JPH06304546A - Operation controller for waterwork plant - Google Patents

Abstract

PURPOSE:To stably maintain the supply of safe city water by measuring the concn. of the water pollutants in a water source, such as lake, pond or dam lake, deciding the concn. of the odorous materials therein and controlling the operation of a water purification plant in accordance with the time when the fountainhead water arrives at an intake source. CONSTITUTION:This operation controller includes a wide water sphere monitor means 100 provided with a plankton monitor control means 110, a water quality evaluating means 120 and a basin water quality predicting means 130. The controller monitors the generation state of vegetative planktons by these means and monitors the operation of purification equipment 12 by diagnosing the water pollution of the fountainhead 1 from the monitor information obtd. in such a manner and automatic water quality monitor information. The controllers include a purification plant monitor control means 200 provided with a water quality safety monitor means 210 and a flocculating agent injection control means 220 and proportionally controls the amt. of the ozone to be generated by deciding Whether the quality of the raw water taken in the intake equipment 3 from rivers 2 is abnormal or not. Further, the controller includes a hardness treating plant monitor control means 300 provided with an equipment operation management means 310, an ozone contact pond control means 320 and a bioactive carbon column control means 330.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a water purification plant operation management apparatus, and particularly to an operation management system suitable for stably maintaining treated water in a water purification plant having an ozone treatment process or an activated carbon treatment process for growing organisms. Regarding the control device.

[0002]

2. Description of the Related Art Odorous taste disorders in tap water are caused by odorous substances such as diosmin and dimethylisoborneol (2-MIB) generated by phytoplankton that have abnormally grown due to eutrophication of lakes and dams that are water sources. It is supposed to be the cause.
In addition, the existence of trace organic chemicals such as the precursor of trihalomethane (THM), which is considered to be a carcinogen, has been clarified.
There is concern that nutrient salts such as nitrogen compounds and phosphorus compounds may be contained in the raw water of the tap water. It is difficult to remove these substances only by the coagulant injection slow filtration method or the rapid filtration method of the water purification plant, and new measures are required.

In order to treat these substances and supply safe and delicious water, advanced treatment plants having ozone contact ponds and biological activated carbon towers as basic constituent facilities have been installed in water purification plants.

In the ozone contact pond, the treated water amount Q, the residual ozone concentration Co, the ozonized gas amount G and the ozone concentration g
_i, the ozone concentration g _o in the gas discharged from the contact pond measured, compared with the ozone injection rate measured value R obtained from the preset ozone injection rate target value (1), the variation of the amount of water treated Correspondingly, the amount of raw material gas of the ozone generator and the applied voltage are adjusted to proportionally control the amount of ozone generated. If the relationship between the absorption efficiency η and the treatment effect and the residual ozone expressed by the equation (2) is known, it is also possible to control the ozone injection targeting the residual ozone concentration (purified water maintenance management guideline). R = G · g _i / Q (1) η = 100 · (g _i −g _o ) / g _i (2) Oxygenated gas ozone concentration and exhaust gas ozone concentration A method of adjusting the amount of production (Japanese Patent Laid-Open No. 58-20292), a method of making the ozone concentration of ozone gas constant and adjusting the amount of ozone gas corresponding to the ozone concentration of exhaust gas (Japanese Patent Publication No. 56-898).
No. 95). Furthermore, external factors (events) that serve as criteria for skilled operators and the ozone injection rate correction value corresponding to the degree are fuzzy set and stored in advance in the computer.
The operator inputs the degree of this factor to obtain a correction value for the injection rate, provides guidance on the actual injection rate that is the sum of this correction value and the basic injection rate, and refers to the guidance injection rate to manually implement the actual injection rate. There is a method (Japanese Patent Laid-Open No. 60-161797) for setting the injection rate of.

In the biological activated carbon tower, there is a system (Japanese Patent Laid-Open No. 2-233192) in which the suspended substance deposited on the surface layer of the activated carbon is sprayed from below to backwash the suspended material.

[0006]

The prior art is a method of individually controlling each process of a plant. However, it goes without saying that it is preferable to perform the operation by taking into consideration the change in water quality and by incorporating the information from each process.

An object of the present invention is to provide an operation management control device for a water purification plant, which appropriately manages a water purification plant and an advanced treatment plant in consideration of water quality and operation cost, and stably supplies safe and delicious water. Especially.

[0008]

The present invention comprises wide area hydrosphere monitoring means, water purification plant monitoring control means, advanced treatment plant monitoring control means, and water distribution equipment monitoring control means.

The wide area hydrosphere monitoring means comprises plankton monitoring means, water quality evaluation means, and basin water quality prediction means, monitors the phytoplankton generation state, and diagnoses water pollution at the water source from the monitoring information and automatic water quality monitoring information. The operation and management of the purification facility is performed, and the water quality condition of the intake site is simulated and predicted from the water quality information of the water source, the geographical information of the river system such as the river, and the water quality information of the drainage facility and the tributary river.

The water purification plant monitoring control means comprises a water quality safety monitoring means and a coagulant injection control means, determines whether or not the quality of the raw water taken is abnormal, and monitors the floc formation state after coagulant injection. Then, the proper injection amount of the coagulant is manipulated.

The advanced treatment plant monitoring control means comprises equipment operation management means, ozone contact pond control means, and biological activated carbon tower control means, and controls the amount of water for advanced treatment, the number of operating facilities, and ozone generation control and injection based on effective ozone consumption. Ozone injection control that makes effective use of ozone, Exhaust ozone treatment tower control that diagnoses the state of the filler in the exhaust ozone treatment tower and replaces it,
In addition, biological activated carbon tower backwashing control is performed to monitor the state of organisms and activated carbon and perform backwashing operation.

The water distribution facility monitoring and control means is composed of a water demand forecasting means and a wide area water operation monitoring means, and based on the demand forecast, water intake and water distribution to distribution reservoirs, diagnosis and control of residual chlorine, and deterioration of old pipes. Perform diagnostics and renewal plans.

[0013]

In the wide area hydrosphere monitoring means, the plankton monitoring means uses image processing to monitor the type and amount of phytoplankton generated, and the water quality evaluation means monitors the plankton monitoring means and DO, pH, water temperature, turbidity. ， Electrical conductivity ，
The water pollution of the water source is diagnosed from the water quality monitoring information such as organic matter, nitrogen and phosphorus concentration, and the purification equipment is operated and managed according to the water pollution state to purify the water of the water source. The watershed water quality prediction means predicts the water quality of the water intake site from the water source water quality information obtained by the water quality evaluation device, geographical information and meteorological information of the water system related to the water intake site of the water treatment plant, and water quality information and water quantity information. Furthermore, the distribution of water intake at multiple intakes will be planned. In this way, the wide area hydrosphere monitoring means can grasp the water quality information of the raw water taken by the water purification plant and the amount of water held in the water system, and the water purification plant can use these information to take prompt action.

In the water purification plant monitoring control means, the water quality safety monitoring means guides the taken raw water to the fish breeding aquarium, monitors the behavior of this fish by image processing, and diagnoses the presence or absence of water quality abnormality. If it is determined that the water quality is abnormal, an alarm will be issued and guidance will be provided on measures such as thorough water quality analysis and suspension of water intake.

The coagulant injection control means monitors the floc formation state after coagulant injection by an underwater camera and image processing, analyzes the present value of this image information and water quality measurement information, and past history data by a neural network, The amount of coagulant injected that becomes a turbidity substance below the standard value is presented.

As described above, the water purification plant monitoring and control means quickly takes measures such as stopping the intake of water when a substance such as a toxic substance that adversely affects the organism is mixed, so that the growth of the biological activated carbon tower arranged in the subsequent stage can be prevented. Organisms can be treated without sacrificing them even if they protect organisms and resume water intake.

Further, by suppressing the turbidity substances flowing out from the water purification plant, clogging of the biological activated carbon tower and deterioration of the activated carbon in the advanced treatment plant can be reduced, the backwash frequency can be reduced, and the life of the activated carbon can be reduced. Can be extended.

In the advanced processing plant monitoring control means,
The equipment operation management means uses the plant measurement information, the water quality information of the water system and the intake area, and distributes the flow rate only for the advanced treatment and the conventional water purification treatment by coagulation sedimentation, and manages the advanced treatment equipment based on the distribution result. If the water quality item to be removed in the advanced treatment is below the standard value, the advanced treatment plant is not operated and only the water purification plant is treated. If it is equal to or higher than the reference value, the flow distribution between the advanced treatment plant and the water purification plant is calculated and determined on the condition that the water quality in the mixing pond at the latter stage of the advanced treatment plant satisfies the reference value. The number of lines to be operated (when there are multiple lines of plant equipment), the number of operating ozone generators, and operating conditions are set based on the advanced treatment flow rate determined here. The facility operation management can also be determined by obtaining the ozone effective consumption amount described later.

The ozone contact pond control means uses the water quality information obtained by the water quality evaluation means and the water quality prediction means and the effective ozone consumption amount used for the oxidative decomposition of the target substance based on the ozone substance balance to obtain an appropriate ozone injection amount. Then, the applied voltage of the ozone generator, the gas flow rate, and the operating number are controlled. The proper ozone injection amount can also be obtained by learning the past operation history data with a neural network using the raw water quality, the treated water quality, and the measured value of the ozone contact pond as input factors. Furthermore, the bubble diameter of the ozonized gas injected into the ozone contact pond is monitored by image processing, and the pressure and gas flow rate of the ozone generator and ozone contact pond are adjusted so that the dissolution of ozone in the liquid is optimal.

The biological activated carbon tower control means performs backwashing using the pressure loss before and after the activated carbon layer or the reduction rate of the water flow rate of the entire tower due to the change in pressure loss as an index. Backwashing uses treated water and air in combination, and carries out a two-step operation. In the first stage, both treated water and air are blown in to enhance the cleaning ability. As a result, not only the surface of the activated carbon but also a part of the adsorbed substance in the pores is desorbed. The second step is to wash with treated water only, to completely remove the desorbed substances and to remove the substances that are about to desorb on the activated carbon surface. When air is not used, the backwashing operation of the above two steps is performed by changing the backwashing flow rate and the blowing pressure of the treated water. Furthermore, the breakthrough state of the activated carbon and the activated state of the microorganism are monitored from the presence or absence of the microorganism that has been peeled from the activated carbon as an index of the backwash operation, and the amount of water removed at the inlet and outlet of the pond, and the results are used.

The monitoring of microorganisms is carried out by applying image processing, but it is possible to monitor not only the microorganisms but also the crushed activated carbon and the suspended matter that has been filtered by the activated carbon layer of the adsorption tank. The life judgment information of

As described above, since the advanced treatment plant monitoring control means manages the operation of the plant according to the quality of raw water, it is possible to reduce the operating cost without wastefully operating the equipment.

Further, since the amount of ozone required for the oxidative decomposition of the target substance is injected, tasty water of good quality can be produced at the minimum necessary cost. Further, since the suspended matter flowing out from the biological activated carbon tower is monitored and the backwashing operation is carried out, safe and reliable water free from microorganisms and turbidity can be provided.

In the water distribution facility monitoring and control means, the water demand forecasting means uses a neural network from the input factors such as precipitation, sunshine hours, temperature, day of the week, and current and past several hours of water distribution and past operation history data. Predict future water demand of consumers (consumers) and reflect it in the amount of water intake.
Wide area water operation monitoring means plans water distribution under the constraints of water intake, demand, and pond capacity, and manages water appropriately.
We will control leakage and red water generation by diagnosing and updating old pipes based on the pipeline database, and analyze whether the residual chlorine to the customer is appropriate from the pipeline network and distribution flow rate.
Guidance is given on the chlorine injection amount so that the minimum residual chlorine concentration satisfies the standard value. In this way, the water distribution facility monitoring and control means supplies the delicious water produced at the water purification plant to consumers without excess or deficiency and without change.

According to the present invention, it is possible to consistently control the water quality from the intake to the water supply, and it is possible to always supply the safe, reliable and delicious water to the customer in a stable manner.

[0026]

DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described below based on the embodiment shown in FIG.

In FIG. 1, 1 is a water source such as a lake or dam, 2 is a river having the water source 1 as a water source, 2A and 2B are tributaries that join the river 2, and 3 is a water intake facility having the river 2 as a water source. 3A is a controller for the water intake facility 3, 4 is a first water purification plant, 5 is an advanced treatment plant, 7 is a second water purification plant, 8
Is a water distribution facility group, 9 is tap water utilization facility, 100 is wide-area hydrosphere monitoring means, 200 is water purification plant monitoring control means, 300
Is an advanced processing plant monitoring control means, and 400 is a water distribution facility monitoring control means.

In the water source 1, 11 is an underwater suspended substance imaging device, 12 is purification equipment, and 13 is water sensor equipment. In the river 2, 21 and 22 are water sensor facilities. In the first water purification plant 4, 41 is a pretreatment facility, 42 is a rapid stirring basin, 43 is a floc formation basin, 44 is a sedimentation basin, 45 is a biological breeding facility, 46 is a flocculant injection facility,
Reference numeral 47 is a water sensor facility, and 48 is an underwater imaging device. In the advanced treatment plant 5, 51 is a water distribution device, 52 is an ozone contact tank, 53 is a biological activated carbon tower, 54 is a treated water storage tank, 55 is an ozone generation facility, and 55A is an ozone generation facility 5.
5, control device 56, waste ozone treatment facility, 57 backwash facility of biological activated carbon tower 53, 57A control device of backwash facility 57, 58 storage water tank, 59 control device of water distribution device 51, 60 Bypass lines, 61A and 61B are imaging devices, 62A, 62B and 62C are water sensor equipment, 63
A, 63B and 63C are gas sensor equipment, and 64 is a pressure adjusting device.

In the second water purification plant 7, 71 is a mixing adjustment basin, 72 is a sand filtration basin, 73 is a backwash facility for the sand filtration basin 72, 74 is a chlorine mixing basin, 75 is a chlorine injection facility, and 73A is a backwash facility. The controller 73, 76A and 76B, is a water sensor facility. In the water distribution facility group 8, 81 is a reservoir,
82A, 82B, 82C and 82D are water distribution pipe networks, 83
A, 83B, 83C and 83D are water sensor equipment.

In the wide area hydrosphere monitoring means 100, 110
Is a plankton monitoring means, 120 is a water quality evaluation means, 13
0 is a watershed water quality prediction means.

In the water purification plant monitoring control means 200, 210 is a water quality safety monitoring means, 220 is a coagulant injection control means, 212 of the water quality safety monitoring means 210 is an imaging device,
Reference numeral 214 is an image analysis device, 216 is a computer, 222 of the coagulant injection control means 220 is an image analysis device, and 224 is a computer.

In the advanced processing plant monitoring control means 300, 310 is equipment operation management means, 320 is ozone contact pond control means, 330 is biological activated carbon tower control means, and 322 of the ozone contact pond control means 320 is an image analysis device, 324.
Is a computer, 332 of the biological activated carbon tower control means 330 is an image analysis device, and 334 is a computer.

In the water distribution facility monitoring control means 400, 4
Reference numeral 10 is a sand filter control means, 420 is a wide area water operation management means, and 430 is a water demand prediction means.

The raw water taken from the river 2 by the water intake facility 3 is the first water purification plant 4, the advanced treatment plant 5, and the second
After a predetermined target substance is treated in the water purification plant 7, the tap water utilization facility 9 such as a home, a building or a factory is supplied with water through the water distribution facility group 8. Here, the processing contents of each plant will be briefly described below.

In the first water purification plant 4, after precipitating equipment 41 to remove coarse suspended matter in the raw water by sedimentation, the rapid stirring basin 4
In step 2, the coagulant injected from the coagulant injection facility 46 and the raw water are agitated, the finer suspended substance is flocculated and flocculated in the floc formation pond 43, and the flocculated substance is precipitated in the settling pond 44 to suspend the suspended substance. To remove. In the advanced treatment plant 5, the ozonized gas from the ozone generating facility 55 is blown into the ozone contact basin 52 to oxidize and remove odorous substances such as diosmin and dimethylisoboneol in the inflowing water, while being hard to be decomposed by microorganisms. A part of the hardly decomposable organic matter is changed to biodegradable organic matter. Ozone contact pond 5
The exhaust gas of No. 2 is guided to an exhaust ozone treatment facility 56 filled with activated carbon or a manganese catalyst, and residual ozone in the gas is adsorbed or decomposed and removed. The treated water flowing out from the ozone contact pond 52 is introduced into the biological activated carbon tower 53, and the activated carbon and the microorganisms adhering to and grown on the activated carbon treat ammonia nitrogen and easily decomposable organic substances that cannot be removed by the ozone treatment.

The treated water of the biological activated carbon tower 53 is sent to the second water purification plant 7 via the treated water storage tank 54. In the second water purification plant 7, after being filtered in the sand filtration basin 72, it is sterilized and disinfected by the chlorine injected from the chlorine injection equipment 75 in the chlorine mixing basin 74 and then sent to the distribution basin 81.

The detailed contents of the embodiment will be described below. Figure 2
The details of the plankton monitoring means 110 and the underwater suspended matter imaging device 11 in the wide area hydrosphere monitoring means 100 are shown in FIG.

The plankton monitoring means 110 is composed of an image processing device 115 and a monitor television 116, and processes the image signal of the underwater suspended matter imaging device 11 to measure the type and amount of plankton generated in the ground 1. It is a thing. The underwater suspended substance imaging device 11 is composed of an imaging unit 111, a local operation panel 112, and a central operation panel 113. The image pickup unit 111 is immersed in the stored water of the water source 1, and images suspended substances such as plankton and dust. Imaging unit 111
For example, the image pickup device disclosed in Japanese Patent Laid-Open No. 2-182630 can be used. The video signal (grayscale image) picked up by the image pickup unit 111 is input to the image processing apparatus 115 via the local operation panel 112 and the central operation panel 113.
The on-site operation panel 112 is installed in the vicinity of the image pickup unit 111, and can operate the power source of the image pickup unit 111 and the built-in lighting, cleaning, and sampling mechanism on the spot. The central control panel 113 is installed in the vicinity of the plankton monitoring means 110, and the local control panel 1
It has a function of receiving a video signal from 12 and transmitting it to the image processing apparatus 115, and a function of remotely performing the same operation as the on-site operation panel 112. Further, the operation of the cleaning and sampling mechanism can be executed by a control signal from the image processing device 115.

FIG. 3 shows the configuration of the image processing apparatus 115.
The video signal from the imaging unit 111 is A / D converted and transmitted to the image processing processor 151 of the image processing unit 150. The image processor 151 processes the received image and calculates a feature amount such as an area or a shape coefficient for each recognized object. Here, the object is a plankton or garbage that lives in the stored water of the water source 1. In the image processing, various plankton measurements are executed by combining image processing methods such as binarization processing, noise removal processing, and labeling processing. The processing method described in Japanese Patent Application No. 4-64171 filed by the present inventors can be used for this method.

The image processed by the image processor 151 is input and stored in the image memory 152 together with the enlarged grayscale image before being processed. The measured number of objects and the characteristic amount such as each area and shape coefficient are transmitted to the central processing unit 154 via the system bus 153.
The central processing unit 154 has a function of identifying plankton and dust based on the input measurement information, and identifying and diagnosing the type of plankton based on the plankton area and its number distribution and shape. This diagnostic function is performed by the auxiliary memory 156.
The type of the object is identified based on the inference rule stored in advance, and the generation amount is counted for each type. The inference rule is, for example, a production rule such as "if the area is a constant value a ₁ or less and the area ratio of holes is a constant value a ₂ or more, the object is plankton A." If there are many, the perimeter is long, and the number of end points is large,
The object is Plankton B. , Or a frame rule in which image feature quantities such as area, total area / number ratio, circumference length, number of holes, number of end points and ratio of length, and their judgment reference values are set.

The information diagnosed by the central processing unit 154, the measurement information used for the diagnosis, the diagnostic program, etc. are stored in the main memory 155, the auxiliary memory 156, or the disk 1.
57. Memory 155, 156 and disk 1
The information stored in 57 is output to the water quality evaluation unit 120 and the facility operation management unit 310 via the communication interface 158. Further, the stored information and the image information in the image memory 152 can be displayed on the monitor television 116. In addition, here, the plankton diagnosis method using the knowledge engineering method is applied, but various image feature quantities are input to the input layer,
It is also possible to use a neural network method in which a feature value serving as a criterion for each type of plankton is used as teacher data. Further, in the above description, the plankton monitoring means 110 to which the image processing technology is applied is given as an example.
A plankton measuring method using a chlorophyll meter or an optical fiber may be applied.

The water quality evaluation means 120 evaluates the water pollution status of the water source 1 on the basis of the input information from the plankton monitoring means 110 and the measurement information of the water sensor-equipment 13, and purifies in response to the water pollution status. The facility 12 is operated to purify the stored water in the water source 1. Water sensor equipment 1
3, DO, pH, water temperature, turbidity, organic matter concentration, chlorophyll amount, total nitrogen and ammonia nitrogen concentration, total phosphorus and phosphoric acid concentration, odor concentration, chromaticity, electrical conductivity, flow velocity,
Targets are odor level and water level.

An example of the structure of the water quality evaluation means 120 is shown in FIG. 121 is a central processing unit, 122 is a main memory, 123
Is an auxiliary memory, 124 is a disk, 125 is a printing device,
126 is an inference device, 127 is a communication interface, 128
Is the system bus.

Information on the inside can be displayed on the display device 129A, and the input device 129B transmits necessary information to the water quality evaluation means 120 from a mouse, a light pen, a keyboard or the like.
The central processing unit 121 executes the main program stored in the main memory 122 and controls the entire water quality evaluation unit 120. The execution timing depends on a start command signal from the input device 129B, a timer, and information input from the communication interface 127. The auxiliary memory 123 receives the plankton monitoring means 1 received via the communication interface 127.
Plankton information from 10 and water sensor equipment 13
The measurement information of is stored. The inference unit 126 uses the plankton and measurement information of the auxiliary memory 123 and the main memory 122.
Alternatively, the water quality diagnosis rule and the purification measure rule stored in the auxiliary memory 123 are called to diagnose the comprehensive water pollution state of the specific region or the entire water area and its factor, and the purification measure means and its operation control method, and An alarm command to the water facility related to the water area, operation guidance, etc. are deduced and displayed on the display device 129A.

For example, the state of water pollution and its factor diagnosis are
"The number of plankton Y exceeds a certain value N, and the water temperature is T
When the DO is a constant value M or higher at a temperature of ℃ or higher, the water pollutant is an odorous substance S "is entered in advance as a water quality diagnosis rule. Next, the concentration of this pollutant G ₀ (mg
/ L) is calculated and predicted from the relational expression stored in the main memory 122 or the auxiliary memory 123 in advance. Equation (1) is the relationship of plankton Y where plankton Y produces diosmin that causes musty odor.

G ₀ = αB (3) where α: Diosmin production coefficient of plankton Y B: Concentration of plankton Y in water source (cells / l) In addition to this, for example, odorant dimethyliso which is also a cause of musty odor The relational expression between borneol (2-MIB) and cyanobacteria such as formium and oscillatoria, or the concentration of trihalomethane precursors and organic substances such as humic substances based on water sensor measurement information and plankton image information, or nitrogen and phosphorus. Enter the relationship of the eutrophication substance concentration such as. The reasoning device 126 is the water sensor equipment 1
It has a function of diagnosing and selecting a relational expression to be used from the measured value of 3 and the type of plankton. These diagnostic contents and results are stored in the main memory 122 or the auxiliary memory 123, and guidance is displayed on the display device 129A. Further, the content of the diagnosis is not limited to the odorous substance, and it is also possible to provide guidance so as to take measures before a water purification obstacle such as filtration blockage occurs. In other words, whirling millet and groundwort are typical plankton that cause filtration blockage of the water purification plant, but guidance is displayed when the concentration of these plankton exceeds a certain value. Furthermore, the inference device 126 determines the operation method such as the number of operating purification devices and the time of the purification device 12 according to the type and concentration of the water pollutant and the type and concentration of the plankton, and the result is displayed on the display device. It is possible to display guidance on 129A or directly support the operation of the purification device 12.

A plurality of purification devices 12 are generally installed in one water source 1, and various types of purification methods are applied. For example, there is a purification method in which a polluted liquid containing suspended substances is directly collected, only suspended substances are separated and removed by filtration, and then the filtrate is returned to a water body as a cleaning liquid. The polluted liquid may be collected by a pump from a water pipe fixed to a ship or a region where suspended solids accumulate. Furthermore, as a purification method, a method of directly operating an apparatus installed in the water area can be applied. This method involves stirring the liquid near the surface of the water to generate a water flow and supplying oxygen, and a hollow cylinder near the bottom of the water area to continuously or intermittently circulate air to force the water flow. There is a method to generate.
These purification methods are effective for the direct purification of abnormal phytoplankton, and when the pollution factors are affected by water temperature, water flow stagnation, and solar radiation. In addition, when the pollutant is organic substances or eutrophication salts such as nitrogen and phosphorus, the method of selectively decomposing and removing eutrophication salts using microorganisms such as activated sludge method, precipitation by adding agents such as flocculants A method of removing, a method of physically removing with activated carbon or a film, and a method of decomposing and removing by ozone injection or ultraviolet irradiation can be applied.

The water source 1 water quality diagnosis information and the purification countermeasure information of the water quality evaluation means 120 are output to the basin water quality prediction means 130 and the equipment operation management means 310 of the advanced treatment plant monitoring control means 300 via the communication interface 127.
The basin water quality prediction means 130 predicts the water quality of the water source 1 and the river 2 based on the information of the water quality evaluation means 120 and the measurement information of the water sensor equipments 21 and 22 of the river 2, and further, the water purification plant which takes in water by the water intake equipment 3. Predict overall raw water quality. FIG. 5 shows an embodiment of the watershed water quality prediction means 130.

The basin water quality predicting means 130 includes the water quality information in the water source 1, the water quality diagnosis and purification information from the water quality evaluating means 120, the water quality measurement information on the river 2 from the water sensor facilities 21 and 22, and the meteorological observation device. Weather measurement information from 180, water surface measurement information from the water surface observation device 185,
In addition, the rainfall measurement information from the rainfall observation device 190 is continuously received, and the water quality and the water amount of the entire water bodies 1 and 2 are diagnosed based on these information. Here, the water quality measurement information is the water temperature,
Turbidity, pH, DO (dissolved oxygen concentration), electrical conductivity, organic matter concentration, nutrient rich (nitrogen, phosphorus compound) concentration, chlorophyll amount, water level, flow velocity, odor, etc.
Water surface measurement information such as wind direction, wind force, and solar radiation is chromaticity of water,
Rainfall measurement information, such as the amount of suspended solids, is rainfall, pH, N
For example, Ox concentration and SOx concentration. Also, the water sensor equipment is installed at two places in FIG. 1, but the number is not limited to this. Further, the meteorological observation device 180, the water surface observation device 185, and the rainfall observation device 190 can be installed in an optimal position for monitoring in consideration of the geographical condition of the monitoring target and can be directly managed, but they can be managed by different management departments. Information is also available. In addition, it is desirable to collect such information from a device installed in each block by dividing the water area into multiple monitoring blocks. In this case, the basin water quality prediction means 130 may be a distributed system method in which the functions are divided, but a one-station management method in which various kinds of information are constructed by wireless or by constructing and transmitting a wired network using a telephone line or an optical fiber is desirable in operation. Information used by the basin water quality prediction means 130 is stored in the database 135 via the communication interface 137.

The central processing unit 131 executes the main program stored in the main storage unit 132 and controls the entire basin water quality prediction means 130. The execution timing is set periodically, or in accordance with the start command signal from the input device 139B and the diagnosis result from the water quality evaluation means 120.
The external storage device 140 stores the topography and geographical data of the water source 1 and the river 2 used by the simulation device 133, and also stores the simulation result and the inference result of the inference device 134. A display device 139A and an input device 139B are connected to the central processing unit 131, and necessary information can be input from the information display in the basin water quality prediction means 130 and a mouse, a light pen, a keyboard, and the like, and each component device via the system bus 141. Information is transmitted.

The simulation device 133 determines the water source 1 and the river 2 on the basis of the water quality and plankton information stored in the database 135, the weather, the water surface, the rainfall information, and the topographical and geographical data stored in the external storage device 140. Predict the current state and future of water quality and discharge. First, the flow rate simulation is based on the amount of water discharged or drained from the amount of water discharged from the water source 1, the tributary river 2B that joins the river 2, and the amount of water in the facility (not shown) that drains the river 2 or the tributary river 2B. Predicts the time it will take for the water intake device 3. The amount of water discharged from the water source 1 is often determined in consideration of the amount of stored water and the amount of rainfall, and the amount of stored water and the season, and it is necessary to grasp the amount of stored water as well as the actual measured value of the amount of discharged water. is there. The water storage amount is calculated from the water level of the water source 1, the topography, and the rainfall amount in the area where the water is collected.
It is possible to predict the future discharge of water based on this water storage. The arrival time to the water intake device 3 is calculated and predicted from the amount of discharged water, the amount of water in the tributary river 2B, the amount of rainfall and water level collected in the river 2, and topographical information.

On the other hand, the water quality simulation predicts the water pollution state of the water source 1, the water quality state of the river 2, and the water intake quality of the water intake device 3. The water quality state of the water source 1 is the water temperature, turbidity, pH, DO (dissolved oxygen concentration), electric conductivity, organic matter concentration, eutrophic salt (nitrogen,
(Phosphorus compound) concentration, chlorophyll amount, water level, flow velocity, odor, and other water quality measurement information and plankton image measurement information, meteorological measurement information from external observation devices 180, 185, 190, water surface measurement information, rainfall measurement information, external storage device 140
It is estimated using the topographical and geographical information of the water source 1 stored in. In this prediction, the water quality change due to the operation of the purification facility 12 can be further considered.

In the water source 1 water quality simulation, the level distribution of various water qualities in the plane and the depth direction of the water source 1 and the water quality of the water discharged to the river 2 are obtained. The water quality status of the river 2 is the water quality simulation result of the water source 1, the water quality measurement information about the river 2 from the water sensor equipments 21 and 22, the rainfall information collected in the river 2, and the external storage device 1.
It is predicted from the topographical and geographical information of the river 2 stored in 40. Here, the topographical and geographical information of the river 2 is the position, length, width, slope, and river bed. In this river water quality simulation, a mixture model of various water qualities along with the flow, and a purification model considering the water temperature and season are taken into consideration, and the water quality distribution in the downflow direction of the river 2 and the arrival time to the intake device 3 and the water quality Relationship is required. Reasoning device 1
In 34, the simulation result and the rules regarding the water intake source and the rules regarding the operation of the water purification plant stored in the knowledge base 136 are called, and the pollution state of the intake water quality and its factors, the purification countermeasure status of the water intake destination, and the water purification plant. Diagnose the driving measures of. This inference is basically performed periodically, but may be performed when the water pollution level in the water source 1 or the water intake device 3 exceeds a predetermined value by simulation. Inference device 134
First, the water pollution level and its items are used to determine whether or not it is necessary for the water purification plant to respond. For example, "Plankton Y has a large growth rate, the odorant concentration C increases,
Plankton Y'which produces odorous substance Cs proliferates "," The water temperature T is high, and the eutrophication concentration E is high, "
Whether or not the advanced treatment plant needs to be operated is determined by the enforcement of rules such as "the raw water has an offensive odor", "the concentration of organic matter in the water source is high, and the concentration of organic matter in the upstream of the river is high". Conversely, it is also possible to diagnose whether or not the operation of the advanced processing plant can be stopped. Further, in this diagnosis, the degree and the time when the water pollution of the water source 1 affects the installation water area of the water intake device 3 based on the simulation result are presented.

In the water purification plant monitoring control means 200, the water quality safety monitoring means 210 detects whether the quality of raw water flowing into the water purification plant is normal or abnormal.
A method of monitoring the behavior of fish using image processing can be applied. One or a plurality of fishes are bred in the organism breeding facility 45 to which a part of the raw water taken by the water intake facility 3 has been sent. The biological breeding facility 45 forms a fish body that is being raised with a material that can be observed from the outside, and illuminates the fish body in the biological breeding facility 45 by illuminating it from one side through a semitransparent material (not shown).

The image pickup device 212 picks up an image of the illuminated fish body, and an industrial television camera or the like is used. The video signal of the imaging device 212 is transmitted to the image analysis device 214. The image analysis device 214 has a configuration similar to that of FIG.
Image processing is performed by A / D converting the video signal from the imaging device 212. An example of the execution procedure is shown in FIG.

First, in the image capturing step 214a, a video is captured at preset time intervals h and stored in the image memory. In the fish body recognition first step 214b, the image captured in the image capturing step 214a is binarized using a predetermined brightness level as a threshold value to recognize the fish body. In the fish body recognition second step 214c, the latest image of the image capturing step 214a is subtracted from the captured image before the time h, and the fish body existing between the two images is synthesized and recognized. In the center of gravity calculation step 214d, the coordinate position of each recognized fish body, that is, the fish body weight center is obtained from the processed images of the fish body recognition first and second steps 214b and 214c. The velocity calculation step 214e calculates the distance of the fish body changed in time h from the coordinate position between the recognized fish bodies in the fish body recognition second step 214c obtained in the center of gravity calculation step 214d. In the fish body dispersion degree calculation step 214f, the center position between the fish body weight centers of the fish body recognition first step 214b obtained in the center of gravity calculation step 214d is obtained, and the distance between this center position and each fish body is calculated. The calculation results of the fish body weight center, the fish body change distance, the center position, and the center-to-center distance are stored in the memory of the image analysis device 214. The above-described image processing and calculation are repeated for a preset measurement time T or the number of times N, and then statistical processing is executed. The position distribution calculation step 214g obtains the position distribution of the fish body from the vertical direction of the center of gravity of all fish obtained at the set time T or the number of times N, that is, the water depth position of the biological breeding facility 45 and its frequency. The velocity distribution calculation step 214h obtains the velocity distribution of the fish body from the fish body change distance and its frequency. Furthermore, the dispersion degree calculation step 214i
Statistical processing is performed on the relationship between the center position and the center-to-center distance and the frequency, and the center position distribution and the dispersity distribution between the fish bodies when breeding multiple fish bodies are obtained. The position distribution, the velocity distribution, the center position distribution, and the dispersion degree distribution are stored in the memory of the image analysis device 214 at every measurement time T or the number of times N, and are output to the computer 216.

The computer 216 analyzes the behavior of the fish based on the input position distribution, velocity distribution, center position distribution, and dispersity distribution, and evaluates whether the water quality of the raw water is normal or abnormal. An example of the analysis evaluation method is shown in FIG. The position distribution, the velocity distribution, the center position distribution, and the dispersity distribution result output from the image analysis device 214 are calculated in step 216a to calculate the average value of each distribution and the area ratio of the specific range to the total area of each distribution. The specific range is, for example, in the case of position distribution, the upper part 1 of the water depth.
It can be arbitrarily set to 0%. Calculation process 216a
The average value and the area ratio of the specific range obtained in step 3 are input to the comparison step 216b and are compared with the reference value from the reference value setting step 216c. The reference value is set based on the experience of the observer when the fish body behaves abnormally or the past breeding monitoring results. When the reference value is exceeded in the comparison step 216b, various distributions in the normal state and the current distribution are compared in the pattern evaluation step 216d, and the abnormal content of the behavior is diagnosed. An example of diagnosis is shown in FIG.

From this diagnosis result, it is possible to grasp whether the abnormality is due to nose lift, madness, or confusion. The water quality evaluation step 216e is the behavior abnormality content of the pattern evaluation step 216d, and the comparison step 216.
The deviation from the reference value in b, the behavior abnormality and the continuity in the deviation direction are evaluated to diagnose the degree of abnormality in water quality. The operation determination step 216f determines the operation method of the water purification plant based on the diagnosis result of the water quality evaluation step 216e. For example,
When the nose is raised, if there is no continuity, an alarm is displayed to the operator. If it is continuing, a voice alarm is issued. At the same time, if there is a madness or confusion with a large degree of water quality abnormality, the water intake facility controller 3A and facility operation are performed at the same time. Outputs a signal to the management means 310,
Stop water intake and inflow to the advanced treatment plant 5. By this operation, it is possible to prevent abnormal water from being introduced into the water purification plant, and to prevent the activity or death of the microorganisms grown in the biological activated carbon tower 53 of the advanced treatment plant 5 to be prevented.

The coagulant injection control means 220 is composed of an image analysis device 222 and a computer 224, and the floc formation pond 43
The image of the flock imaged by the underwater image pickup device 48 installed in is input to the image analysis device 222. The image analysis device 222 has a configuration similar to that of FIG. 3, and performs image processing by A / D converting the video signal from the imaging device 212.

The outline of the function of the image analysis device 222 will be described. The A / D-converted grayscale image is binarized to form a binary image, and the number of flocs, the area, volume, representative particle size, shape, etc. of each floc are calculated from this binary image. Next, the particle size distribution obtained from a plurality of images, the number of particles, a typical particle size obtained by statistically processing the particle size distribution, the standard deviation obtained from the particle size distribution, the floc shape characteristics, the amount of flock formation, the flock And the background brightness and flock density. These flock feature amounts are calculated from a preset measurement time T1 or the number of processed screens N1 and a statistical representative value is calculated each time the time T1 or the number of screens N1 is processed, and the result is output to the computer 224.

In addition to the floc feature amount from the image analysis device 222, various measurement information from the water sensor equipment 47 is input to the computer 224. The measurement items include water temperature, turbidity, alkalinity, pH, electric conductivity, water amount, and water level. These pieces of measurement information are input at predetermined time intervals (1 minute to 1 hour). The computer 224 arithmetically controls the appropriate value of the coagulant injection amount from the coagulant injection facility 46 based on the measurement information. An example of the arithmetic control system is shown in FIG. The floc feature amount from the image analysis device 222 and the measurement information from the water sensor facility 47 are input to the injection amount calculation step 224a and the database 224b.
The database 224b also stores past values of the floc feature amount and the measurement information. Teacher database 224
In c, a plurality of examples of the floc feature amount, the measurement information, and the coagulant injection amount in the past successful operation examples are stored. In the injection amount calculation step 224a, the coagulant injection amount to be operated is calculated from the present value of the floc feature amount and the measurement information, the past information that affects the current operation, and the stored data of the teacher database 224c. For the calculation, the inventors have proposed so far (for example, Japanese Patent Laid-Open No. 3-15902).
10) can be used. FIG. 10 shows an example of a network having a three-layer structure. The input layer 224A is provided with the current value and the past value of the measurement information and the flock image feature amount, and the time deviation values thereof by the number of items (neurons A _{1 to An} ), and the intermediate value Layer 224B
The recall (simulation) result of the coagulant injection amount is output to the output layer 224C via (neurons B _n <A _n ) based on the weighting factors W _i and W _j . Weighting factors W _i , W
_j inputs information on successful driving to the teacher class 224D,
The result of the output layer 224C calculated by using the past information of various input items of the input layer 224A is compared in the comparison layer 224E, and the correction learning is sequentially performed so that the error is equal to or less than the specified value, and a new unlearned condition is input. Then, recall the coagulant injection amount. The error evaluation step 224d compares the coagulant injection amount output result from the injection amount calculation step 224a with the coagulant injection amount operation result at the same time as this output result from the database 224b, and when the error is large, it is stored in the database 224b. The condition is added or updated to the teacher database 224c as a new successful driving case. By performing these learnings, the error evaluation step 22
The coagulant injection amount at which the error becomes equal to or less than the specified value in 4d is the coagulant injection equipment 46, equipment operation management means 310, sedimentation evaluation step 2
24e. In the sedimentation evaluation step 224e, the floc formation amount is judged from the measurement information, the floc image feature amount and the coagulant injection amount, and the floc sedimentation characteristics in the sedimentation basin 44 and the turbidity substance amount contained in the treated water of the first water purification plant 4 are determined. It predicts and outputs to the biological activated carbon control means 330.

The advanced processing plant monitoring control means 300 is
Monitoring the quality of influent water and treated water of the advanced treatment plant 5,
This is for diagnosing and managing the operating conditions of various processing equipments. The equipment operation management means 310, the ozone contact pond control means 320,
The biological activated carbon tower control means 330 is used. The equipment operation management means 310 has a function of managing the operation of the entire equipment constituting the advanced processing plant 5, and one embodiment thereof will be described with reference to FIG. In the equipment operation management means 310, the type and amount of the plankton from the plankton monitoring means 110, the measurement information of the water sensor equipment 13 from the water quality evaluation means 120, the water quality diagnosis information, and the purification measure information, the basin water quality prediction means 130 Delivery time, the intake water quality prediction value considering the delay due to the delivery time, the water quality determination information from the computers 216 and 224 of the water purification plant monitoring control means 200, the coagulant injection amount, and the raw water flow rate from the water sensor facility 47. Is entered. The water quality determination step 351 compares the intake water quality predicted value with a preset reference value, determines that advanced processing is required when the predicted value is large, and processes the signal and the deviation between the predicted value and the standard value by advanced processing. Output to the flow rate determination step 352.
Here, the predicted water quality value is the odor concentration (diosmin, 2-MI
B), the concentration of trihalomethane precursors such as humic substances, the concentration of organic substances, the concentration of ammonia nitrogen, the chromaticity, and the metal ions such as iron and manganese, which can be removed by the advanced treatment plant 5. Further, the reference value is set for each target item. In the advanced treatment flow rate determination step 352, among the items exceeding the reference value, the item having the highest ratio between the predicted value and the reference value is targeted, and the advanced treatment flow rate ratio to the total raw water flow rate Q (m ³ / d) is calculated by the following formula. η (−) is determined.

Η = (C ₁ −C) / (C ₁ −C ₂ ) ... (4) Here, C is the reference value (mg / l) of the target item, and C ₁ is the predicted water quality value (mg / l). , C ₂ is the water quality concentration of highly treated water (mg / l)
Is. The water quality concentration C ₂ is the reaction in the advanced treatment plant 5.
(Retention) time, ozone injection amount and absorption efficiency of ozone contact pond 52, adsorption efficiency of biological activated carbon tower 53, microorganism concentration and its activity can be obtained by simulation.

Further, when a substance that can be completely removed in the advanced treatment plant 5 is a judgment item, it can be set to 0. The actual advanced treatment flow rate Q '(m ³ / d) is obtained by ηQ,
It is output to the flow rate distribution step 353. Flow rate distribution process 353
Sends a signal output corresponding to Q'to the controller 59, which operates the water distributor 51 to maintain the flow rate to the advanced treatment plant 5 at Q '. The water distributor 51 uses, for example, an electric gate valve. Note that (1-η) Q does not pass through the advanced treatment plant 5, is subjected to the treatment in the first water purification plant 4, and is sent to the mixing adjustment pond 71 of the second water purification plant 7 via the bypass line 60. The advanced treatment operation determination step 354 is based on the flow rate Q ′ output from the advanced treatment flow rate determination step 352 and the water quality concentration (C ₁ -C) to be treated, and the number of operating ozone contact basins 52 and biological activated carbon towers 53,
The number of operating ozone generators 55 and the operating conditions are determined. This is an ozone contact pond 5 in one water treatment plant.
2. When a plurality of biological activated carbon towers 53 and ozone generating equipment 55 are installed, respectively, and when all are one, only operating conditions such as gas flow rate and ozone generation concentration of ozone generating equipment 55 are determined. The number of operating ozone contact basins 52 and biological activated carbon towers 53 depends on the flow rate Q ′ of each reaction.
Basically, the (residence) time satisfies a predetermined value set in advance. The number of operating ozone generating facilities 55 corresponds to the number of operating ozone contact basins 52, and the operating conditions are determined from the required treated water quality concentration (C ₁ -C). For example, if (C ₁ -C) is large, the operating conditions of the gas flow rate or ozone generation concentration are selected so that the ozone injection amount (g / h) increases.

The cost diagnosis step 355 includes the number of operating ozone generating facilities 55 determined in the advanced treatment operation determining step 354 and the operating conditions thereof, and the cost required for operating the exhaust ozone treating facility 56 accompanying the operation of the ozone contact basin 52. The cost required for the coagulant injection amount output from the computer 224 of the coagulant injection control means 220 is calculated. The operation execution determination step 356 diagnoses the quality of the operation cost calculated in the cost diagnosis step 355, and if it is good, the advanced treatment operation determination step 354 operates the ozone contact pond 52, the biological activated carbon tower 53, and the ozone generation facility 55. The number of units and the operating conditions are output to the calculator 324 and the calculator 334 of the ozone contact tank control means 320 and the biological activated carbon tower control means 330. When the cost is too high, the raw water quality concentration C ₁ is corrected based on the effective ozone consumption in the ozone contact pond 52 described later. In the process flow rate correction step 357, the flow rate Q ″ corresponding to the corrected water quality concentration is calculated,
The output signal of the flow rate distribution step 353 is corrected. In addition, the flow rate, which is the determination factor of the advanced processing operation determination step 354, is corrected. In the present embodiment, the cost diagnosis is executed to correct the output signals of the advanced treatment operation determination step 354 and the flow rate distribution step 353. However, the cost diagnosis step 355 and the subsequent steps are omitted in a water purification plant on which water quality is improved. You can further,
The various measurement, diagnosis, and prediction information input to the advanced processing plant monitoring control means 300 is stored in the storage device 358 and appropriately displayed on a display device (not shown) so that the water quality status upstream of the intake pond can be referred to.

The ozone contact pond control means 320 is composed of an image analysis device 322 and a computer 324.
5 and the operating conditions of the waste ozone treatment equipment 56 are diagnosed and controlled. The image analysis device 322 has the same configuration as that of FIG. 3, and an example of the processing procedure is shown in FIG. A grayscale image obtained by digitizing the video signal of the ozonized gas bubbles imaged by the imaging device 61A installed inside the ozone contact pond 52 or by modifying a part of the ozone contact pond 52 in the A / D conversion step 322a. In the gradation enhancement step 322b, processing for further clarifying the brightness difference in the area where the change in brightness (brightness) is large is performed. By this processing step, the contour portion of the gas bubble existing in the image is emphasized and the gas bubble is made clear. The grayscale image emphasized in the bubble recognition step 322c is binarized to obtain a binary image. In this first binary image, the outline portion of the bubble is well extracted, and the bubble is recognized as an object with holes. Next, in the first binary image, the hole in the contour part is filled, and the whole contour is processed as an object, that is, a bubble,
Obtain a second binary image. Further, the first and second binary images are differentiated to recognize only the object for which the filling processing has been executed. The object in the second binary image in the same area as the object extracted in the difference image is regarded as a bubble, and is identified and recognized as the object in the second binary image deleted in the difference image. In the feature amount calculation step 322d, the number of objects identified in the bubble recognition step 322c, the area, volume, representative particle size, shape, etc. of each object are calculated. Feature quantity statistical calculation step 322e
Collects a plurality of screens of the above feature amount for each single screen output from the feature amount calculation step 322d and performs statistical processing to calculate the particle size distribution of bubbles, total number, average particle size, standard deviation of particle size, etc. To do. In the bubble recognition step 322c, the same calculation is executed for the object identified as a bubble, but it is preferable to exclude the object having a particle diameter of a predetermined value or less from the calculation target. This is because even if a bubble is too small in particle size, it is not recognized as an object having a hole in the first binary image and cannot be distinguished from objects other than bubbles. These object feature amounts are calculated from a preset measurement time T2 or the number of processed screens N2, and a statistical representative value is calculated each time the time T2 or the number of screens N2 is processed, and the result is output to the computer 324.

The computer 324 sets the operating conditions of the ozone generation equipment 55 and the exhaust ozone treatment equipment 56, and is composed of an ozone generation equipment control step 324a and an exhaust ozone treatment equipment operation support step 324b. A concrete example is shown in FIG.
3 will be used for the explanation. Ozone generation equipment control process 324a
In the figure, reference numeral 360 is a number-of-units determining step, 361 is an equipment operation planning step, 362 is an operating equipment setting step, 363 is a data storing step, 364 is a method setting step, 365 is a data selecting step, 366 is an ozone injection amount calculating step, 367 is It is a generation equipment condition setting process. Exhaust ozone treatment facility operation support process 3
In 24b, 370 is an absorption efficiency determination step, 371 is a pressure determination step, 372 is an adsorption efficiency determination step, and 373 is a breakthrough state determination step.

In the ozone generation equipment control step 324a, first, the ozone contact pond 52 and the ozone generation equipment 55 transmitted from the equipment operation management means 310 to the number determination step 360.
And the operating conditions of the ozone generation facility 55 are input. In the number-of-units determination step 360, it is determined whether or not the input number and conditions can be operated, and if possible, the input information is directly output to the facility operation planning step 361. Convert and output. The equipment operation planning process 361 plans which equipment among a plurality of equipments is to be operated or stopped according to input conditions. This plan diagnoses the past operation history of each facility, the current management status (for example, during failure or repair inspection), whether the ozone gas flow rate and its ozone concentration meet the input conditions, etc. Used as a factor. The operating equipment setting step 362 displays the names and operating conditions of the ozone contact pond 52 and the ozone generating equipment 55 selected in the equipment operation planning step 361 as guidance and presents them to the operation manager. An input port is prepared in the operation equipment setting step 362 so that the operation manager can approve or change the guidance content. The approved or changed content is output to the generated facility condition setting step 367.

In the data storage process 363, the water temperature, turbidity, pH, DO (dissolved oxygen concentration), ORP (oxidation-reduction potential), electric conductivity, organic matter concentration, rich nutrients (nitrogen) from the water sensor equipment 62 and 62A are stored. , Phosphorus compound) Concentration, flow rate, odor, chromaticity and other water quality measurement information before and after the ozone contact pond 52,
PH, DO, ORP, and electrical conductivity from the water sensor equipment 61A, ozonized gas flow rate and ozone concentration from the gas sensor equipment 63A, target substance prediction value from the watershed water quality prediction means 130, image analysis device 322 The object feature amount is input and stored. The method selection step 364 is for selecting and instructing an ozone injection amount adjustment method, which can be set by an operation manager from an input port (not shown). The data selection step 365 commands the transmission of necessary data items and their information from the data storage step 363 corresponding to the ozone injection amount adjustment method set in the method selection step 364. In the ozone injection amount calculation step 366, the ozone injection amount is calculated based on the ozone injection amount calculation method selected based on the setting method of the method selection step 364 and the input data from the data selection step 365. Next, the ozone injection amount calculation method will be described. FIG. 14 shows an embodiment using the neural network system. Water quality and ozone-related measurement information, bubble image feature amounts, and time deviation values thereof are given to the input layer 36a, and ozone is injected to the output layer 36c through the intermediate layer 36b based on the respective weighting factors W _i and W _j. The amount recall (simulation) result is output. The teacher layer 36e inputs information at the time of successful driving, compares the result of the output layer calculated using the past information of various input items of the input layer with the comparison layer 36d,
Sequential correction learning is performed so that the error becomes equal to or less than a specified value, a new unlearned condition is input to recall the ozone injection amount, and the ozone injection amount is output to the generation equipment condition setting step 367.

FIG. 15 shows a method of calculating the operation amount by judging the consumption amount of ozone injected into the ozone contact pond 52. The consumption amount determining step 37a first calculates the effective ozone consumption amount U from the following equation based on the material balance.

U = (injection amount) − (exhaust gas residual amount) − (processed water residual amount) − (dissolved oxygen decomposition amount) (5) Here, the dissolved oxygen decomposition amount is ozone self-decomposed and converted into oxygen. It can be determined by setting the decomposition coefficient corresponding to the water temperature and the residence time with the amount of ozone to be generated. It can be determined that the ozone effective consumption amount U has been consumed for decomposing and removing the substance to be removed. Therefore, substances with a clear composition, such as diosmin and 2-MIB, need ozone for decomposition per unit amount of substance a (mg ozone / mg substance)
Can be defined. From the ozone required amount a and the ozone effective consumption amount U (ozone g / h), the ozone consuming substance concentration C
s (mg / l) can be calculated by the following formula.

Cs = U / a · Q (6) Here, Q is the treated water amount (m ³ / h). In the water quality determination step 37b, the concentration of ozone consuming substance remaining in the treated water is determined from the predicted value of the substance to be treated and the ozone consuming substance concentration Cs from the basin water quality predicting means 130. The injection amount correction step 37c corrects the ozone injection amount in accordance with the ratio of the target substance predicted value and the residual ozone consuming substance concentration, and outputs it to the generation equipment condition setting step 367. The generation equipment condition setting step 367 changes the number of ozone contact basins 52 and ozone generation equipment 55 and operates the ozone generation equipment 55 according to the ozone injection quantity from the ozone injection quantity calculation step 366 and the output signal from the equipment operation planning step 361. Set the conditions. Ozone generator 55
The operating condition is that the flow rate of the ozonized gas sent from the ozone generating equipment 55 or the ozone concentration in the ozonized gas is adjusted so that the input ozone injection amount is obtained.
Set the supply air volume, voltage, discharge frequency, etc. These set amounts are transmitted to the ozone generation equipment control device 55A. In this way, by operating the ozone contact pond 52 and the ozone generating facility 55 in consideration of the substance to be treated, it is possible to stably supply treated water of good water quality, and to inject the necessary amount of ozone, ozone is required. The operating cost of the generating facility can be reduced.

In order to improve the efficiency of injected ozone, it is also effective to reduce the bubble diameter of the ozone contact reservoir 52 and increase the contact surface area. However, forming bubbles with extremely small diameters increases the pressure loss and increases the power cost of the ozone generating facility 55. Therefore, there is an appropriate particle size in terms of penetration efficiency and cost. Here, the average particle size d of the bubbles obtained by the image analysis device 322 is compared with a preset particle size target value D, and the ozonized gas flow rate from the ozone generating facility 55 is adjusted so that the average particle size becomes the target value. Alternatively, the blowing pressure can be manipulated. Further, as an ozone injection amount calculation method, the ozone concentration in the exhaust ozonized gas discharged from the ozone contact basin 52 is maintained at a constant value, which is proportional to the flow rate of the raw water inflow, and further outflows from the ozone contact basin 52. It is also possible to maintain the ozone concentration remaining in the treated water at a constant value. Further, it is also possible to apply a combined system in which correction is made based on the concentration of the substance to be treated and the ozone consumption amount, based on these flow rate proportional and ozone constant value control systems. In the exhaust ozone treatment facility operation support process 324b, first, the ozone concentration in the ozonized gas from the ozone generation facility 55 input in the absorption efficiency determination process 370 is compared with the ozone concentration in the exhaust ozone gas of the ozone contact pond 52. If the deviation is larger than the predetermined value, the information indicating that the absorption efficiency of injected ozone is deteriorated is determined as the pressure determination step 371.
Output to. In the pressure determination step 371, the ozone contact pond 5
The present pressure in 2 is compared with the allowable pressure of the ozone generator 55, and when it is determined that the allowable pressure has a margin, a signal is output to the pressure adjusting device 64 to operate the pressure in the ozone contact pond 52. Increase absorption efficiency. If the deviation is less than the specified value,
Since the burden is placed on the ozone generating facility 55 and the operating cost is increased, the pressure adjusting device 64 is operated so as to reduce the pressure in the ozone contact basin 52. By this operation,
It is possible to stabilize the ozone absorption efficiency and reduce the operating cost. In the adsorption efficiency determination step 372, the ozone concentration in the exhaust ozone gas and the ozone concentration in the exhaust gas of the exhaust ozone treatment facility 56 are input, and the deviation amount thereof is the breakthrough state determination step 3
It is output to 73. In the breakthrough state determination step 373, the deviation amount is diagnosed, and when the deviation amount is large, that is, when the ozone treatment capacity of the exhaust ozone treatment equipment 56 deteriorates, it is determined that the adsorbent has passed through or deteriorated, and the adsorbent is replaced. The guidance such as is displayed on a display device (not shown). Due to the prompt replacement of the adsorbent by this guidance,
High-concentration ozone gas is not emitted, and the safety of workers is secured.

The biological activated carbon tower control means 330 comprises an image analysis device 332 and a computer 334.
It diagnoses the operation status of and executes backwash start / stop control.
The image analysis device 332 has the same configuration as that of FIG. 3, and an example of the processing procedure is shown in FIG. The video signal of the effluent of the biological activated carbon tower 53 captured by the image capturing device 61B is sent to the image analyzing device 332. The imaging device 61B has, for example, the structure of the underwater suspended matter imaging device 11 described above, and is immersed in the stored water tank 58 as shown in FIG.
The structure shown in can be used. In FIG. 16, the image pickup device 61 B includes a water conduit 61 that is part of the water conduit 611.
One of the window glasses 611A and 611B that is in contact with the effluent of the biological activated carbon tower 53 flowing through 1 moves up and down, and the liquid between them is magnified by a magnifying lens 612, and a television camera 613.
Take a magnified image with. An illumination device 614 is installed so as to face the magnifying lens 612. A part of the treated water and the backwash drainage of the biological activated carbon tower 53 can be guided to the water conduit 611 by the switching device 615. Image analysis device 33
In 2, first, the analog image signal from the image pickup device 61B is digitized in the A / D conversion step 332a, and the obtained grayscale image is subjected to processing to clarify the change in luminance (brightness) in the grayscale enhancement step 332b. This process step enhances the contours or the whole of the suspended matter present in the image,
Suspended matter becomes clear. The grayscale image emphasized in the object recognition step 332c is binarized to obtain a binary image. In the binary image, the object part is identified as the "1" region and the liquid part is identified as the "0" region. In the feature amount calculation step 332d, the object recognition step 332
The number of objects identified in c and the area, shape, brightness, etc. of each object are calculated. In the object determination step 332e, it is determined whether the object is a microorganism or activated carbon based on the area, shape, brightness, etc. of each object. The spillage evaluation step 332f collects a plurality of screens of the above determination results for each single screen output from the object determination step 332e, performs statistical processing, and calculates the spillage evaluation and the amount of the spilled material from the biological activated carbon tower 53. . These leaked substances are calculated from a preset measurement time T2 or the number of processed screens N2, a statistical representative value is calculated every time the time T2 or the number of screens N2 is processed, and the result is calculated by the computer 32.
Output to 4.

The function of the computer 324 will be described with reference to FIG. The water quality comparison step 380 compares the water quality measurement information of the water sensor equipments 62A and 62B at the time of passing water, that is, at the time of water treatment, and compares the water quality measurement information of the water sensor equipments 62B and 62C at the time of backwashing to calculate the deviation. To do. In the activated carbon determination step 381, it is determined that the activated carbon has passed if the deviation in passing water is smaller than a predetermined value. If the deviation is smaller than a predetermined value during backwashing, it is determined that the washing is completed. It should be noted that this determination is executed when the water quality measurement information from the water sensor facility 62A at the time of water flow is equal to or higher than a predetermined value. The determination result is output to the driving operation determination step 385. The differential pressure comparison step 382 is executed only during water passage, and compares the water pressures of the water sensor facilities 62A and 62B, obtains the pressure loss thereof, and outputs the pressure loss to the clogging determination step 383. In the clogging determination step 383, when the pressure loss is equal to or larger than a predetermined value, it is determined that the activated carbon filling portion is heavily clogged, and the result is output to the driving operation determination step 385. In the spillage determination step 384, microbial detachment or activated carbon or turbidity outflow is determined based on the content of the spillage. In the case of water flow, the presence or absence of spilled material is used to determine microbial exfoliation or turbidity outflow. In the driving operation determination step 385, based on these determination results, it is determined whether backwashing of the biological activated carbon tower 53 is started, stopped, or water is started, and an operation signal is output to the backwash facility control device 57A. The backwash facility 57 is operated by this signal. Although not shown in the figure, the backwash operation generally uses treated water, but air can also be used in combination. In addition, the presence or absence of backwashing operation and its timing can be determined using the pressure loss and the reduction rate of the water flow rate accompanying the change in pressure loss as an index. When air is also used, the backwash operation can be performed in two steps. In the first stage, both treated water and air are blown in to enhance the cleaning ability. As a result, not only the activated carbon surface,
Part of the adsorbed substance in the pores is also desorbed. The second step is to wash with treated water only, to completely remove the desorbed substances and to remove the substances that are about to desorb on the activated carbon surface. When air is not used, the backwashing operation in the above two stages can be performed by changing the backwashing flow rate and the blowing pressure of the treated water. Further, the surface of the activated carbon packed bed can be photographed by an underwater camera or the like, and the backwashing operation can be carried out based on the monitoring result of the suspended matter accumulation state. Further, as a backup of these judgment and monitoring results, backwash termination can be executed by setting a timer.

The water distribution facility monitoring control means 400 supports the operation management of the second water purification plant 7, the treatment amount of the entire water treatment plant, and the amount of water distribution to the tap water utilization facility 9. The sand filter control means 410 compares the pressures or water levels of the water sensor facilities 76A and 76B, judges that the clogging is large when the deviation is equal to or more than a predetermined value, and issues a command to the backwash facility control device 73A to perform backwash. The equipment 72 is driven to wash the sand filter basin 72.

The wide area water operation management means 420 is the distribution reservoir 81.
Water sensor equipment 85, water distribution network 82A installed in
Residual chlorine concentration at the tap water utilization facility 9 is controlled by controlling the chlorine injection facility 75 depending on the residual chlorine concentration from the water sensor facilities 83A, 83B, 83C and 83D installed at arbitrary positions of 82B, 82C and 82D and the presence or absence of organisms. To be the minimum necessary.

The water demand forecasting means 430 is the water sensor equipment 8
The water demand is predicted from the flow velocity or flow rate from 3A, 83B, 83C, and 83D, and the intake facility control device 3A is output to operate the intake volume from the intake facility 3. Further, the estimated water intake amount to be operated is also output to the equipment operation management means 310. A method using a neural network can be applied to predict water demand based on input factors such as precipitation, sunshine hours, temperature, day of the week, and past operation history data. In addition, the water distribution plan under the constraints of water intake, demand, and reservoir capacity,
Diagnosis and renewal plan of aged pipes based on pipeline database, simulate whether residual chlorine to consumers is appropriate using geographical information, and supply delicious water made at the water purification plant to consumers without excess or deficiency. You can also

As described above, by consistently controlling the water quality from intake to water supply, delicious water can always be stably supplied.

[0080]

According to the present invention, since the plant is operated and managed according to the quality of raw water, the operating cost can be reduced without wastefully operating the equipment. Further, since the amount of ozone required for the oxidative decomposition of the target substance is injected, delicious water with good water quality can be produced at the minimum necessary cost. Further, since the suspended matter flowing out from the biological activated carbon tower is monitored and the backwashing operation is carried out, safe and reliable water free from microorganisms and turbidity can be provided. In addition, whether or not the residual chlorine to the customer is appropriate is analyzed and diagnosed from the pipeline network and the distribution flow rate, and the chlorine injection amount is guided so that the minimum residual chlorine concentration that satisfies the standard value is provided, and it is produced at the water purification plant. It is possible to supply delicious water to consumers without excess or deficiency and without change. As described above, according to the present invention, it is possible to consistently control the water quality from intake to water supply, and it is possible to consistently and stably supply delicious water that is safe and reliable to consumers.

[Brief description of drawings]

FIG. 1 is an embodiment showing the overall configuration of an operation control device for a water supply plant according to the present invention.

FIG. 2 is a configuration diagram illustrating an example of plankton monitoring means.

FIG. 3 is a configuration diagram showing hardware of an image processing apparatus.

FIG. 4 is a block diagram showing hardware of water quality evaluation means.

FIG. 5 is a configuration diagram illustrating an embodiment of a watershed water quality prediction means.

FIG. 6 is a configuration diagram showing an example of an image processing execution procedure for fish body image recognition.

FIG. 7 is a configuration diagram illustrating an example of an analysis and evaluation method of fish body behavior.

FIG. 8 is a configuration diagram showing a method for diagnosing abnormal fish behavior.

FIG. 9 is a configuration diagram illustrating an example of a coagulant injection amount calculation control system.

FIG. 10 is a configuration diagram illustrating an example of coagulant injection support by a neuron.

FIG. 11 is a configuration diagram showing an embodiment of an operation management method for advanced processing plant equipment.

FIG. 12 is a configuration diagram showing an execution procedure of bubble image monitoring of an ozone contact pond.

FIG. 13 is a configuration diagram showing an example of operating condition setting of ozone generation equipment and exhaust ozone treatment equipment.

FIG. 14 is a configuration diagram showing an example of an ozone injection amount calculation method using a neuro.

FIG. 15 is a configuration diagram showing an example of an ozone injection amount calculation method based on consumption determination.

FIG. 16 is a configuration diagram showing an execution procedure for monitoring a biological activated carbon tower leaked material.

FIG. 17 is a configuration diagram showing an example of a backwash diagnosis method for a biological activated carbon tower.

[Explanation of Codes] 1 ... Water source such as lake or dam, 2 ... River, 3 ... Intake facility, 4 ... First water purification plant, 5 ... Advanced treatment plant, 7
… Second water purification plant, 8… Water distribution facility group, 9… Tap water utilization facility, 11,48… Underwater suspended substance imaging device, 12… Purification facility, 13,21,22,47,62A, 62B, 62
C ... Water sensor equipment, 43 ... Flock formation pond, 45 ... Biological breeding equipment, 46 ... Flocculant injection equipment, 51 ... Water distribution device, 52 ... Ozone contact pond, 53 ... Biological activated carbon tower, 55 ...
Ozone generation equipment, 56 ... Waste ozone treatment equipment, 57 ... Biological activated carbon tower backwash equipment, 61A, 61B, 212 ... Imaging device, 63A, 63B, 63C ... Gas sensor equipment, 74 ... Chlorine mixing pond, 75 ... Chlorine injection equipment , 100 ... Wide area hydrosphere monitoring means, 110 ... Plankton monitoring means, 120 ... Water quality evaluation means, 130 ... Watershed water quality prediction means, 200 ... Water purification plant monitoring control means, 210 ... Water quality safety monitoring means, 220
... Flocculating agent injection control means, 214, 222, 322, 33
2 ... Image analysis device, 216, 224, 324, 334 ...
Computer, 300 ... Advanced processing plant monitoring control means, 31
0 ... Equipment operation management means, 320 ... Ozone contact pond control means, 330 ... Biological activated carbon tower control means, 400 ... Water distribution equipment monitoring control means, 410 ... Sand filtration pond control means, 420 ... Wide area water operation management means, 430 ... Water Demand forecasting means.

─────────────────────────────────────────────────── ─── Continuation of the front page (51) Int.Cl. ⁵ Identification number Reference number within the agency FI technical display location G01N 33/18 A 7055-2J (72) Inventor Naoki Hara 5-2 Omika-cho, Hitachi-shi, Ibaraki No. 1 Incorporated company Hitachi Ltd. Omika factory (72) Inventor Shigeo Shiono 1-1-1 Kokubuncho, Hitachi City, Ibaraki Prefecture Incorporated Hitachi Ltd. Kokubun factory (72) Inventor Masahiko Ishida Seven Omikacho, Hitachi City, Ibaraki Prefecture 1-1-1, Hitachi, Ltd. Hitachi Research Laboratory

Claims (13)

[Claims] 1. An operation control device for a water supply plant installed in a water purification plant using water from a river having water sources such as lakes and dams as a water source, and means for measuring the concentration of water pollutants in the water source. A means for determining the odorous substance concentration from the water pollutant concentration obtained by the measuring means, a means for predicting the time to reach the intake source of the water source water having the odorous substance concentration obtained by the determining means And a means for operating and managing the water purification plant based on the arrival time of the predicting means, and an operation control device for a water supply plant. 2. An operation control device for a water supply plant installed in a water purification plant using water from a river such as a lake or dam as a water source, said means for measuring the concentration of water pollutants in the water source. Means for determining the odorous substance concentration from the water pollutant concentration obtained by the measuring means, time to reach the water intake source of the source water having the odorous substance concentration obtained by the determining means, and the odorous substance An operation control device for a water supply plant, comprising: a means for predicting the concentration, and means for operating and managing the water treatment plant based on the arrival time of the predicting means and the odorous substance concentration. 3. The operation control device for a water supply plant according to claim 2, further comprising means for setting an operation mode of the water purification plant based on the arrival time of the predicting means and the odorous substance concentration. 4. An operation control device for a water supply plant installed in a water purification plant using water from a river having water sources such as lakes and dams, and means for measuring the concentration of water pollutants in the water source. A means for determining the odorous substance concentration and / or the humic substance concentration from the water pollutant concentration obtained by the measuring means, and a water source water having the odorous substance concentration and / or the humic substance concentration obtained by the determining means Means for predicting the time to reach the water intake source and the odorous substance concentration and / or humic substance concentration, and the operation of the water purification plant based on the arrival time and the odorant substance concentration and / or humic substance concentration of the predicting means An operation control device for a water supply plant, comprising: means for setting a method. 5. The water purification plant has a coagulation sedimentation treatment process and an advanced treatment process composed of an ozone reaction tank and a biological activated carbon tank, and the delivery time of the predicting means and the odorous substance concentration and / or humic substance concentration. The operation control device for a water supply plant according to claim 1, further comprising means for determining whether or not the advanced treatment process can be operated. 6. The operation control device for a water supply plant according to claim 5, wherein the number of operating ozone reaction tanks and biological activated carbon tanks is set by the determination means. 7. The operation control device for a water supply plant according to claim 5, wherein the number of operating ozone reaction tanks and ozone generators is set by the determination means. 8. The method according to claim 5, further comprising means for adjusting the raw water flow rate to the coagulation sedimentation treatment process and the advanced treatment process, and based on the delivery time and the odorant concentration and / or humic substance concentration of the predicting means. An operation control device for a water supply plant, comprising: means for determining the flow rate of an advanced treatment process. 9. The means for calculating the amount of ozone consumed by the substance to be treated in the ozone reaction tank according to claim 5,
An operation control device for a water supply plant, comprising means for adjusting an ozone injection amount based on an ozone consumption amount of the calculation means. 10. The method according to claim 5, further comprising means for calculating the amount of ozone consumed by the substance to be treated in the ozone reaction tank, and odorous substance concentration and / or humic substance concentration obtained from the determining means. Means for predicting the time to reach the water intake source and the odorous substance concentration and / or humic substance concentration of the source water, the ozone consumption of the computing means, the delivery time of the estimating means and the odorous substance concentration and / or An operation control device for a water supply plant, which further comprises means for adjusting the ozone injection amount based on the humic substance concentration. 11. The operation control device for a water supply plant according to claim 1, wherein the water pollutant concentration obtained by the measuring means is a phytoplankton generation amount. 12. A device for monitoring ozonized gas bubbles in the liquid in the ozone reaction tank according to claim 5, and a device for adjusting the operation amount of the ozone generator based on the bubble particle size from the monitoring device. An operation control device for a water supply plant, comprising: 13. The means for monitoring suspended matter in the treated water of the biological activated carbon tank according to claim 5, and the backwashing and washing of the biological activated carbon tank based on the suspended matter monitoring result from the monitoring means. An operation control device for a water supply plant, which has a means for executing