount is calculated from the pH adjuster injection rate and the PAC injection rate, the response to a change in the control target value SV is delayed by the same amount of time as each chemical injection rate (for example, about 10 to 30 minutes).

[0084] 10 is a diagram showing an example of changes in the operating cost (evaluation value) of a plant to be controlled by a pH adjuster injection control device according to one embodiment and the control target value. Here, the target value of the pH of the water to be treated during coagulation is shown as a value corresponding to the control target value SV. When the cost of sulfuric acid, a pH adjuster, is the main component of the evaluation value, the phase lag relative to SV is small. However, when the cost of cleaning the filter basin is the main component, the phase lag relative to SV is large.

[0085] As described above, each cost included in the operating cost of the controlled plant has a different response time to a change in the control target value (SV). This results in a larger phase shift in the evaluation value calculated by adding up each cost. Considering the phase shift of each cost, it is necessary to set a longer dither signal period. This is because, if the phase shift between the control target value (SV) and the evaluation value exceeds π / 2, the minimum value control achieved by extreme value control becomes maximum value control. However, extending the dither signal period makes it difficult to improve the control performance (convergence speed) of the extreme value search, making it difficult to adjust the control target value (SV) to minimize operating costs while maintaining water quality constraints.

[0086] On the other hand, in the breakdown of operating costs, for example, in cases where the water quality management value constraint is reached, the chemical cost and sludge disposal cost are the main components of the evaluation value, and the phase shift in the evaluation value is small. Also, in the breakdown of operating costs, for example, in cases where the water level of filtration basin 7 based on the turbidity at the outlet of sedimentation basin 6 (filter basin cleaning cost) is the main component, the residence time of the treated water up to filtration basin 7 is long, and the phase shift in the evaluation value is large. The inventors of the present application have experimentally confirmed that there are multiple cases of phase shift in the evaluation value, depending on the operation of the controlled plant.

[0087] Therefore, the pH adjuster injection control device 1 of this embodiment includes a phase lag estimator 1000 that adjusts the estimated phase lag value φ according to the breakdown of costs, which are multiple components included in the operating cost (evaluation value) of the controlled plant. Specifically, the phase lag estimator 1000 sets the estimated phase lag value φ using the values ​​of each of the multiple components (costs) included in the evaluation value and the phase lag values ​​corresponding to each of the multiple components.

[0088] For example, if the principal component of the multiple components (chemical cost, sludge disposal cost, filter basin cleaning cost) included in the operating cost, which is the evaluation value, has a small phase shift, a smaller phase lag estimated value φ is set. Conversely, if the principal component of the multiple components included in the operating cost has a large phase shift, the phase lag estimator 1000 sets a larger phase lag estimated value φ. Note that the number of components that is considered to be the principal component is not limited to one, and there may be multiple components.

[0089] The phase lag estimated value φ may be switched manually by an operator, or may be automatically selected from, for example, a pre-defined table of the relationship between each cost and the phase compensation parameter (phase lag estimated value φ) according to the proportion of each cost in the operating cost. This allows the phase compensation parameter to be automatically adjusted even if the operating state of the plant, unit cost, etc., change.

[0090] The phase lag estimator 1000 may take a cost with a large proportion in the breakdown of a plurality of costs in the operating cost as the main component, and set the phase lag estimate value φ based on the phase shift of the cost with a large proportion.

[0091] For example, when the breakdown of operating costs is 15% chemical costs, 35% sludge disposal costs, and 50% filtration basin cleaning costs, and the phase compensation parameter for chemical costs is φ1, the phase compensation parameter for sludge disposal costs is φ2, and the phase compensation parameter for filtration basin cleaning costs is φ3, the phase lag estimation unit 1000 may set the phase lag estimated value φ to φ3, which is the phase compensation parameter for filtration basin cleaning costs, using the filtration basin cleaning cost, which has the largest proportion, as the main component.

[0092] The phase lag estimator 1000 may calculate a phase lag estimated value φ by combining phase compensation parameters for each cost according to the ratio of the breakdown of the operating cost into a plurality of costs.

[0093] For example, if the breakdown of operating costs is 15% chemical costs, 35% sludge disposal costs, and 50% filtration basin cleaning costs, and the phase compensation parameter for chemical costs is φ1, the phase compensation parameter for sludge disposal costs is φ2, and the phase compensation parameter for filtration basin cleaning costs is φ3, the phase lag estimation unit 1000 may set the phase lag estimated value φ to a value calculated by φ=φ1*0.15+φ2*0.35+φ3*0.5 according to the proportion of each cost.

[0094] By setting the phase lag estimate φ based on the principal component determined by the cost ratio, the phase lag estimate φ can be adjusted without taking into account other cost fluctuations, i.e., phase shifts of components that have little effect on fluctuations in the evaluation value.

[0095] Furthermore, the phase lag estimator 1000 may focus on a cost that has a large fluctuation in the breakdown of the operating cost as a major component, and set the phase lag estimate value φ based on the phase shift of the cost that has a large fluctuation.

[0096] For example, the phase lag estimation unit 1000 can calculate a plurality of operating costs, record each calculated value in association with time information, and calculate the amount of change in each cost over a predetermined period, the rate of change, the ratio of the amounts of change among the plurality of costs, etc. The phase lag estimation unit 1000 may use a cost with a large amount of change (or rate of change) as the main component, or may use the cost with the largest amount of change based on the ratio of the amounts of change among the plurality of costs as the main component.

[0097] For example, when the ratios of the change in the operating costs for multiple costs are 10% for the change in the chemical cost, 15% for the change in the sludge disposal cost, and 75% for the filter basin cleaning cost, and the phase compensation parameter for the chemical cost is φ1, the phase compensation parameter for the sludge disposal cost is φ2, and the phase compensation parameter for the filter basin cleaning cost is φ3, the phase lag estimation unit 1000 may set the phase lag estimated value φ to φ3, which is the phase compensation parameter for the filter basin cleaning cost, using the filter basin cleaning cost, which has the largest change ratio, as the main component.

[0098] The phase lag estimator 1000 may calculate a phase lag estimated value φ by combining phase compensation parameters for each cost in accordance with the ratio of the amount of change in a plurality of operating costs.

[0099] For example, when the breakdown of operating costs is such that the ratio of change in chemical costs is 10%, the ratio of change in sludge disposal costs is 15%, and the filtration basin cleaning costs is 75%, and the phase compensation parameter for chemical costs is φ1, the phase compensation parameter for sludge disposal costs is φ2, and the phase compensation parameter for filtration basin cleaning costs is φ3, the phase lag estimation unit 1000 may set the phase lag estimated value φ to a value calculated by φ=φ1*0.1+φ2*0.15+φ3*0.75 according to the ratio of change in each cost.

[0100] By setting the phase delay estimate φ based on the main component due to cost fluctuations, if there is a fixed-value cost that accounts for a high proportion of the cost but hardly changes, it is possible to adjust the phase delay estimate φ based on the phase shift of the cost, which fluctuates greatly and is the main component of the fluctuations in the evaluation value, without taking into account the phase shift of the fixed value.

[0101] The estimated phase lag value φ is not limited to the above value. For example, the phase lag estimator 1000 may use the cost with the largest ratio among the multiple operating costs as the first principal component, use the cost with the largest fluctuation as the second principal component, and combine the phase compensation parameters of the first principal component and the second principal component to calculate the estimated phase lag value φ. For example, the phase lag estimation unit 1000 may select a plurality of costs from among a plurality of operating costs in descending order of ratio, further select at least one cost with a large fluctuation from among the selected plurality of costs, and calculate the phase lag estimation value φ using a phase compensation parameter corresponding to the at least one selected cost.

[0102] Figure 11 shows an example of the changes in SV (the target pH value of the treated water), pH adjuster, turbidity at the outlet of the settling basin, and evaluation value during coagulation using extreme value control when the coagulant injection rate is reduced. During the operation of a water purification plant, the injection rate of PAC (coagulant) may be reduced at the discretion of the operator. In such cases, the turbidity of the treated water at the outlet of the settling basin may increase.

[0103] When extreme value control is applied in this function, if the turbidity at the outlet of the settling basin exceeds the management target value, the evaluation value rises significantly, and the SV is automatically adjusted to lower the evaluation value. Here, since the turbidity at the outlet of the settling basin decreases when more sulfuric acid is injected and the pH of the treated water during coagulation is shifted to the acidic side, the SV is lowered to the acidic side. This causes the turbidity at the outlet of the settling basin to converge again to near the water quality constraint. The pH of the SV that converges at this time is the pH of the treated water during coagulation that satisfies the water quality constraints at the changed PAC injection rate while minimizing operating costs.

[0104] Figure 12 shows an example of the changes in SV (the pH target value during coagulation using extreme value control), changes in pH adjuster, changes in turbidity at the outlet of the settling basin, and changes in the evaluation value when the coagulant injection rate is increased. During the operation of a water purification plant, the injection rate of PAC (coagulant) may be increased at the discretion of the operator. In such cases, the turbidity of the treated water at the outlet of the settling basin may decrease further.

[0105] When extreme value control is applied in this function, if the turbidity at the outlet of the settling basin falls significantly below the management target value, it is determined that there is room to adjust the SV in a direction that further reduces costs, and the SV operates in the direction that reduces costs, i.e., suppresses the sulfuric acid injection rate. Specifically, the SV shifts toward alkaline. This reduces the sulfuric acid injection rate and reduces operating costs, but at a certain injection rate, the turbidity of the settling basin rises again, and water quality constraints are imposed. The pH at this convergence point is the pH of the treated water during coagulation that achieves operation with minimal operating costs while satisfying water quality constraints at the changed PAC injection rate.

[0106] If the PAC injection rate is reduced, extreme value control will shift the target pH of the treated water during coagulation to the acidic side, and if the PAC injection rate is increased, extreme value control will shift the target pH of the treated water during coagulation to the alkaline side.In this way, a feature of this function is that it can automatically find the appropriate target pH of the treated water during coagulation, even when the coagulant injection rate is changed during water purification plant operation.

[0107] There are several methods for setting and controlling the PAC injection rate at water purification plants. In addition to the method described above where the operator manually sets it, there is also a method of controlling the PAC injection rate in proportion to the turbidity of the raw water, known as feedforward control. Even in this case (when feedforward control is used), if the PAC injection rate is changed due to an increase in turbidity, the feature of this function is that it determines whether there is room for the evaluation value to decrease depending on the difference between the turbidity of the treated water at the outlet of the sedimentation tank and the management target value, and automatically adjusts the SV.

[0108] Here, extreme value control is a method of finding the optimum value by manipulating the SV relatively slowly, so if the raw water turbidity changes suddenly and the PAC injection rate changes suddenly in response, the control speed cannot respond.This can be improved by setting limits on changes in raw water turbidity in advance and switching between modes of application and non-application of extreme value control.

[0109] In recent years, a method has been proposed in which the charge state of flocs after coagulant injection is measured in real time and the PAC injection rate is feedback-controlled according to the target charge state. Even when this method of controlling the coagulant injection rate (feedback control according to the target charge state) is used, the pH adjustment function of this system can be applied. Experimental studies to date have shown that feedback control of the PAC injection rate according to the target charge state maintains a constant pH in the treated water during coagulation. While the pH remains constant, it is unknown whether it is optimal. Therefore, by applying this function, a combined method can be adopted in which the coagulant injection rate is feedback-controlled according to the target charge state of flocs while the pH of the treated water during coagulation is sought using extreme value control.

[0110] As described above, according to the present embodiment, it is possible to provide a pH adjuster injection control device, a pH adjuster injection control method, and a computer program that minimize the operating costs of a water treatment plant while maintaining appropriate water quality.

[0111] In other words, according to this embodiment, it is possible to provide a pH adjuster injection control device, a pH adjuster injection control method, and a computer program that can more appropriately control the amount of coagulant injected even when there are fluctuations in the quality and volume of raw water to be treated, the water quality control values ​​at a specified location in the plant, and raw unit costs related to costs such as the unit price of the chemical.

[0112] In particular, according to this embodiment, in a system that feeds back the amount of pH adjuster to be injected based on the floc charge state, it is possible to automatically calculate the control target value (SV) of the floc charge state in real time, and it is possible to provide a pH adjuster injection control device, a pH adjuster injection control method, and a computer program that realize operations that minimize operating costs while maintaining the water quality control value.

[0113] The program according to this embodiment may be transferred in a state where it is stored in an electronic device, or in a state where it is not stored in an electronic device. In the latter case, the program may be transferred via a network, or in a state where it is stored in a storage medium. The storage medium is a non-transitory tangible medium. The storage medium is a computer-readable medium. The storage medium may be in any form, such as a CD-ROM or a memory card, as long as it is capable of storing the program and is computer-readable.

[0114] Although several 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 novel embodiments can be embodied in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included within the scope and spirit of the invention, and are also included in the scope of the invention and its equivalents as defined in the claims. [Explanation of symbols]

[0115] 1...pH adjuster injection control device, 3...receiving well, 4...rapid mixing basin, 5...flocculation basin, 6...sedimentation basin, 7...filtration basin, 8...coagulant injection device, 9...pH measurement sensor during coagulation, 10...pH adjuster injection device, 11...control target value determination unit, 12...pH adjuster injection control unit, 31...raw water quality meter, 32...flow meter, 41...rapid mixer, 42...mixed water quality meter, 51-53...mixing basin, 54-56...slow mixer, 61...sedimentation basin water Quality meter, 62...sludge discharge basin, 63...sludge extraction pump, 71...filter basin water quality meter, 72...filter basin water level meter, 100...water treatment plant, 110...process measurement value acquisition unit, 120...process evaluation value calculation unit, 130...demodulation dither signal generation unit, 140...evaluation function gradient estimator, 160...gradient estimator normalization unit, 170...extremum search unit, 180...modulation dither signal generation unit, 190...output unit, 1000...phase lag estimator

Claims

1. a process measurement value acquisition unit that acquires a plurality of measurement values ​​of the water to be treated from a control object including a pH adjuster injection process; a process evaluation value calculation unit that calculates, using the measurement values, an evaluation value of an evaluation function that changes depending on a first manipulated variable of the controlled object and indicates an index related to optimization of the controlled object; a phase lag estimator that calculates an estimated value of a phase lag from the first manipulated variable in the controlled object to the evaluation value, using values ​​of each of a plurality of components included in the evaluation value and a phase compensation parameter corresponding to each of the plurality of components; an evaluation function gradient estimator that calculates gradient information of the evaluation function, which is a rate of change of the evaluation value with respect to the first manipulated variable, using information on the phase lag estimated value and the evaluation value; an extremum seeker that determines an optimal manipulated variable for the first manipulated variable by integrating the gradient information; a control unit that acquires a control amount from the controlled object, calculates and outputs a second operation amount so that the control amount follows the target value of the control amount, with the optimal operation amount being a target value of the control amount of the controlled object.

2. 2. The pH adjuster injection control device according to claim 1, wherein the phase delay estimation unit determines the component having the largest proportion among the plurality of components included in the evaluation value as the main component, and sets the phase delay estimation value based on the phase compensation parameter corresponding to the main component.

3. The pH adjuster injection control device according to claim 1 , wherein the phase lag estimation unit calculates the phase lag estimation value by combining the phase compensation parameters of each of the plurality of components according to the ratio of the plurality of components included in the evaluation value.

4. The pH adjuster injection control device according to claim 1, wherein the phase delay estimation unit sets the component with the largest fluctuation among the plurality of components included in the evaluation value as the main component, and sets the phase delay estimation value based on the phase delay value corresponding to the main component.

5. The pH adjuster injection control device according to claim 1 , wherein the phase lag estimation unit calculates the phase lag estimation value by combining the phase compensation parameters of each of the plurality of components in accordance with fluctuations of the plurality of components included in the evaluation value.

6. The controlled object is a water treatment plant including a pH adjuster injection process, the first manipulated variable is a target value of pH of the water to be treated at the time of coagulation, the target value being contained in the water to be treated into which the pH adjuster has been injected by the pH adjuster injection process in the water treatment plant; the evaluation value is a total cost including, as a plurality of components, a plurality of costs each having a different time from a change in the first manipulated variable to a response in the water treatment plant, and 2. The pH adjuster injection control device according to claim 1, wherein the control unit calculates and outputs the second manipulated variable, which is the pH adjuster injection rate, so that the pH of the water to be treated during the coagulation follows the target value of the controlled variable.

7. A plurality of measurement values ​​are acquired from a control target including a pH adjuster injection process; calculating an evaluation value of an evaluation function that changes depending on a first manipulated variable of the controlled object and indicates an index related to optimization of the controlled object, using the measured value; calculating an estimated value of a phase lag from the first manipulated variable in the controlled object to the evaluation value using values ​​of each of a plurality of components included in the evaluation value and a phase compensation parameter corresponding to each of the plurality of components; calculating gradient information of the evaluation function, which is a rate of change of the evaluation value with respect to the first manipulated variable, using information on the phase lag estimated value and the evaluation value; determining an optimal manipulated variable for the first manipulated variable by integrating the gradient information; A control unit and a pH adjuster injection control method that obtain a control amount from the controlled object, calculates and outputs a second operation amount so that the control amount follows the target value of the control amount, with the optimal operation amount being a target value of the control amount of the controlled object.

8. A computer program that causes a computer to execute the pH adjuster injection control method according to claim 7.

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