Water quality simulation method, water quality simulation device, and method for determining the number of carriers for treating pollutants by a fluidized carrier method

Abstract

To accurately simulate water quality after treatment by considering effect of interspecific competition among a plurality of microbial populations which decompose respective different polluted substances contained in water to be treated.SOLUTION: A water quality simulation method according to the present invention is a method in a treatment process for biologically treating target water to be treated. The method simulates water quality after treatment of the target water with a plurality of microbial populations, on the basis of a model expression which has a term expressing interspecific competition of a plurality of different microbial populations which decompose respective different polluted substances contained in the target water.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a water quality simulation method, a water quality simulation device, and a method for determining the number of carriers for treating pollutants by a flowing carrier method. [Background technology]

[0002] BACKGROUND ART Conventionally, a biological treatment method for water to be treated using an aerobic fluidized bed is known, which is capable of selectively and efficiently removing thiacyanate ions (see, for example, Patent Document 1).

[0003] In addition, a water quality simulation method is known in which pollutants (COD) in ammonia water are fractionated into phenol, thiocyanate, thiosulfate, and persistent COD, and the decomposition rate of each component is indirectly measured by an oxygen consumption experiment (see, for example, Patent Document 1). 2 (See [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2016-112556 [Patent Document 2] Patent No. 5435658 Summary of the Invention [Problem to be solved by the invention]

[0005] For example, coke oven wastewater (ammonia water) undergoes physicochemical treatment, followed by the activated sludge process, a biological water treatment method, to treat the COD (Chemical Oxygen Demand) components that meet the wastewater standards before being discharged. The COD in the treated water can increase due to improper treatment of thiocyanide (SCN), a COD component, in the ammonia water, or the generation of nitrite from ammonia. For this reason, stable and efficient ammonia water treatment is required in production activities.

[0006] However, the technology described in Patent Document 1 biologically treats the water to be treated using actual equipment that uses an aerobic fluidized bed, and does not anticipate simulating water quality.

[0007] Furthermore, the technology described in Patent Document 2 does not take into consideration the interspecific competition that occurs between the microbial groups treating each pollutant and the impact of this interspecific competition on the water quality after treatment. For this reason, it is difficult to accurately simulate the water quality after treatment using the method described in Patent Document 2.

[0008] Therefore, the present invention aims to accurately simulate the water quality after treatment, taking into account the influence of interspecies competition between multiple microbial groups that decompose each of the multiple different pollutants contained in the water to be treated, and to accurately determine the number of carriers for treating pollutants using the flowing carrier method. [Means for solving the problem]

[0009] The gist of the present disclosure is as follows.

[0010] (1) In the biological treatment process of the treated water , executed by the processor A water quality simulation method, comprising: A term representing interspecies competition among a plurality of different microbial groups that decompose each of a plurality of different pollutants contained in the water to be treated. The term "microorganisms" indicates that the higher the density of any one of the microorganism groups, the greater the reduction in the other microorganism groups. A water quality simulation method for simulating the water quality after treatment of the water to be treated by the plurality of microorganism groups based on a model formula having the following formula:

[0011] (2) The model formula is a formula for the plurality of microorganisms. For each, the microorganisms grow according to the concentration of the pollutants to be decomposed. A term representing the self-reproduction and extinction of the microbial population due to overcrowding, and a term representing the extinction of multiple microbial populations For each, the higher the density, the more the microbial population is reduced. The water quality simulation method according to (1) above, further comprising a term representing autolysis.

[0012] (3) The model formula is for each of the plurality of pollutants. The higher the concentration of pollutants that are decomposed, Toxicity the law of nature The microbial community The increase in The water quality simulation method according to (1) or (2) above, further comprising a term representing:

[0013] (4) A water quality simulation method according to any one of (1) to (3) above, which calculates the decomposition rate of each of the plurality of pollutants using the model formula, and fits parameters representing the model formula based on the actual measured values ​​of the decomposition rates of the plurality of pollutants and the calculation results of the decomposition rates using the model formula.

[0014] (5) A water quality simulation method according to any one of (1) to (3) above, which calculates the concentration of each of the plurality of pollutants in the treated water using the model formula, and fits parameters representing the model formula based on the actual measured values ​​of the concentrations of each of the plurality of pollutants in the treated water when they reach a steady state and the calculation results of the concentrations of each of the pollutants using the model formula.

[0015] (6) A water quality simulation method according to any one of (1) to (3) above, which calculates the concentration of each of the plurality of pollutants in the treated water using the model formula, and fits parameters representing the model formula based on the actual measured values ​​of the concentrations of each of the plurality of pollutants in the treated water at each measurement time point and the calculation results of the concentrations of each of the pollutants using the model formula corresponding to each measurement time point.

[0016] (7) A water quality simulation method according to (6) above, which predicts the concentration of each of the plurality of pollutants in the treated water at a later arbitrary time point from the value at an arbitrary time point.

[0017] (8) A water quality simulation method according to any one of (4) to (7) above, in which the water quality of the treated water after treatment is simulated based on a plurality of model formulas obtained by the fitting, the model formulas corresponding to parameters for which the value of the evaluation function used in the fitting is equal to or less than a predetermined value.

[0018] (9) The water quality simulation method according to any one of (1) to (8) above, wherein the treatment process is a fluidized carrier method.

[0019] (10) The water quality simulation method according to any one of (1) to (9) above, wherein the pollutants include at least one of thiocyanate, phenol, and thiosulfate.

[0020] (11) A water quality simulation device for a treatment process that biologically treats water to be treated, comprising: the water quality simulation device comprises a processor; The processor: A term representing the actual measured values ​​of the decomposition rates of a plurality of different pollutants obtained by adding a plurality of microbial groups that decompose each of the plurality of different pollutants into the water to be treated, and an interspecies competition term representing the interspecies competition between the plurality of microbial groups in the water to be treated. The term "microorganisms" indicates that the higher the density of any one of the microorganism groups, the greater the reduction in the other microorganism groups. a parameter fitting unit that fits parameters of the model formula based on calculated values ​​of decomposition rates of the plurality of pollutants calculated from a model formula having the formula: a water quality simulation unit that simulates the water quality of the treated water after treatment using the model formula to which the parameters have been fitted; A water quality simulation device comprising:

[0021] (12) A method for determining the number of carriers required to treat pollutants by a flowing carrier method using the water quality simulation method described in (9) above. [Effects of the Invention]

[0022] According to the present invention, it is possible to accurately simulate the water quality after treatment by taking into account the influence of interspecies competition between multiple microbial groups that decompose each of the multiple different pollutants contained in the water to be treated, and to accurately determine the number of carriers for treating pollutants using the flowing carrier method. [Brief explanation of the drawings]

[0023] [Figure 1] FIG. 1 is a schematic diagram showing a model according to the present embodiment. [Figure 2] FIG. 1 is a schematic diagram showing a model according to the present embodiment. [Figure 3] 3 is a flowchart showing a processing procedure of a wastewater quality simulation method according to the present embodiment. [Figure 4] 1 is a block diagram showing an example of the configuration of a wastewater quality simulation device according to an embodiment of the present invention; [Figure 5] FIG. 1 is a schematic diagram showing a biological treatment device. [Figure 6] FIG. 1 is a characteristic diagram showing the removal rate (decomposition rate) of thiocyanate per day of operation. [Figure 7] FIG. 1 is a characteristic diagram showing the removal rate (decomposition rate) of phenol per day of operation. [Figure 8] FIG. 1 is a characteristic diagram showing the removal rate (decomposition rate) of thiosulfate per day of operation. [Figure 9] FIG. 1 is a characteristic diagram showing the thiocyanate removal rate per day of operation. [Figure 10] FIG. 1 is a characteristic diagram showing the phenol removal rate per day of operation. [Figure 11] FIG. 1 is a characteristic diagram showing the thiosulfate removal rate per day of operation. [Figure 12] FIG. 1 is a schematic diagram showing a method for collecting a sponge carrier from a water treatment reactor, placing it in a beaker, and conducting a culture test to identify the autolysis rate (di). [Figure 13] FIG. 13 is a characteristic diagram showing the difference in thiocyanate concentration in the culture test shown in FIG. 12, which was carried out on each collection day. [Figure 14] FIG. 10 is a characteristic diagram showing the results of comparing calculated values ​​(Calculation) with measured values ​​(Experiment). [Figure 15] FIG. 10 is a characteristic diagram showing the results of comparing calculated values ​​(Calculation) with measured values ​​(Experiment). [Figure 16] FIG. 10 is a characteristic diagram showing the results of comparing calculated values ​​(Calculation) with measured values ​​(Experiment). [Figure 17] FIG. 10 is a characteristic diagram showing the results of comparing a toxic model with a non-toxic model. [Figure 18] FIG. 1 is a characteristic diagram showing the thiocyanate concentration obtained from experimental results in operation of a water treatment reactor using thiocyanate as a pollutant. [Figure 19] FIG. 10 is a characteristic diagram showing the actual measured value of thiocyanate concentration, the simulation results of thiocyanate concentration obtained by a non-steady state model taking into account time changes, and the simulation results of microorganism concentration. [Figure 20] FIG. 10 is a characteristic diagram showing the concentration of thiocyanate ions in treated water obtained from the results of monitoring thiocyanate ions. [Figure 21] FIG. 10 is a characteristic diagram showing the concentration of thiocyanate ions in treated water obtained from the results of monitoring thiocyanate ions. [Figure 22] FIG. 10 is a characteristic diagram showing both simulation results and actual measurement values ​​when the number of sponge carriers 21 is changed. [Figure 23] FIG. 19 is a diagram showing the results of a simulation based on the experimental results shown in FIG. 18, and is a characteristic diagram showing the results of a simulation when parameters are changed, in comparison with FIG. 19. [Figure 24] FIG. 20 is a characteristic diagram showing an example of a simulation of the results of another water treatment reactor using the same parameters as in FIG. 19, based on the experimental results shown in FIG. [Figure 25] FIG. 24 is a characteristic diagram showing an example of a simulation of the results of another water treatment reactor using the same parameters as in FIG. 23, based on the experimental results shown in FIG. [Figure 26] FIG. 20 is a characteristic diagram showing the results of shading the range between the maximum and minimum values ​​in each simulation result calculated using a set of 81 parameters obtained from the measurement data of FIG. 18. [Figure 27] FIG. 26 is a characteristic diagram showing the results of simulating the results of another water treatment reactor using all of the set of 81 parameters obtained from the measurement data of FIGS. 24 and 25. DETAILED DESCRIPTION OF THE INVENTION

[0024] Hereinafter, several embodiments of the present invention will be described with reference to the drawings. However, these descriptions are intended to be merely examples of preferred embodiments of the present invention and are not intended to limit the present invention to such specific embodiments.

[0025] In this embodiment, a model is constructed to simulate the decomposition of multiple pollutants contained in the water to be treated when the water is biologically treated using an aerobic fluidized bed. In order to decompose multiple pollutants contained in the water to be treated using an aerobic fluidized bed, multiple microbial groups that decompose these multiple pollutants are attached to the fluidized carriers.

[0026] These multiple microbial communities self-multiply while decomposing pollutants. Furthermore, these multiple microbial communities die due to self-decomposition. Therefore, when building a model, the self-proliferation and self-decomposition of the microbial communities are taken into account.

[0027] Furthermore, the inventors focused on the interspecific competition between these multiple microbial groups and discovered that the accuracy of the model could be improved by constructing a model that takes into account the impact of interspecific competition on the decomposition of each pollutant.

[0028] Furthermore, when the concentration of pollutants is high, the pollutants may act as toxic components that inhibit the growth of microorganisms or even kill them. The inventors have found that the accuracy of the model can be further improved by building a model that takes into account the effects of these toxic components on the decomposition of each pollutant.

[0029] FIG. 1 is a schematic diagram showing a model for simulating the decomposition of multiple pollutants according to this embodiment. As shown in FIG. 1, this embodiment uses coke wastewater as an example of the water to be treated. Thiocyanate contained in the coke wastewater is decomposed by a thiocyanate-decomposing microbial group S, phenol is decomposed by a phenol-decomposing microbial group P, and thiosulfate is decomposed by a thiosulfate-decomposing microbial group S2. The wastewater from which the pollutants have been decomposed is discharged as treated water. Note that, since there are multiple types of microorganisms that decompose thiocyanate, these microorganisms will be collectively referred to as a microbial group. Similarly, since there are multiple types of microorganisms that decompose phenol, these microorganisms will be collectively referred to as a microbial group, and since there are multiple types of microorganisms that decompose thiosulfate, these microorganisms will be collectively referred to as a microbial group.

[0030] When treating wastewater biologically using an aerobic fluidized bed, each microbial group attaches to the fluidized carrier. The inventors discovered that as each microbial group grows while decomposing their respective pollutants, they compete with each other on the fluidized carrier, resulting in a decrease in the number of microbial groups. They found that when the amount of each microbial group attached to the fluidized carrier is relatively small, the number of microbial groups dying due to competition is relatively small. However, when the amount of each microbial group attached to the fluidized carrier becomes saturated due to self-renewal and they can no longer attach, interspecies competition becomes severe, resulting in a decrease in the number of microbial groups.

[0031] For this reason, the inventors constructed a model in which the treatment microbial groups were separated into groups for each of the three pollutant components (phenol, thiocyanate, and thiosulfate) in coke oven wastewater shown in Figure 1, and also constructed a competitive system model shown in Figure 2, taking into account the competitive relationships between these microbial groups.

[0032] The constructed model includes multiple parameters that determine the characteristics of the model. The inventors performed parameter fitting so that the decomposition rates of each pollutant obtained from the model matched the decomposition rates of each pollutant obtained from experiments, and then applied the parameters obtained by parameter fitting to the model to complete the model.

[0033] Using the model thus completed, the post-treatment water quality of the coke oven wastewater, which is the water to be treated, can be calculated. Therefore, the concentration of each pollutant in the treated water can be predicted. Furthermore, because the model parameters are related to water quality, it is possible to control the post-treatment water quality by controlling the various physical quantities related to the parameters.

[0034] Furthermore, for example, there are no clear regulations regarding the amount of fluid carrier required for treatment; the standard value is 33% (v / v), and if the treatment capacity is insufficient, the amount is increased to 50%, 66%, etc. However, excessive increases in the amount can result in unnecessary increases in costs. In light of this situation, a model that can accurately predict treatment performance is needed, but such a model did not exist.

[0035] The model of this embodiment makes it possible to predict the change in water quality relative to the total surface area of ​​the carriers, and therefore to calculate the exact amount of carriers to be added to maintain the post-treatment water quality at a predetermined standard.

[0036] 3 is a flowchart showing the processing steps of the wastewater quality simulation method according to this embodiment. First, in step S10, a model formula is constructed to calculate the decomposition rates of multiple pollutants in the water to be treated. Next, in step S12, actual measured values ​​of the decomposition rates of multiple pollutants are obtained. Next, in step S14, parameters of the model formula are fitted based on the calculated values ​​of the decomposition rates using the model formula and the actually measured decomposition rates. Next, in step S16, the model formula with the fitted parameters is used to calculate the wastewater quality. After step S16, the processing ends.

[0037] FIG. 4 is a block diagram showing an example of the configuration of a wastewater quality simulation device according to this embodiment. More specifically, FIG. 4 is a schematic diagram showing functional blocks of a processor 100 included in the water quality simulation device. The processor 100 of the water quality simulation device includes a parameter fitting unit 110 that fits parameters of a model formula based on actual measurements of the decomposition rates of multiple pollutants in the wastewater and calculated values ​​of the decomposition rates using the model formula, and a water quality simulation unit 120 that simulates the water quality after treatment using the model formula to which the parameters have been fitted. Each of these units included in the processor 100 is a functional module implemented by, for example, a computer program running on the processor 100. In other words, the functional blocks of the processor 100 are composed of the processor 100 and a program (software) for operating the processor 100. The program may be stored in a memory included in the water quality simulation device or in a recording medium connected externally. Alternatively, each of these units included in the processor 100 may be a dedicated arithmetic circuit provided in the processor 100. [Example]

[0038] The present embodiment will be described in detail below based on examples in the order of [1] to

[13] below. [1] Operation of a water treatment reactor using thiocyanate as a pollutant [2] Operation of a water treatment reactor using phenol as a pollutant [3] Operation of a water treatment reactor using thiosulfate as a pollutant [4] Operation of a water treatment reactor using thiocyanate, phenol, and thiosulfate as pollutants [5] Building a competitive model (non-toxic) [7] Self-decomposition rate (d i ) Identification [8] Simplification of the Type 1 model by partial non-dimensionalization (toxicity) [9] Simplification of the three-species model by partial nondimensionalization (toxicity)

[10] Parameter fitting of the model equation

[11] Simulation results for the three-type model

[12] Comparison of toxic and non-toxic models (1 type)

[13] Use of model formulas

[14] Parameter fitting considering unsteady states

[15] Determination of the number of carriers by the flow carrier method

[16] Simulation method using multiple parameter sets

[0039] First, in [1] to [4], extraction of water treatment data for model analysis is described using treated water simulating coke oven wastewater (ammonia water). In [1] to [3], a water treatment reactor is operated and actual measured values ​​of water treatment data are extracted for each of the cases where thiocyanate is used as the pollutant, where phenol is used as the pollutant, and where thiosulfate is used as the pollutant. In [4], a water treatment reactor is operated and actual measured values ​​of water treatment data are extracted for the cases where thiocyanate, phenol, and thiosulfate are used as the pollutants. The actual measurement of water treatment data can be performed in the same manner as in Patent Document 1 mentioned above.

[0040] [1] Operation of a water treatment reactor using thiocyanate as a pollutant The solutes shown in Table 1 were dissolved in a solvent obtained by mixing industrial water and natural seawater in a volume ratio of 2:3, at the concentrations shown in Table 1, to prepare artificial wastewater (water to be treated).

[0041] [Table 1]

[0042] As shown in FIG. 5, an integrated biological treatment device 20 was prepared. The biological treatment zone 20a and the settling zone 20b were separated from each other by a partition wall 23 within a single tank, and the zones were connected below the partition wall 23. A 10 mm x 10 mm x 10 mm sponge carrier 21 (fluidized carrier (AQ-1 manufactured by Kanto Inoac Co., Ltd.)) and high-concentration activated sludge as a microbial inoculum were placed in a plastic bottle, kneaded thoroughly by hand, and then left to soak overnight with a lid on, allowing the microorganisms to adhere to the sponge carrier 21. The activated sludge added here is preferably an inoculum rich in thiocyanate-degrading microorganisms. However, since the activated sludge used to treat coke oven wastewater containing all of thiocyanate, thiosulfate, and phenol is used as the microbial inoculum, the activated sludge may be the same as the activated sludge added in steps [2] to [4] described below.

[0043] The sponge carriers 21 (500 pieces) and activated sludge prepared in this way were loaded into the biological treatment area 20a of the biological treatment device 20 to prepare the biological treatment device 20. The loading amount was determined to be approximately 20% of the apparent volume. Since the volume of the biological treatment area 20a was 3.4 L, the loading rate of the carriers was 14.7% (v / v), but the apparent volume was approximately 20%.

[0044] The water to be treated 24 was introduced into the biological treatment device 20 prepared in this manner, and activated sludge was added as a microbial inoculant source. During the first stage of treatment, the water to be treated 24 was introduced so that the hydraulic retention time of the water to be treated 24 was 24 hours. Furthermore, air aeration 22 was performed on the water to be treated 24 in each biological treatment device 20 to form an aerobic fluidized bed and acclimate the microorganisms. Furthermore, treatment was performed while adjusting the pH to around 7.5 using a 5 wt% sodium hydroxide aqueous solution. The treated water 26 was then discharged from the biological treatment device 20.

[0045] Thiocyanate ion monitoring was performed by measuring the thiocyanate ion concentration in the treated water in the biological treatment region 20a of each biological treatment device 20. The monitoring was performed approximately twice a week.

[0046] After this first stage treatment had stabilized, water to be treated 24 was introduced so that the amount of thiocyanate ions introduced per day reached the amount shown in Table 2. Each stage was operated for at least two weeks, and once it was confirmed that the fluctuations in the treated water concentration at each stage had stabilized, the system was moved to the next stage. Monitoring was carried out approximately twice a week.

[0047] [Table 2]

[0048] Figure 6 is a characteristic diagram showing the thiocyanate removal rate (decomposition rate) per day of operation, obtained from the results of thiocyanate ion monitoring. The thiocyanate removal rate is obtained from the amount of thiocyanate ions flowing in per day and the thiocyanate ion concentration of the treated water in the biological treatment zone 20a, obtained by monitoring. In other words, the thiocyanate removal rate is obtained from the difference between the amount of thiocyanate ions flowing into the biological treatment zone 20a and the amount of thiocyanate ions that were not completely treated in the biological treatment zone 20a (residual thiocyanate ions in the treated water).

[0049] [2] Operation of a water treatment reactor using phenol as a pollutant The solutes shown in Table 3 were dissolved in a solvent obtained by mixing industrial water and natural seawater in a volume ratio of 2:3 at the concentrations shown in Table 3 to prepare artificial wastewater (water to be treated).

[0050] [Table 3]

[0051] In addition, a sponge carrier 21 (fluid carrier (AQ-1 manufactured by Kanto Inoac)) measuring 10 mm x 10 mm x 10 mm and highly concentrated activated sludge as a microbial inoculum were placed in a plastic bottle, kneaded thoroughly by hand, and left to soak overnight with a lid on, thereby allowing the microorganisms to adhere to the sponge carrier 21. The activated sludge placed here is preferably an inoculum rich in phenol-decomposing microorganisms.

[0052] The sponge carriers 21 (500 pieces) and activated sludge prepared in this manner were placed in the biological treatment area 20 a of the biological treatment device 20 .

[0053] The water to be treated 24 was introduced into the biological treatment device 20 prepared in this manner, and activated sludge was added as a microbial inoculant source. During the first stage of treatment, the water to be treated 24 was introduced so that the hydraulic retention time of the water to be treated 24 was 24 hours. Furthermore, air aeration 22 was performed on the water to be treated 24 in each biological treatment device 20 to form an aerobic fluidized bed and allow the microorganisms to acclimate. Furthermore, treatment was performed while adjusting the pH to around 7.5 using a 5 wt% sodium hydroxide aqueous solution. The treated water 26 was then discharged from the biological treatment device 20.

[0054] The phenol concentration was monitored by measuring the phenol concentration in the treated water in the biological treatment area 20a of each biological treatment device 20. The monitoring was carried out about twice a week.

[0055] After this first stage treatment had stabilized, water to be treated 24 was introduced so that the amount of phenol inflow per day reached the amount shown in Table 4. Each stage was operated for at least two weeks, and once it was confirmed that the fluctuations in the treated water concentration at each stage had stabilized, the system was moved to the next stage. Monitoring was carried out approximately twice a week.

[0056] [Table 4]

[0057] Figure 7 is a characteristic diagram showing the phenol removal rate (decomposition rate) per day of operation obtained from the results of phenol monitoring. The phenol removal rate is obtained from the amount of phenol flowing in per day and the phenol concentration in the treated water in the biological treatment zone 20a obtained by monitoring. In other words, the phenol removal rate is obtained from the difference between the amount of phenol flowing into the biological treatment zone 20a and the amount of phenol that was not completely treated in the biological treatment zone 20a (residual phenol in the treated water).

[0058] [3] Operation of a water treatment reactor using thiosulfate as a pollutant The solutes shown in Table 5 were dissolved in a solvent obtained by mixing industrial water and natural seawater in a volume ratio of 2:3 at the concentrations shown in Table 5 to prepare artificial wastewater (water to be treated).

[0059] [Table 5]

[0060] In addition, a sponge carrier 21 (fluid carrier (AQ-1 manufactured by Kanto Inoac)) measuring 10 mm x 10 mm x 10 mm and high-concentration activated sludge as a microbial inoculum were placed in a plastic bottle, kneaded thoroughly by hand, and left to soak overnight with a lid on, thereby allowing the microorganisms to adhere to the sponge carrier 21. The activated sludge placed here is preferably an inoculum containing a large amount of thiosulfate-decomposing microorganisms.

[0061] The sponge carriers 21 (500 pieces) and activated sludge prepared in this manner were placed in the biological treatment area 20 a of the biological treatment device 20 .

[0062] The water to be treated 24 was introduced into the biological treatment device 20 prepared in this manner, and activated sludge was added as a microbial inoculant source. During the first stage of treatment, the water to be treated 24 was introduced so that the hydraulic retention time of the water to be treated 24 was 24 hours. Furthermore, air aeration 22 was performed on the water to be treated 24 in each biological treatment device 20 to form an aerobic fluidized bed and allow the microorganisms to acclimate. Furthermore, treatment was performed while adjusting the pH to around 7.5 using a 5 wt% sodium hydroxide aqueous solution. The treated water 26 was then discharged from the biological treatment device 20.

[0063] The thiosulfate ion concentration was monitored by measuring the thiosulfate ion concentration in the treated water in the biological treatment region 20a of each biological treatment device 20. The monitoring was carried out about twice a week.

[0064] After this first stage treatment had stabilized, water to be treated 24 was introduced so that the amount of thiosulfate ions introduced per day reached the amount shown in Table 6. Each stage was operated for at least two weeks, and once it was confirmed that the fluctuations in the concentration of treated water at each stage had stabilized, the system was moved to the next stage. Monitoring was carried out approximately twice a week.

[0065] [Table 6]

[0066] Figure 8 is a characteristic diagram showing the thiosulfate removal rate (decomposition rate) per day of operation, obtained from the results of thiosulfate monitoring. The thiosulfate removal rate is obtained from the amount of thiosulfate ions flowing in per day and the thiosulfate ion concentration in the treated water in the biological treatment zone 20a, obtained by monitoring. In other words, the thiosulfate removal rate is obtained from the difference between the amount of thiosulfate ions flowing into the biological treatment zone 20a and the amount of thiosulfate ions (thiosulfate ions in the treated water) that were not completely treated in the biological treatment zone 20a.

[0067] [4] Operation of a water treatment reactor using thiocyanate, phenol, and thiosulfate as pollutants In the above [1] to [3], one of thiocyanate, phenol, and thiosulfate was used as the pollutant, and a water treatment reactor was operated for each pollutant separately to extract water treatment data. Next, a water treatment reactor was operated using the three pollutants thiocyanate, phenol, and thiosulfate, and water treatment data was extracted.

[0068] The solutes shown in Table 7 were dissolved in a solvent obtained by mixing industrial water and natural seawater in a volume ratio of 2:3 at the concentrations shown in Table 7 to prepare artificial wastewater (water to be treated).

[0069] [Table 7]

[0070] In addition, a sponge carrier 21 (fluid carrier (AQ-1 manufactured by Kanto Inoac)) measuring 10 mm x 10 mm x 10 mm and highly concentrated activated sludge as a microbial inoculum were placed in a plastic bottle, kneaded thoroughly by hand, and left to soak overnight with a lid on, thereby allowing the microorganisms to adhere to the sponge carrier 21. The activated sludge placed here is preferably an inoculum rich in thiocyanide-decomposing microorganisms, phenol-decomposing microorganisms, and thiosulfate-decomposing microorganisms.

[0071] The sponge carriers 21 (500 pieces) and activated sludge prepared in this manner were placed in the biological treatment area 20a of the biological treatment device 20, and the biological treatment device 20 was prepared.

[0072] The water to be treated 24 was introduced into the biological treatment device 20 prepared in this manner, and activated sludge was added as a microbial inoculant source. During the first stage of treatment, the water to be treated 24 was introduced so that the hydraulic retention time of the water to be treated 24 was 24 hours. Furthermore, air aeration 22 was performed on the water to be treated 24 in each biological treatment device 20 to form an aerobic fluidized bed and allow the microorganisms to acclimate. Furthermore, treatment was performed while adjusting the pH to around 7.5 using a 5 wt% sodium hydroxide aqueous solution. The treated water 26 was then discharged from the biological treatment device 20.

[0073] The concentrations of thiocyanate ions, phenol, and thiosulfate ions were monitored by measuring the concentrations of thiocyanate ions, phenol, and thiosulfate ions in the treated water in the biological treatment region 20a of each biological treatment device 20. The monitoring was carried out about twice a week.

[0074] After this first stage treatment had stabilized, water to be treated 24 was introduced so that the amounts of thiocyanate ion, phenol, and thiosulfate ion introduced per day were as shown in Table 8. Each stage was operated for at least two weeks, and once it was confirmed that the fluctuations in the treated water concentrations at each stage had stabilized, the system was moved on to the next stage. Monitoring was carried out approximately twice a week.

[0075] [Table 8]

[0076] Fig. 9 is a characteristic diagram showing the thiocyanate ion removal rate per day of operation obtained from the results of monitoring thiocyanate ions in the treated water in the biological treatment zone 20a. Fig. 10 is a characteristic diagram showing the phenol removal rate per day of operation obtained from the results of monitoring phenol in the treated water in the biological treatment zone 20a. Fig. 11 is a characteristic diagram showing the thiosulfate removal rate per day of operation obtained from the results of monitoring thiosulfate in the treated water in the biological treatment zone 20a.

[0077] [5] Building a competitive model (non-toxic) The inventors considered three pollutant components in coke oven wastewater (phenol = P, thiocyanate = S, thiosulfate = S2) as shown in Figure 1, and separated the microorganisms attached to the fluidized carrier into three microbial groups that primarily treat each of the pollutant components. Taking into account the competitive relationships between these microbial groups, a competitive system model was constructed as shown in Figure 2. Because the treatment process is a fluidized carrier method, the constructed mathematical model describes the amount of microbial groups as the amount per unit surface area of ​​the carrier.

[0078] As shown in Figure 1, the thiocyanide contained in the coke wastewater is decomposed by the thiocyanide-decomposing microbial group S, the phenol contained in the coke wastewater is decomposed by the phenol-decomposing microbial group P, and the thiosulfate contained in the coke wastewater is decomposed by the thiosulfate-decomposing microbial group S2. Each microbial group grows by processing thiocyanide, phenol, and thiosulfate. Furthermore, each microbial group decreases due to interspecific competition, as shown in Figure 2. Furthermore, each microbial group decreases due to autolysis. For this reason, the changes in the thiocyanide-decomposing microbial group, phenol-decomposing microbial group, and thiosulfate microbial group are expressed by the following equations (1), (2), and (3), respectively.

[0079]

number

[0080] In each of equations (1), (2), and (3), the first term on the right-hand side is a term related to self-replication (self-replication term). The second term on the right-hand side is a term related to interspecific competition (interspecific competition term). The third term on the right-hand side is a term related to self-decomposition (self-decomposition term).

[0081] The self-renewal term is the density of the microbial population b i (i=S, P, S2) increases, so its sign is positive (+). On the other hand, the interspecific competition term increases as the density of the microbial community b i The higher the density of the microbial population, the stronger the interspecific competition, and the more the microbial population decreases, so the sign is negative (-). i The higher the value, the more the microbial population is reduced, so its sign is negative (-).

[0082] In the self-reproducing term, f i (c i ) (i=S, P, S2) is defined by the following equation (4): α i (i=S, P, S2) represents the growth rate. i represents the concentration of pollutants in wastewater, and c S is the thiocyanate concentration, and c P is the phenol concentration, and c S2 is the thiosulfate concentration. k i is the death rate per unit time due to overcrowding, K i is the reference microbial population density per unit area, β i is the concentration of pollutant that gives half the growth rate of the maximum growth rate.

[0083]

number

[0084] In the interspecific competition term, A ij (i,j=S,P,S2) represents the interspecific competition coefficient. According to the interspecific competition term, the higher the density of the other two competing microbial species, the lower the density b i The amount of change decreases.

[0085] Also, in the self-decomposition term, d i (i=S,P,S2) represents the autolysis rate. According to the autolysis term, the density of the microbial population b i The higher the density b i The autolysis term represents the natural death of a certain proportion of the microbial population due to autolysis.

[0086] The inventors also constructed a model for decomposing three pollutant components, expressed by the following equations (5), (6), and (7). In each of equations (5), (6), and (7), i (i=S, P, S2) represents the concentration of each pollutant, and c S is the concentration of thiathioic acid in wastewater, c S is the phenol concentration in wastewater, c S2 is the thiosulfate concentration in the wastewater.

[0087]

number

[0088] In each of equations (5), (6), and (7), the left side is the concentration of each pollutant component, c i is a value obtained by multiplying the amount of change in L by the volume V of the biological treatment device 20. In each of equations (5), (6), and (7), L is the surface area of ​​the sponge carrier 21.

[0089] In each of equations (5), (6), and (7), the first term on the right side is the pollutant decomposition reaction term, and h i (b i ) (i=S, P, S2) is expressed by the following equation (8). i (b i ) is the concentration of microbial population b i This means that it changes depending on

[0090]

number

[0091] In addition, in each of equations (5), (6), and (7), the second term on the right side is the inflow term of the water to be treated, which is a term related to the concentration of pollutants due to the inflow of the water to be treated 24 into the biological treatment device 30. P i (i=S, P, S2) is the inflow rate, which is defined as the reciprocal of the hydraulic retention time. Therefore, the product of the inflow rate and the hydraulic convection time is the amount of water to be treated 24 flowing into the biological treatment area 20a of the biological treatment device 20 per hour. i in (i=S, P, S2) is the inflow concentration of each pollutant (i-type pollutant). S in ,c P in ,c S2 in is a value calculated from the proportion of each pollutant in the inflowing water 24 to be treated.

[0092] [6] Simplification of the first-class model by partial non-dimensionalization (variable transformation) (no toxicity) Of the constructed models, the model for decomposing one type of pollutant, excluding the interspecies competition term, was partially non-dimensionalized, and equations (1) to (3) and (5) to (7) were simplified to the following equations (9) and (10).

[0093]

number

[0094] In equations (9) and (10), K i ,k i The total surface area of ​​the sponge carrier, L, and the volume of the biological treatment area, V, are made dimensionless. The details of the simplification are as follows. First, S, P, and S2 in equations (1) to (3) are replaced with i, and B i =(k i / α i *K i )*b i In addition, in equations (1) to (3), the interspecific competition term (second term) on the right side is removed. Furthermore, R i =α i *r i (L / V),Γ i=(k i / α i *K i )γ i By rearranging the equations, we obtain equations (9) and (10). This non-dimensionalization is achieved by using the density of the microbial community b i The concentration of pollutants, c i is the inflow concentration c i in is included in equation (10) as an experimental parameter, so it is not non-dimensionalized. Time is also not non-dimensionalized.

[0095] [7] Self-decomposition rate (d i ) Identification When determining the parameters of the model [5] or [6] through the experiments [1] to [4], it is difficult to distinguish between the growth and decomposition of microorganisms in a normal treatment system where both occur simultaneously. Therefore, the autolysis rate d i (i=S, P, S2) (autolysis rate) was determined based on experiments with different treatment systems.

[0096] In a water treatment reactor operated using thiocyanate as a pollutant (see [1]), artificial wastewater was prepared from the solutes listed in Table 1, excluding sodium thiocyanate, starting on day 197 to create starvation conditions for thiocyanate-degrading microorganisms. As shown in Figure 12, 30 sponge carriers 21 were sampled from the reactor on days 190, 197, 203, 206, 209, 210, and 215. They were placed in a beaker 30 containing 300 mL of artificial wastewater prepared with the composition listed in Table 1, and a 3-hour incubation test was conducted. The difference in thiocyanate concentration before and after the 3-hour incubation was calculated. Figure 13 is a characteristic diagram showing the difference in thiocyanate concentration in the incubation test conducted on each sampling day.

[0097] As a result, as shown in Figure 13, the concentration difference decreased after day 197 and remained constant after day 203. It is believed that the apparent concentration difference was observed after day 203 due to the dilution of the artificial wastewater by the solvent contained in the sponge carrier 21. That is, on days 190 and 197, the concentration difference increased due to dilution caused by the addition of the carrier to the beaker 30 and the decomposition of thiocyanate. On the other hand, as mentioned above, the concentration difference after day 203 is believed to be due solely to dilution. Here, day 197 was the day sodium thiocyanate was removed from the artificial wastewater, and thiocyanate may still have remained in the reactor. Therefore, it is believed that the thiocyanate-decomposing ability was lost over the five days from day 198 to day 203, i.e., the thiocyanate-decomposing microorganisms were completely wiped out.

[0098] In equation (9), the concentration of pollutant component (i) such as thiocyanate in the culture test, c i When is set to 0, equation (9) becomes the following equation (11).

[0099]

number

[0100] According to the model of equation (11), the microbial mass at the initial time B i (0), the amount of microorganisms at time t, B i (t) is expressed by the following equation (12).

[0101]

number

[0102] Equation (12) is i By solving for, we obtain the following equation (13).

[0103]

number

[0104] The unit time of the equation system is 1 hour, and according to the experimental results mentioned above, the microbial community is thought to have been wiped out in 5 days, or 120 hours, from the initial state. In the model equation, the microbial community is wiped out, that is, at a certain time t1, B i Since (t1) = 0 is not possible, if we consider the experimental results as the amount of microorganisms decreasing to about 1% of the initial time in 120 hours, below the observation limit, the autolysis rate d i is calculated as follows:

[0105]

number

[0106] From the above, the autolysis rate of thiocyanate-decomposing microorganisms d s The autolysis rate of phenol and thiosulfuric acid was estimated to be about 0.0384. i The above values ​​are used as the autolysis rates of phenol and thiosulfate, but the autolysis rates of phenol and thiosulfate may be calculated individually by the same method. The calculation of the autolysis rate described above is only an example, and instead, the autolysis coefficient proposed in a known activated sludge model may be used.

[0107] [8] Simplification of the Type 1 model by partial non-dimensionalization (toxicity) When the concentration of pollutants is high, the pollutants may act as toxic components and inhibit or kill the corresponding microorganisms. In particular, the remaining pollutants that cannot be completely treated by the microorganisms may be toxic. For this reason, as shown in the following equation (14), k i , K. i The term related to toxicity (the third term on the right side) was added to equation (9) where the total surface area L of the sponge carrier and the volume V of the biological treatment device 20 are non-dimensional. The newly added term related to toxicity is the concentration c i When is sufficiently small, it is close to 0, but when the concentration of pollutants c i When the concentration becomes large enough, the toxicity increases, suppressing the growth of the microbial population and even reducing the microbial population.

[0108]

number

[0109] [9] Simplification of the three-species model by partial nondimensionalization (toxicity) A model for wastewater containing all three pollutants (S: thiocyanate, P: phenol, S2: thiosulfate) was non-dimensionalized as shown in the following equations (15), (16), and (17). Equations (15) to (17) are obtained by adding an interspecies competition term to equation (9), non-dimensionalizing it in the same way as equation (14), separating it into three pollutants (i = S, P, S2), and adding a term related to toxicity (the third term on the right-hand side). After non-dimensionalization, B i =(k i / α i *K i )*b i In this case, in the interspecific competition term, ((α S *K S ) / k S )*((α P *K P ) / k P )*A 12 =A' 12 ,((α S *K S ) / k S )*((α S2 *K S2 ) / k S2 )*A 13 =A' 13 ,((α P *K P ) / k P )*((α S *K S ) / k S )*A 21 =A' 21 ,((α P *K P ) / k P )*((α S2 *K S2 ) / k S2 )*A 23 =A' 23 ,((α S2 *K S2 ) / k S2 )*((α S *K S ) / k S )*A31 =A' 31 ,((α S2 *K S2 ) / k S2 )*((α P *K P ) / k P )*A 32 =A' 32 , and replace it with. Equations (18) to (20) can be obtained by dividing equation (15) into three types of pollutants (i = S, P, S2).

[0110]

number

[0111]

[10] Parameter fitting of the model equation The parameters in equations (15) to (20) are found by fitting the constructed model to the results of a water treatment experiment using wastewater. That is, numerical calculations are performed on the model equations (15) to (20) according to the conditions of the water treatment experiment explained in [1] to [4], and the amount (density) of the microbial community and the concentration of pollutants at each time are calculated, and the decomposition rate of the pollutants is obtained, which is then compared with the results of the water treatment experiment. For the numerical calculations, well-known methods such as the Runge-Kutta method are used. When comparing the numerical calculations with the experimental results, it is more preferable to use a criterion based on relative error, since the decomposition rate at each time varies greatly. Note that at the initial time, the amount of the microbial community is calculated as b i (0) = 1, and the concentration of pollutants is c i (0) = 0. This is given in the hope that the state obtained from the model equation will quickly converge to a state where the decomposition of pollutant components is sufficiently carried out. Since the measured values ​​of the decomposition rate of each pollutant were obtained from the experiments described in [1]-[4], there is no need to fit these parameters.

[0112] [10.1] Details of parameter fitting Consider the case where a steady state can be achieved under each experimental condition. In this case, the subscript n corresponds to each experimental condition. That is, the sponge surface area L corresponds to the experimental condition n (1≦n≦N). n , inflow rate P n , inflow pollutant concentration c in n is determined and the sponge surface area is L n , the inflow rate is P n , the inflow pollutant concentration is c n in The numerical calculation is performed assuming that the measured value is c n When parameter fitting is performed in a steady state, the measured value c n is the average value of the measured values ​​under each experimental condition n. n may be the value when a steady state is reached under each experimental condition n. At this time, a numerical experiment is performed as follows, and the numerical experimental value c^ corresponding to the parameter λ is obtained. n Calculate (λ). Note that experimental condition n is a serial number that specifies each experimental condition, and N is the total number of experimental conditions. The experimental conditions here are defined by the hydraulic retention time, the concentration of the inflowing substance, and the total surface area of ​​the sponge, but some experimental conditions may provide the same conditions.

[0113] 1) The starting conditions ^b0(λ) and c^0(λ) are appropriately determined. Typically, these are given as ^b0(λ)=1 and c^0(λ)=0, with the expectation that the decomposition of pollutant components is sufficient. 2)^b n-1 (λ),c^ n-1 The numerical solution of the model equation with (λ) as the initial value is - b n (t;λ),c - n (t;λ). In this case, ^b n (λ),c^ n (λ) is defined as in the following equations (21) and (22).

[0114]

number

[0115] The mathematical expression is as above, but in practice, the numerical calculation is performed until it is deemed to have converged sufficiently numerically, and then it is stopped at that point and used as a substitute for the limit value. The above numerical experimental values ​​c^ n The cost function E(λ) is calculated using (λ). The cost function E(λ) is set appropriately, but in this example, the definition of the following equation (23) is used, utilizing the relative error of the decomposition rate.

[0116]

number

[0117] In the example, sampling was performed using the Metropolis-Hastings method with E(λ) as the likelihood function, and parameters that minimize E(λ) were searched for.

[0118] Among the parameters, α i ,β i ,ω i ,δ i ,R i ,Γ i ,n i ,m i ,q i(i = S, P, S2) are all parameters unrelated to the interspecies competition term. Therefore, these parameters can be determined by fitting the parameters so that the decomposition rate of each pollutant obtained from the results of water treatment experiments using wastewater containing one pollutant as described in [1] to [3] above matches the decomposition rate of each pollutant calculated from the model for one pollutant excluding the interspecies competition term (Equations (14) and (10)). Alternatively, these parameters can be determined by fitting the parameters so that the concentration of each pollutant obtained from the results of water treatment experiments using wastewater containing one pollutant as described in [1] to [3] above matches the concentration of each pollutant calculated from the model for one pollutant excluding the interspecies competition term (Equations (14) and (10)). Whether to fit the parameters to the decomposition rate of each pollutant or to the concentration of each pollutant can be selected appropriately depending on the water treatment process being designed. For example, fitting by decomposition rate is used when calculating the treatment capacity of a water treatment process, especially the maximum capacity. Also, for example, fitting by concentration is used when calculating the remaining (untreated) concentration of each pollutant and determining whether it meets the wastewater standards. Note that fitting to find parameters that are not related to the interspecies competition term is called first-stage fitting. Here, R i =α i *r i Since (L / V), R i Once r is found, i is obtained. Also, Γ i =(α i *k i / K i )γ i Therefore, Γ i Once γ i is obtained.

[0119] For example, if one of the pollutants is thiathione, and the decomposition rate of thiathione obtained from the water treatment experiment results in [1] above is used to calculate the parameter α S ,β S ,ω S ,δ S ,RS ,Γ S ,n S ,m S ,q S When fitting, in steps 1 to 8 shown in Table 2, parameter fitting is performed so that the decomposition rate of thiocyanate calculated from equations (14) and (10) matches the decomposition rate of thiocyanate obtained in the experiment shown in Figure 6.

[0120] Also, A' 12 ,A' 13 ,A' 21 ,A' 23 ,A' 31 ,A' 32 Each competitive coefficient can be calculated by fitting the results of the water treatment experiment using wastewater containing the three pollutants described in [4] above to the calculated values ​​of the model in equations (15) to (20) in the second stage of fitting. 12 ,A' 13 ,A' 21 ,A' 23 ,A' 31 ,A' 32 Since the parameters other than have already been calculated, the second stage of fitting is performed with these parameters incorporated into equations (15) to (20).

[0121] In this case, parameter fitting is performed in steps 1 to 9 shown in Table 8 so that the decomposition rate of each pollutant calculated from equations (15) to (20) matches the decomposition rate of each pollutant obtained in the experiments shown in Figures 9 to 11. Alternatively, parameter fitting is performed in steps 1 to 9 shown in Table 8 so that the concentration of each pollutant calculated from equations (15) to (20) matches the concentration of each pollutant obtained in the experiments shown in Figures 9 to 11.

[0122]

[11] Simulation results for the three-type model The results of the parameter determination using the above method are shown below. First, based on the actual measurement data obtained from the water treatment reactors operated in [1], [2] and [3] with one type of pollutant, thiocyanate, phenol and thiosulfate, parameter fitting was performed for the model (Equation (14), Equation (10)) constructed in [8] for one type of pollutant. The autolysis rate (d i ) was calculated using the values ​​obtained in [7]. As a result, the parameters were estimated as shown in Table 9 below. Note that the parameter n i , m i , q i was fitted within the range of natural numbers.

[0123] [Table 9-1] [Table 9-2]

[0124] Next, based on the data from the water treatment reactor operated in [4] with all three pollutants, thiocyanate, phenol, and thiosulfate, parameter fitting was performed using the parameters determined in the first stage of fitting for the coefficients of the interspecies competition terms in the model (Equations (15)-(20)) for the three pollutants constructed in [9]. As a result, the parameters were estimated as follows:

[0125] [Table 10]

[0126] Using the model [9] with the estimated parameters, the decomposition rates of each pollutant, i.e., thiocyanate (SCN), phenol (P), and thiosulfate (S2), were calculated and compared with the measured values ​​of the decomposition rates obtained in the experiment [4]. Figures 14 to 16 are characteristic diagrams showing the results of comparing the calculated values ​​(Calculation) with the measured values ​​(Experiment). Figure 14 shows the comparison results for thiocyanate, Figure 15 shows the comparison results for phenol, and Figure 16 shows the comparison results for thiosulfate. In Figures 14 to 16, the vertical axis of the upper graph indicates the decomposition rate, and the vertical axis of the lower graph indicates the ratio ((Calculation value - Measured value) / Measured value) obtained by subtracting the measured value from the calculated value and dividing it by the measured value. The results of Figures 14 to 16 demonstrate that the wastewater quality simulation method according to the present invention was able to precisely simulate the measured values.

[0127]

[12] Comparison of toxic and non-toxic models (1 type) Next, the model for one pollutant constructed in [6] and [8] was parameter-fitted based on the actual measurement data of the water treatment reactor operated in [1] using thiocyanate as the pollutant, and the simulation results were obtained and compared with the actual measurement values. i ) was calculated using the value obtained in [7].

[0128] In the toxic model, stages 1 to 9 shown in Table 2 in [1] above were set as Conditions 1 to 9, and parameter fitting was performed so that the water treatment rate calculated from Equation (14) and Equation (10) for each condition matched the decomposition rate obtained in the experiment shown in Figure 6.

[0129] In addition, in the non-toxic model, stages 1 to 9 shown in Table 2 in [1] above were set as Conditions 1 to 9, and parameter fitting was performed so that the water treatment rate calculated from equations (9) and (10) for each condition matched the decomposition rate obtained in the experiment shown in Figure 6.

[0130] Figure 17 is a characteristic diagram showing the results of comparing the toxic model and the non-toxic model. The upper part of Figure 17 shows the results of comparing the calculated values ​​and the measured values ​​for the non-toxic model. The lower part of Figure 17 shows the results of comparing the calculated values ​​(Calculation) and the measured values ​​(Experiment) for the toxic model. Again, the vertical axis of the left-hand graph shows the decomposition rate, and the vertical axis of the right-hand graph shows the ratio ((Calculation value - Actual value) / Actual value) obtained by subtracting the measured value from the calculated value and dividing it by the measured value. According to the results of Figure 17, the measured values ​​(Experiment) and the simulation results (Calculation) were significantly different for the non-toxic model, but by using a model that took toxicity into account, simulation results that were almost identical to the measured values ​​were obtained.

[0131]

[13] Use of model formulas Once the model of Equations (15) to (20) is completed as a result of parameter fitting, it becomes possible to simulate the decomposition rate of each pollutant, as shown in Figures 14 and 15. It also becomes possible to simulate the concentration of each microbial group.

[0132] Furthermore, according to equations (15) to (20), the inflow concentration of each pollutant c S in ,c P in ,c S2 in Based on the above, the concentration of each pollutant in the treated water 26 after decomposition treatment is S ,c P ,c S2 Therefore, it is possible to control the concentration of each pollutant in the treated water 26 to be limited to a desired value or less.

[0133] For example, in the formulas (15) to (20), as described above, R i =α i *r i (L / V). Therefore, the R obtained by fitting is i By changing the value of L, the concentration of each pollutant when these values ​​are changed, c S ,cP ,c S2 is obtained. As a result, when controlling the concentration of each pollutant in the treated water 26 to a desired value, the surface area L of the sponge carrier 21 required can be obtained, making it possible to add just the right amount of sponge carrier 21 according to the target concentration of each pollutant. This prevents costs from increasing due to the addition of an excessive amount of sponge carrier 21. Furthermore, by adding just the right amount of sponge carrier 21, the sponge carrier 21 is occupied by the necessary microorganisms, and it is also possible to eliminate unnecessary microorganisms.

[0134]

[14] Parameter fitting considering unsteady states In the above explanation, it was assumed that a steady state could be achieved under each experimental condition, and the average value of the measurement data at each stage was used as a representative value, which was taken as the daily removal rate. On the other hand, in the case of a model that solves a steady-state problem, it is possible to evaluate the treatment capacity in a stable state after a steady state has been reached under certain operating conditions, but because the actual increase and decrease of microorganisms involves a time element, it is preferable to use a model that can evaluate the treatment capacity in a non-steady state that takes time changes into account.

[0135] FIG. 18 is a characteristic diagram showing the thiocyanate concentration of treated water in the biological treatment area 20a obtained from experimental results during operation of the water treatment reactor described above in [1], using thiocyanate as a pollutant. Stages 1 to 8 in FIG. 18 correspond to stages 1 to 8 in Table 2, respectively. As shown in FIG. 18, even under the same experimental conditions (stages), the pollutant concentration changes from moment to moment, and the water quality data changes over time. For this reason, we constructed a model capable of simulating water quality in an unsteady state that takes time changes into account. A model capable of simulating water quality in an unsteady state makes it possible to determine the contaminant concentration, which changes over time. This allows us to obtain information such as, for example, how many days it will take for the concentration of pollutants remaining in the treated water to recover to a level that satisfies the requirements, or how many days it will take for the concentration of pollutants to deteriorate to a level that exceeds the requirements.

[0136] When treating the condition as steady state, the subscript n corresponds to each experimental condition. When treating the condition as unsteady state, the subscript n corresponds to each measurement. That is, the total sponge surface area L corresponds to measurement n (1≦n≦N). n , inflow rate P n , inflow pollutant concentration c n in is to be determined. Also, the measured value is n At this time, a numerical experiment is carried out as follows, and the numerical experimental value c^ corresponding to the parameter λ is n Calculate (λ).

[0137] 1) Appropriately set the starting conditions ^b(0;λ) and c^(0;λ). Typically, these are given as ^b(0;λ)=1, c^(0;λ)=0, with the expectation that pollutant components are being sufficiently decomposed. Unlike when treating the system as a steady state, when treating the system as a non-steady state, the starting conditions may affect the value of the cost function. Since estimating appropriate starting conditions increases the number of parameters that need to be estimated, in practice it is considered desirable to operate the system under conditions that allow sufficient decomposition of pollutant components only in the early stages until a steady state is reached.

[0138] 2) Time t n-1 In b(t n-1 )=^b(t n-1 ;λ),c(t n-1 )=c^(t n-1 ;λ), and the model equation is n The operating conditions are as follows: the sponge surface area is L n , the inflow rate is P n , the inflow pollutant concentration is c n in Numerical calculations are performed assuming that

[0139] The above numerical experimental values ​​c^ n The cost function E(λ) is calculated using (λ). The cost function E(λ) is set appropriately, but here the cost function E(λ) of the following equation (24) is used.

[0140]

number

[0141] In equation (24), ε is a parameter related to the measurement error, and in this embodiment, ε = 1.0. In addition, sampling is performed using the Metropolis method with E(λ) as the likelihood function to find the parameters that minimize E(λ).

[0142] Here, based on the experimental results shown in Figure 18, i.e., the experimental results of [1] mentioned above, we constructed a model that can simulate water quality in an unsteady state using thiocyanate as a pollutant. As a result of the first stage of parameter fitting, α S ≒0.0491, β S ≒0.0072, ω S ≒0.0115, R S ≒1.6453, γ S ≒0.292, δ S ≒0.3017, n S = 1, ms = 1, qs = 3. By applying the same method to the experimental results of [2] to [4] above, it is possible to construct a model capable of simulating water quality in an unsteady state for other pollutants, including the three types of pollutants. According to the constructed model, the total sponge surface area L n , inflow rate P n , inflow pollutant concentration c n in Depending on this, it becomes possible to predict the concentration of each of the multiple pollutants in the treated water at any later point in time from the value at any point in time.

[0143] Figure 19 is a characteristic diagram showing the actual measured values ​​of thiocyanate concentration based on the experimental results shown in Figure 18, and the simulation results of the thiocyanate concentration (shown by the solid line) obtained using a non-steady-state model that takes time changes into account. The vertical axis represents thiocyanate concentration, and the horizontal axis represents the number of days. As shown in Figure 19, the model capable of simulating water quality under non-steady-state conditions reveals that the thiocyanate concentration in the treated water rises sharply at treatment stage 7. This makes it possible to predict when treatment will deteriorate. Furthermore, as shown in Figure 19, the model capable of simulating water quality under non-steady-state conditions reveals that immediately after a change in treatment stage (e.g., immediately after switching from stage 3 to stage 4, or from stage 5 to stage 6), the proliferation of treating microorganisms cannot keep up, causing a temporary rise in the thiocyanate concentration in the treated water, resulting in a temporary deterioration in treatment.

[0144]

[15] Determination of the number of carriers by the flow carrier method Next, we will explain a method for determining the required number of carriers by determining model parameters from experiments in which the number of carriers was changed. Since the required number of carriers is obtained from simulations, the number of carriers can be optimized and excessive increase in the amount of carriers can be prevented.

[0145] To build the model, a water treatment reactor using thiocyanate as the pollutant was operated in the same manner as in [1] above, and then the number of carriers was changed and the water treatment reactor was operated again, and parameter fitting was performed from the measurement results. Here, based on the operation of this water treatment reactor, we explain the construction of a model capable of simulating water quality in an unsteady state when thiocyanate is used as the pollutant. By applying this model construction method to the experimental results of [1] to [4] above, it is possible to build a model capable of simulating water quality in an unsteady state for three types of pollutants.

[0146] First, as in [1] above, industrial water and natural seawater were mixed in a volume ratio of 2:3 to obtain a solvent, and the solutes shown in Table 11 below were dissolved in the concentrations shown in the table below to prepare artificial wastewater (water to be treated).

[0147] [Table 11]

[0148] As shown in Figure 5, an integrated biological treatment device 20 was prepared, in which a biological treatment area 20a and a sedimentation area 20b were separated from each other by a partition wall 23 within a single tank, and the areas were connected to each other below the partition wall 23. A sponge carrier 21 (fluid carrier (AQ-1 manufactured by Kanto Inoac)) measuring 10 mm x 10 mm x 10 mm and high-concentration activated sludge as a microbial inoculum source were placed in a plastic bottle, kneaded well by hand, and left to soak overnight with the lid on, thereby attaching the microorganisms to the sponge carrier 21.

[0149] The sponge carriers 21 (500 pieces) and activated sludge prepared in this manner were placed in the biological treatment area 20a of the biological treatment device 20 to prepare the biological treatment device 20. Since the volume of the biological treatment area 20a was 3.4 L, the carrier loading rate was 14.7% (v / v).

[0150] The water to be treated 24 was introduced into the biological treatment device 20 thus prepared, and activated sludge was added as a microbial inoculant source. During the first stage of treatment, the water to be treated 24 was introduced so that the hydraulic retention time of the water to be treated 24 was 48 hours. Furthermore, air aeration 22 was performed on the water to be treated 24 in each biological treatment device 20 to form an aerobic fluidized bed and acclimate the microorganisms. Furthermore, treatment was performed while adjusting the pH to around 7.5 using a 5 wt% sodium hydroxide aqueous solution. The treated water 26 was then discharged from the biological treatment device 20.

[0151] Thiocyanate ion monitoring was performed by measuring the thiocyanate ion concentration in the treated water in the biological treatment region 20a of each biological treatment device 20. The monitoring was performed approximately twice a week.

[0152] After this first stage treatment had stabilized, water to be treated 24 was introduced so that the amount of thiocyanate ions introduced per day reached the amount shown in Table 12 below. Each stage was operated for at least two weeks, and once it was confirmed that the fluctuations in the treated water concentration at each stage had stabilized, the system was moved to the next stage. Monitoring was carried out approximately twice a week.

[0153] [Table 12]

[0154] FIG. 20 is a characteristic diagram showing the concentration of thiocyanate ions in the treated water obtained from the results of monitoring thiocyanate ions.

[0155] After operating the water treatment reactor according to Table 12, 300 sponge carriers 21 were collected and placed in the biological treatment area 20a of a new biological treatment device 20 to prepare the biological treatment device 20. Note that, in order to perform a more precise experiment, assuming a case in which microorganisms that have peeled off from the sponge carriers 21 have accumulated on the bottom or microorganisms have adhered to the walls of the biological treatment device 20, 300 sponge carriers 21 were collected and placed in the biological treatment area 20a of the new biological treatment device 20; however, for simplicity, it is also possible to collect all of the sponge carriers 21 from the biological treatment device 20 and return the 300 sponge carriers 21 to the biological treatment area 20a of the same biological treatment device 20.

[0156] The water to be treated 24 was introduced into the biological treatment device 20 prepared in this manner so that the hydraulic residence time was 24 hours, and the amount of thiocyanate ions introduced per day was 460 mg SCN / L. The water to be treated 24 in each biological treatment device 20 was aerated 22, and the pH was adjusted to around 7.5 using a 5 wt% aqueous sodium hydroxide solution. The treated water 26 was then discharged from the biological treatment device 20.

[0157] After this first stage treatment had stabilized, sponge carriers 21 were removed so that the number of sponge carriers 21 was as shown in Table 13 below. Each stage was operated for at least two weeks, and once it was confirmed that the fluctuations in the treated water concentration at each stage had stabilized, the system was moved to the next stage. Monitoring was carried out approximately twice a week.

[0158] [Table 13]

[0159] Figure 21 is a characteristic diagram showing the thiocyanate ion concentration in the treated water obtained from the results of thiocyanate ion monitoring. When determining parameter values, parameter values ​​that minimize the cost function are selected, but when there are multiple parameter values ​​with similar cost function values, the selection can become difficult.

[0160] Here, we treated it as a non-steady state problem (with time changes) as explained in

[14] , determined the parameter values, and simulated the number of sponges required. Based on the measured data obtained from an operating water treatment reactor using thiocyanate as a pollutant, we performed parameter fitting for the model (Equation (14) and Equation (10)) for one type of pollutant constructed in [8]. The autolysis rate (d i ) was calculated using the values ​​obtained in [7]. As a result, the parameters were estimated as shown in Table 14 below. Note that the parameter n i , m i , q i was fitted within the range of natural numbers.

[0161] [Table 14]

[0162] FIG. 22 is a characteristic diagram showing both the simulation results and the actual measurement values ​​when the number of sponge carriers 21 is changed. In FIG. 22, the simulation results and the actual measurement values ​​in stages 1 to 3 shown in Table 13 are shown. According to the constructed model, the total sponge surface area Ln , inflow rate P n , inflow pollutant concentration c n in Depending on the total surface area of ​​the sponge, L n Since the parameters are determined taking into account the change in the total sponge surface area L n The concentration of thiocyanate can be calculated more accurately according to the change in the surface area of ​​each sponge. n The number of sponge carriers 21 is calculated from the above equation. As shown in Figure 22, the simulation results clearly show that the thiocyanate concentration in the treated water increases as the number of sponge carriers 21 decreases, which is consistent with the trend of the actual measurements. In particular, at the third stage (200 sponge carriers), the simulation results reveal that a relatively high concentration of thiocyanate remains in the treated water, making it impossible to operate the water treatment reactor under such conditions. On the other hand, at the second stage (250 sponge carriers), the simulation results show that the thiocyanate concentration is suppressed. Therefore, it can be seen that the minimum number of sponge carriers in the second stage (=250) is required to suppress the thiocyanate concentration. In this way, the required number of sponge carriers can be determined from the simulation results. Note that the microbial concentration shown in Figure 22 is expressed as a relative amount, with the maximum amount adhering to the surface of the sponge carrier 21 set to 1.

[0163]

[16] Simulation method using multiple parameter sets Generally, it is preferable to determine the optimum parameter values ​​for each target process as the model parameter values. This is because the optimum parameter values ​​often differ for each process. Figure 23 is a diagram showing the simulation results based on the experimental results shown in Figure 18, and is a characteristic diagram showing the simulation results when the parameters are changed, as compared with Figure 19. As mentioned above, the parameter values ​​in Figure 19 are α S ≒0.0491, β S ≒0.0072, ω S≒0.0115, R S ≒1.6453, γ S ≒0.292, δ S ≒0.3017, n S =1, ms=1, and qs=3. Figure 23 shows an example of simulating the experimental results shown in Figure 18 with parameters different from those in Figure 19, where the parameter values ​​are α S ≒0.0472, β S ≒0.0036, ω S ≒0.0101, R S ≒2.4694, γ S ≒0.0485, δ S ≒0.3694, n S = 1, ms = 1, and qs = 3. Although different parameter values ​​are used in Figures 19 and 23, both are similar to the experimental values ​​and provide good simulation results.

[0164] On the other hand, Fig. 24 is a characteristic diagram showing an example of a simulation of the results of another water treatment reactor using the same parameters as Fig. 19 based on the experimental results shown in Fig. 18. Similarly, Fig. 25 is a characteristic diagram showing an example of a simulation of the results of another water treatment reactor using the same parameters as Fig. 23 based on the experimental results shown in Fig. 18.

[0165] As shown in Figures 24 and 25, when parameter values ​​determined from the results of one water treatment reactor are used to simulate the results of another water treatment reactor, completely different simulation results may be obtained. In particular, in Figure 24, the simulation results show that almost no pollutant concentrations are detected in the treated water up to the fourth stage, meaning that stages 1 to 3 are good treatment, but in reality, the pollutant concentrations in the treated water increase in the second and third stages, and treatment deteriorates.

[0166] As described above, limiting the model formula parameters to one parameter can lead to overfitting, which can lead to results that are completely different from the actual measurement results when predicting pollutant components in other water treatment reactors.For this reason, we propose a method that enables predictions even when the target process changes by simultaneously applying simulation results based on multiple parameters.

[0167] First, let the set of parameters to be searched be Λ, and then determine an appropriate value of ε. Then, use the following equation (25) to find Λ. ε Define

[0168]

number

[0169] Next, to set the lower and upper limits of b and c so that the cost function (evaluation function) E(λ) in Equation (25) is equal to or smaller than ε, we use Λ ε For b as follows: min (t), b max (t),c min (t),c max Define (t).

[0170]

number

[0171] Next, the estimated value m of b(t) and c(t) is output as the following interval.

[0172]

number

[0173] Here, ε was set to 2.0, and parameters were sampled using the Metropolis method for the measurement data in Figure 18. Of the approximately 7,0000 sampled parameters, there were 81 parameter sets (combinations) that satisfied E(λ)<ε=2.0. Figure 26 is a characteristic diagram showing the results of simulations calculated using these 81 parameter sets, with the range between the maximum and minimum values ​​filled in.

[0174] Figure 27 shows the results of simulating the results of another water treatment reactor using all 81 parameter sets determined by sampling parameters using the Metropolis method on the measurement data of Figures 24 and 25 in a manner similar to that of Figure 26. Figure 27 also shows the results of simulating the results of another water treatment reactor using all 81 parameter sets determined by sampling parameters using the Metropolis method on the measurement data of Figures 24 and 25. The range between the maximum and minimum values ​​is filled in for each simulation result calculated using the 81 parameter sets. As shown in Figure 27, the results indicate that treatment begins to deteriorate from the second stage. This demonstrates that it is possible to predict deterioration in water treatment in another reactor by using multiple parameter values ​​determined in a water treatment reactor.

[0175] As described above, by simulating the water quality of the treated water based on multiple model equations obtained by fitting, which correspond to parameters for which the value of the evaluation function used in fitting is below a predetermined value, it is possible to predict deterioration of water treatment in a reactor other than the water treatment reactor from which actual measured values ​​were obtained. [Explanation of symbols]

[0176] 20 Biological treatment equipment 20a Biological Treatment Area 20b Subsidence area 21 Sponge carrier 22 Air aeration 23 Bulkhead 24 Untreated water 26 Treated water treated by biological treatment equipment

Claims

1. A water quality simulation method executed by a processor in a treatment process for biologically treating water to be treated, comprising: a model formula including a term representing interspecific competition among a plurality of different microbial groups that decompose each of a plurality of different pollutants contained in the water to be treated, the term representing that the higher the density of any one of the plurality of microbial groups, the greater the reduction of other microbial groups; and a simulation of the water quality after treatment by the plurality of microbial groups. The plurality of microbial groups are a first pollutant decomposing microbial group, a second pollutant decomposing microbial group, and a third pollutant decomposing microbial group, The water quality simulation method, wherein the model formula expresses the change per unit time of the decomposition microorganism group of the first pollutant by the following formula (1): [Equation 1] In equations (1) and (4), i = S, P, S2, where S represents the first pollutant, P represents the second pollutant, and S2 represents the third pollutant, and fi (ci) is defined by the following equation (4). In equations (1) and (4), bi represents the density of the decomposing microorganisms for each pollutant, αi represents the growth rate, and ci represents the concentration of the pollutant in the wastewater. Ki is the death rate per unit time due to overcrowding, Ki is the standard microbial population density per unit area, βi is the pollutant component concentration that gives a growth rate of 1 / 2 of the maximum growth rate, and ni is a power coefficient. In equation (1), A12 and A13 are interspecies competition coefficients, and di is the autolysis rate. [Equation 2]

2. 2. The water quality simulation method of claim 1, wherein the model formula further includes a term that indicates that, for each of the plurality of pollutants, the higher the concentration of the pollutant being decomposed, the smaller the change per unit time in the density of the microbial population that decomposes the pollutant due to toxicity.

3. 3. The water quality simulation method according to claim 1, further comprising: calculating the decomposition rate of each of the plurality of pollutants using the model formula; and fitting parameters representing the model formula based on the actual measured values ​​of the decomposition rates of the plurality of pollutants and the calculation results of the decomposition rates using the model formula.

4. 3. The water quality simulation method according to claim 1 or 2, wherein the concentration of each of the plurality of pollutants in the treated water is calculated using the model formula, and parameters representing the model formula are fitted based on the actual measured values ​​of the concentrations of each of the plurality of pollutants in the treated water when they reach a steady state and the calculation results of the concentrations of each of the pollutants using the model formula.

5. 3. The water quality simulation method according to claim 1 or 2, wherein the concentration of each of the plurality of pollutants in the treated water is calculated using the model formula, and parameters representing the model formula are fitted based on the actual measured values ​​of the concentrations of each of the plurality of pollutants in the treated water at each measurement point and the calculation results of the concentrations of each of the pollutants using the model formula corresponding to each measurement point.

6. The water quality simulation method according to claim 5 , wherein the concentration of each of the plurality of pollutants in the treated water is predicted from a value at an arbitrary time point to a value at a later arbitrary time point.

7. The water quality simulation method according to any one of claims 3 to 6, wherein the water quality after treatment of the treated water is simulated based on a plurality of model formulas obtained by the fitting, the model formulas corresponding to parameters for which the value of the evaluation function used in the fitting is equal to or less than a predetermined value.

8. The water quality simulation method according to any one of claims 1 to 7, wherein the treatment process is a fluidized carrier method.

9. 9. The water quality simulation method according to claim 1, wherein the pollutants include at least one of thiocyanate, phenol, and thiosulfate.

10. A water quality simulation device for a treatment process that biologically treats water to be treated, the water quality simulation device comprising: The processor: a parameter fitting unit that fits parameters of the model formula based on actual measurement values ​​of decomposition rates of a plurality of pollutants obtained by introducing a plurality of microbial groups that decompose each of the plurality of different pollutants into the water to be treated, and calculated values ​​of decomposition rates of a plurality of the pollutants calculated from a model formula having a term that represents interspecific competition between the plurality of microbial groups in the water to be treated, the term representing that the higher the density of any one of the plurality of microbial groups, the greater the decrease in other microbial groups; a water quality simulation unit that simulates the water quality of the treated water after treatment using the model formula to which the parameters have been fitted; Equipped with The plurality of microbial groups are a first pollutant decomposing microbial group, a second pollutant decomposing microbial group, and a third pollutant decomposing microbial group, The model formula expresses the change per unit time of the decomposition microorganism group of the first pollutant by the following formula (1): [Equation 3] In equations (1) and (4), i = S, P, S2, where S represents the first pollutant, P represents the second pollutant, and S2 represents the third pollutant, and fi (ci) is defined by the following equation (4). In equations (1) and (4), bi represents the density of the decomposing microorganisms for each pollutant, αi represents the growth rate, and ci represents the concentration of the pollutant in the wastewater. Ki is the death rate per unit time due to overcrowding, Ki is the standard microbial population density per unit area, βi is the pollutant concentration that gives a growth rate of 1 / 2 of the maximum growth rate, and ni is a power coefficient. In equation (1), A12 and A13 are interspecies competition coefficients, and di is the autolysis rate. [Equation 4]

11. A method for determining the number of carriers required to treat pollutants by a flowing carrier method, using the water quality simulation method according to claim 8.

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