Biological treatment of wastewater

Abstract

To provide a biological treatment method for waste water capable of stably performing biological treatment using a database which accumulates functional information on microorganisms and genes.SOLUTION: In a biological treatment method for waste water, a database of treatment performance index and microorganism species and / or genetic species involved in the variation of the treatment performance index (hereinafter referred to as indicator organism species and / or indicator genetic species) stored in association with each other is constructed, time-series data on bacterial flora and / or gene organization in biological sludge is acquired, and on the basis of the time-series data on bacterial flora and / or gene organization and the database, the increase or decrease of the indicator organism species and / or the indicator genetic species in the biological sludge is calculated. Based on this calculated value, the variation of the treatment performance indicator is estimated.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a method for biologically treating wastewater. [Background technology]

[0002] When biologically treating wastewater, stable operation is usually achieved by controlling the raw water quality, treated water quality, and operating conditions to maintain them within a specified range according to the biological treatment method. However, even if the above-mentioned operational control is performed appropriately, the treatment performance may decrease due to fluctuations in raw water load, the organic matter composition in the raw water, and the amount and properties of microorganisms held in the reaction tank. In such cases, the decrease in treatment performance is usually detected by sensors or water quality analysis, or visually confirmed by on-site workers, and countermeasures are generally taken.

[0003] However, in anaerobic treatment such as methane fermentation, once the treatment performance is degraded, it generally takes a long time to recover. In addition, there is a concern that it will take time to identify the cause of the degradation in treatment performance, which may delay the implementation of countermeasures. For these reasons, there is a demand for preventing performance degradation in biological treatment of wastewater, especially anaerobic treatment, and for quickly estimating the cause of the degradation.

[0004] Patent document 1 discloses a technology in which the floating tendency of granular sludge is determined based on the distribution of methanogens and eubacteria inside the granular sludge, and if the floating tendency is determined to be high, the technology is addressed by adjusting the retention time and wastewater temperature.

[0005] Patent Document 2 discloses a technique for determining anomalies in wastewater treatment capacity by observing the rRNA / rDNA ratio and changes in the number of rRNA bands in activated sludge using the DGGE method or the TGGE method.

[0006] However, microbial flora analysis that focuses only on higher classification levels as in Patent Document 1, or indicators that do not include microbiological or biochemical information as in Patent Document 2, has been difficult to diagnose various fluctuations and abnormalities in wastewater treatment. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] JP 2001-104988 A [Patent Document 2] JP 2018-121528 A Summary of the Invention [Problem to be solved by the invention]

[0008] An object of the present invention is to provide a method for biological treatment of wastewater, which can stably carry out biological treatment by using a database that accumulates functional information on microorganisms and genes. [Means for solving the problem]

[0009] The biological treatment method of the present invention is a method for biologically treating wastewater, which comprises determining a treatment performance index of the biological treatment and a microbial species or gene species (hereinafter referred to as "indicator") involved in the fluctuation of the treatment performance index. slight A database is constructed in which the biological sludge is associated with the biological species or indicator gene species, the bacterial flora or gene composition of the biological sludge is measured to obtain time-series data, and the indicator gene species in the biological sludge is determined from the time-series data of the bacterial flora or gene composition and the database. slight The increase or decrease in the biological species or indicator gene species is calculated, and the fluctuation in the processing performance index is estimated based on this calculated value.

[0010] In one aspect of the present invention, the possibility of a fluctuation in a performance index to a level that leads to a deterioration in treatment performance is predicted from the amount of fluctuation in the indicator microbial species or indicator gene species.

[0011] In one aspect of the present invention, the increase or decrease of indicator microbial species or indicator gene species is determined by amplicon sequencing, shotgun sequencing, or the like. , IssueThe rate, rate of change, or amount of change is calculated based on either current analysis or electrophoresis, using the number of reads of a sequence characteristic of the microbial species or gene species, or detection intensity as an alternative thereto.

[0012] In one aspect of the present invention, the treatment performance index is a propionic acid concentration, slight The biological species or indicator gene species is a microorganism having a propionic acid producing function or a propionic acid assimilation function. Effect of the Invention

[0013] According to the present invention, by estimating or predicting fluctuations in the performance indexes of biological treatment using data on the bacterial flora or genetic composition, it becomes possible to stably manage the operation of biological treatment. [Brief description of the drawings]

[0014] [Figure 1] FIG. 2 is a configuration diagram of a test device. [Diagram 2] 13 is a graph showing experimental results. [Diagram 3] 13 is a graph showing experimental results. [Figure 4] FIG. 2 is a functional block diagram of the analysis device. [Diagram 5] FIG. 1A shows an example of microbial function information, and FIG. 1B shows an example of gene function information. [Figure 6] FIG. 1 is a diagram showing an example of a record of operating conditions and water quality of a water treatment system. [Figure 7] 13(a) and 13(b) are diagrams showing an example of associating microorganisms and genes constituting sludge with functional information. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] Hereinafter, an embodiment of the present invention will be described.

[0016] The biological treatment method of the present invention is a method for biologically treating wastewater, which comprises constructing a database in which a treatment performance index and a microbial species and / or a gene species (hereinafter referred to as an indicator microbial species and / or an indicator gene species) involved in fluctuations in the treatment performance index are associated with each other and stored, acquiring time-series data on the bacterial flora and / or the gene composition of the biological sludge, At least one of the bacterial flora and genetic composition of the biological sludge ( Microbial flora and / or genetic composition ) The increase or decrease in the indicator microbial species and / or indicator gene species in the biological sludge is calculated from the time series data and the database, and the fluctuation in the treatment performance index is estimated based on the calculated value.

[0017] In the present invention, a sequencer and an analytical device are used to analyze the bacterial flora that constitutes the biological treatment sludge produced during the biological treatment of wastewater, i.e., the abundance ratio (composition ratio) of microorganisms in the sludge, and the abundance ratio of genes in the sludge.

[0018] The procedure for sludge analysis in the present invention is as follows.

[0019] (1) Sample the target sludge. (2) Genomic DNA is extracted from the sample sludge, and base sequence data is obtained using a next-generation sequencer or other device. (3) Each partial sequence in the base sequence data in (2) above is assigned to a corresponding microbial species or gene species using an external database, etc., and each assigned microbial species or gene species is compiled, thereby obtaining microbial flora data or gene composition data as the correspondence between each microbial species or gene species and its abundance ratio. (4) To the microbial species of the microbial flora data or the gene species of the gene composition data in (3) above, corresponding functional information from a microbial database created in advance (a database of functional information of microorganisms corresponding to microbial species and functional information of genes corresponding to gene species) is assigned, and integrated microbial flora data or integrated gene composition data is obtained as the correspondence between microbial species or gene species, abundance ratio, and functional information. (5) The above processes (1) to (4) are performed periodically to obtain time-series data of the integrated microbiota data or the integrated gene composition data. (6) Extracting microbial species or gene species having a predetermined function from the time-series data of the integrated microbial flora data or integrated gene composition data in (5) above. (7) For the time series data of the integrated microbial flora data in (6) above, calculate the increase or decrease in the abundance ratio of the microbial species in the group with the highest abundance ratio or the groups with the highest abundance ratios. Alternatively, for the time series data of the integrated gene composition data in (6) above, calculate the increase or decrease in the abundance ratio of some or all of the gene species. (8) Based on the calculated values ​​in (7) above, estimate the fluctuations in the treatment performance index of the biological treatment.

[0020] Next, each step will be described.

[0021] Steps (1) and (2) will now be described.

[0022] For example, biological sludge is sampled from the biological treatment reactor of a wastewater treatment facility, and DNA is extracted from the biological sludge. The extracted DNA is amplified, purified, fragmented, and adapter-linked as pretreatment, and then fed to a sequencer. The sequencer analyzes the DNA and outputs the base sequence data to an analysis device.

[0023] Here, the biologically treated sludge in the present invention is not limited to suspended sludge, but also includes microbial granules and non-biological carriers with attached biofilms. In the case of fixed bed carriers, the biofilms are partially removed from the carriers before the operation.

[0024] Step (3) will now be described.

[0025] External nucleic acid sequence databases include, for example, RefSeq, Subsystems, KEGG orthology (KO), COG, NOG, Silva, Greengenes, MDP, MiDAS, etc.

[0026] The means for calculating the composition ratio of microorganisms and genes is preferably, but not limited to, metagenomic analysis by shotgun sequencing or amplicon sequencing. For example, when limiting to a specific bacterial species, DGGE, real-time PCR, or other detection techniques may be used, and when limiting to a specific gene, real-time PCR may be used. When measuring the expression level of a gene, RNA-seq is preferable, but when measuring the expression level of a specific gene, RT-PCR, DNA microarrays, etc. may be used.

[0027] Step (4) will now be described.

[0028] Advances in analytical techniques such as metagenomics have made it possible to obtain comprehensive information on the composition of microorganisms and genes in samples, but it has been difficult to quickly extract information on the microorganisms and genes involved in a phenomenon of interest. Therefore, we will build a database that consolidates information on the properties and functions of each microorganism and gene.

[0029] As the microbial and gene function information, functional information of various microorganisms and functional information of various genes, as shown in FIG. 5, are stored.

[0030] For example, as shown in Figure 5(a), functional information of a microorganism includes the domain of the type of microorganism, the name of the microbial phylum, the name of the microbial class, the name of the microbial order, the name of the microbial family, the name of the microbial species, the name of the microorganism, growth conditions (temperature, pH, salt concentration, etc.), information on substances utilized (carbon source, energy source, final electron acceptor, etc.), information on enzymes possessed, information on products, information on growth inhibition conditions, etc.

[0031] As shown in Figure 5(b), functional information of a gene includes the gene name (including aliases), enzyme name and enzyme number (in the case of an enzyme gene), reactant information, product information, and reaction direction (presence or absence of a reverse reaction), etc.

[0032] Step (5) will now be described.

[0033] Sampling of the biological treatment sludge and DNA analysis are performed periodically, so the sequencer periodically outputs base sequence data to an analysis device.

[0034] Step (6) will now be described.

[0035] The number of microbial species or gene species that have functions that affect the treatment performance index of biological treatment (for example, inhibitor production function or assimilation function) is counted.

[0036] For example, methane fermentation will be taken as an example of biological treatment and its relationship with propionic acid will be explained. Accumulation of propionic acid inhibits methane fermentation, and is therefore an indicator of treatment performance. Once accumulation begins, recovery (restoration of biological activity by removing propionic acid) takes time, leading to loss of profits due to load reductions and shutdowns during that time. Therefore, if it is possible to predict the start of propionic acid accumulation before it progresses, it will be possible to prevent propionic acid accumulation by taking measures such as changing operating conditions at an early stage.

[0037] Step (7) will now be described.

[0038] Even if the targets for detecting and assessing the degree of fluctuation are limited to microbial species or genes whose composition ratio is equal to or greater than a certain ratio, it is possible to estimate the fluctuation tendency of the treatment performance index of biological treatment.

[0039] Step (8) will now be described.

[0040] We will now explain how to use the bacterial flora data and the gene composition data in the sludge analysis procedure described above.

[0041] When a gene of interest catalyzes a reaction in only one specific direction, the abundance ratio of bacteria that have the function of that gene is reflected in the gene composition data, so predictions can be made using only the gene composition data without using the bacterial flora data.

[0042] On the other hand, when a gene of interest catalyzes both forward and reverse reactions, it is difficult to determine which reaction direction is more dominant from gene composition data alone, so gene composition data does not necessarily contribute to prediction. Therefore, in this case, bacterial flora data is used.

[0043] However, if the microbial species with the function of interest is unknown and the gene responsible for that function is known and does not have a reverse reaction, the function cannot be assigned to the unknown bacterium. Therefore, in this case, only gene composition data is used.

[0044] The analysis device according to the present invention will now be described with reference to the drawings.

[0045] 4, the analysis device 1 is a computer equipped with a memory unit 10 having a microorganism / gene function information memory area 10A, a composition data memory area 10B, and a countermeasure information memory area 10C, and a CPU (Central Processing Unit, not shown). An analysis program is stored in the memory unit 10. A hard disk, a flash memory, or the like can be used as the memory unit 10.

[0046] By the CPU executing the analysis program stored in the memory unit 10, the functions of a base sequence data acquisition unit 11, a metagenomic analysis unit 12, a functional information extraction unit 13, a variation detection unit 14, a prediction unit 15 and a countermeasure presentation unit 16 are realized, as shown in Figure 4.

[0047] The base sequence data acquisition unit 11 periodically acquires base sequence data from the sequencer.

[0048] The base sequence data obtained from the sequencer is subjected to appropriate pre-processing and annotation in the metagenomics analysis unit 12, whereby various microorganisms and genes are counted and the types and composition ratios of the microorganisms and genes are calculated. At least a part of the functions of the metagenomics analysis unit 12 may use the functions of an external database.

[0049] As described above, since the base sequence data acquisition unit 11 periodically acquires base sequence data from the sequencer, the metagenomics analysis unit 12 periodically calculates the types and composition ratios of microorganisms and genes. The calculated composition ratios are stored in the composition data storage area 10B.

[0050] The composition data memory area 10B may store composition ratios obtained by analyzing biological treatment sludge sampled from a biological treatment reaction tank installed in the same wastewater treatment facility and operating in parallel, or biological treatment sludge sampled from a biological treatment reaction tank of a water treatment system with the same configuration installed in another facility.

[0051] In addition, information such as the raw water quality, operating conditions (temperature, time, etc.), and treated water quality in a water treatment system including a biological treatment reaction tank from which biological treatment sludge is sampled, as shown in Figure 6, may be obtained continuously or periodically, and stored in the composition data memory area 10B in correspondence with the calculated composition ratio.

[0052] The functional information extraction unit 13 extracts functional information of microorganisms and genes contained in the analysis results of the metagenomic analysis unit 12 from the microorganism / gene functional information storage area 10A. Here, the microorganism / gene functional information storage area 10A corresponds to the "preliminarily created microorganism database" in step (4) of the above-mentioned sludge analysis procedure. The functional information extraction unit 13 may output a list in which the extracted functional information is associated with each microorganism and each gene, as shown in Figures 7(a) and (b).

[0053] The increase or decrease in microbial species or gene species can be calculated as the degree of variation (rate of change or coefficient of variation) for each microorganism and each gene based on the latest composition ratio and past composition ratio (past composition ratios at other sites may also be used).

[0054] When the degree of fluctuation in the composition of microorganisms or genes is less than a predetermined value, it is determined that stable operation is continuing without any significant fluctuation in biological treatment.

[0055] If the degree of fluctuation is equal to or greater than a specified value, it is determined that a problem has already occurred in biological treatment or that there is a possibility that a problem may occur in the future.In this case, the microorganisms or genes with large fluctuations in their abundance ratios are identified, and the possible effects of increases or decreases in factors that affect wastewater treatment (for example, in the case of methane fermentation, a decrease in methanogens, an increase in propionate-producing bacteria and assimilating bacteria, an increase in microorganisms that use chlorine oxide as an electron acceptor, accumulation of inhibitory substances, etc.) are predicted based on functional information on those microorganisms or genes.

[0056] In the countermeasure information storage area 10C, several patterns of changes in the bacterial flora and gene composition are defined, and countermeasure information corresponding to the patterns is registered. For example, patterns of changes that occurred in the past at the same site or other sites and the countermeasures taken at that time are registered as countermeasure information. However, considering the accuracy rate of predictions and countermeasures, it is desirable to construct the countermeasure information as a database that refers to the same site or sludge, or at least sludge with the same treatment method.

[0057] The countermeasure presentation unit 16 refers to the countermeasure information registered in the countermeasure information storage area 10C and presents a countermeasure corresponding to the change in composition.

[0058] By using this analysis system, when the composition ratio of microorganisms and genes that make up biological sludge changes significantly, it is possible to refer to the functional information of the microorganisms and genes whose abundance ratios change significantly and predict the impact that may occur in the water treatment system. By taking measures based on the predictions, it is possible to support the stable operation of biological treatment.

[0059] The actual measured values ​​of the raw water quality, treated water quality, operating conditions, etc. of the water treatment system (see FIG. 6) are compared with the past measured values, and when the rate of change is equal to or greater than a predetermined value, the functional information corresponding to the measurement item whose rate of change is equal to or greater than the predetermined value is identified, and the microorganism or gene having this functional information is identified. The identified microorganism or gene can be used to predict possible effects on the water treatment system.

[0060] <Application to other biological treatments> In the examples below, the inhibition of methane fermentation reaction and the accumulation of propionic acid are related, and the present invention is preferably used. However, the present invention is not limited to this, and can be used in particular for reactions that take a long time to recover from a decrease in performance. For example, the present invention can be used to monitor a decrease in processing capacity due to a decrease in hydrolytic bacteria in methane fermentation, or an increase in denitrifying bacteria that compete with anammox bacteria in an anammox reaction. EXAMPLES

[0061] An artificial substrate with the following composition, mainly composed of sugar, was anaerobically treated using a treatment device with a methane fermentation tank of approximately 10 L capacity as shown in Figure 1. The seed sludge used was sludge from an anaerobic digestion test of artificial food waste. Glucose 2500mg-COD / L Ammonium chloride 303mg / L Disodium hydrogen phosphate 37.4mg / L Potassium dihydrogen phosphate 13.9mg / L Trace metals 10ppm Yeast extract 40.5mg-COD / L

[0062] The artificial substrate was fed at a rate of 5 mL / min, and the pH of the fermenter was maintained at 6.8 to 7.4 using 2N NaOH.

[0063] In order to accumulate propionic acid in the fermentation tank, water was continuously passed through the fermentation tank under overload conditions with the HRT shortened to 10 to 20 days, which is shorter than the HRT for general anaerobic digestion, and data on water quality and microbial flora was collected.

[0064] <Bacteria flora analysis> DNA extraction was performed from approximately 400 μL of sample sludge using the FastDNA SPIN Kit for Soil (MP Biomedicals, Irvine, CA) according to the manufacturer's instructions. Library preparation was performed using Nextera DNA Flex Library Prep and Nextera DNA CD indexes according to the manufacturer's instructions. Shotgun sequencing was performed on an Illumina MiSeq using Reagent Kit v3.

[0065] The obtained sequence data MG-RAST: metagenomics analysis server (https: / / www.mg-rast.org / ) Glass EM, Wilkening J., Wilke A., Antonopoulos D., Meyer F.: Using the metagenomics RAST server (MG-RAST) for analyzing shotgun metagenomes., Cold Spring Harb Protoc., 2010(1), 1-10 (2010) Bacterial flora analysis was performed using the method.

[0066] Here, the forward and reverse fastq file format single-read data files output from Miseq were converted to pair-end reads, low-quality sequences (Phred quality score below 15) were trimmed, and the data were submitted to the pipeline.

[0067] The reference sequence used for bacterial flora analysis is Refseq : NCBI Reference Sequence Database (https: / / www.ncbi.nlm.nih.gov / refseq / ) O'Leary NA, Wright MW, Brister JR, Ciufo S, Haddad D, McVeigh R, Rajput B, Robbertse B, Smith-White B, Ako-Adjei D, Astashyn A, Badretdin A, Bao Y, Blinkova O, Brover V, Chetvernin V, Choi J, Cox E, Ermolaeva O, Farrell CM, Goldfarb T, Gupta T, Haft D, Hatcher E, Hlavina W, Joardar VS, Kodali VK, Li W, Maglott D, Masterson P, McGarvey KM, Murphy MR, O'Neill K, Pujar S, Rangwala SH, Rausch D, Riddick LD, Schoch C, Shkeda A, Storz SS, Sun H, Thibaud-Nissen F, Tolstoy I, Tully RE, Vatsan AR, Wallin C, Webb D, Wu W, Landrum MJ, Kimchi A, Tatusova T, DiCuccio M, Kitts P, Murphy TD, Pruitt KD. The reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 2016 Jan 4;44(D1):D733-45 PubMed Thanks for watching the rest of the game Thanks for reading the book.

[0068] [Specifications and entries(i)] Figure 2 shows the time-dependent changes in influent and removal load, the propionic acid concentration in the treated water, the ratio of the number of reads from all propionic acid-producing bacteria to the number of reads from all propionic acid-utilizing bacteria based on the database (Ratio 1), and the ratio of the number of reads from the propionic acid-producing bacteria Propionibacterium to the number of reads from the propionic acid-utilizing bacteria Candidatus Cloacamonas (Ratio 2).

[0069] For the first 3 days, HRT was set at 10 days. As shown in Figure 2, accumulation of propionic acid occurred immediately.

[0070] From the 4th day, propionic acid was consumed when the HRT was lowered to 20 days, and no abnormalities were observed in the operating performance or water quality until the 33rd day.

[0071] From day 34 to day 39, the HRT load was increased by shortening it to 15 days and then 10 days. As a result, propionic acid began to accumulate from day 39.

[0072] Ratios 1 and 2 remained constant at around 2 during the period when the HRT was 20 days and the treatment performance was stable (from day 3 to day 33), but when the HRT was shortened and the treatment was overloaded from day 34 to day 39, the ratios rose sharply to 3 or more before propionic acid appeared in the water quality.

[0073] Figure 3A shows the genus-level composition of the propionic acid-producing bacteria that make up Ratio 1 in Figure 2. Figure 3B shows the genus-level composition of the propionic acid-utilizing bacteria that make up Ratio 1 in Figure 2.

[0074] The outline of the procedure for creating Figures 3A and 3B is given below. (1) Sample the target sludge. (2) Genomic DNA is extracted from the sample sludge, and base sequence data is obtained using a next-generation sequencer or other device. (3) Each partial sequence in the base sequence data in (2) above is assigned to a corresponding microbial species using RefSeq as an external database, and each assigned microbial species is compiled to obtain microbial flora data as the correspondence between each microbial species and its abundance ratio. (4) To the microbial species in the microbial flora data in (3) above, functional information of the corresponding microorganisms in a microbial database created in advance is assigned, and integrated microbial flora data is obtained as the correspondence between microbial species, abundance ratio, and functional information. (5) The above processes (1) to (4) are carried out on the first day of operation, 1 day later, 19 days later, 33 days later, 38 days later, 39 days later, 40 days later, and 44 days later, and time series data of the integrated bacterial flora data is obtained. (6) Microbial species or gene species having propionic acid production or propionic acid assimilation function are extracted from the time-series data of the integrated microbial flora data in (5) above. (7) For the time-series data of the integrated microbial flora data in (6) above, the increase or decrease in the microbial species of the top several groups with the highest abundance ratios of microbial species is calculated. (8) Based on the calculated values ​​in (7) above, estimate the fluctuations in the treatment performance index of the biological treatment.

[0075] In Figure 3A, the genus Propionibacterium increased in particular, and its abundance ratio increased further as the load increased. In Figure 3B, the dominant genus Candidatus and Cloacamonas decreased on the 19th day, but almost recovered by the 33rd day, and then tended to decrease with overload.

[0076] [Consideration (ii)] As shown in Figure 2, Ratio 1 and Ratio 2 began to rise from the 33rd to the 38th day, prior to the accumulation of propionic acid on the 40th day and after, which is a performance index, and rose from around 2 during the stable period to over 3. This is thought to be because the balance between propionic acid production and assimilation was not able to keep up with the increase in load, leading to a collapse. This is also presumed to be due to differences in the growth and metabolic rates of the bacteria responsible for each, as well as metabolic product inhibition.

[0077] These results suggest that in this system, it can be assumed that propionic acid accumulation will occur soon after Ratio 1 or 2 exceeds 3. Therefore, when Ratio 1 or 2 begins to rise, it is believed that by taking measures such as early reduction of the load, it is possible to prevent propionic acid accumulation or minimize it to a level that would be detected by water quality.

[0078] Furthermore, the trend of Ratio 2 from the stable phase to the propionic acid accumulation phase was in sync with Ratio 1. Therefore, it is believed that advance detection of propionic acid accumulation is possible by detecting Propionibacterium and Candidatus Cloacamonas as indicator microbial species, as shown in Figure 3, without performing microbial flora analysis.

[0079] In addition, assuming that propionic acid assimilation in this system is largely dependent on Candidatus Cloacamonas, it can be assumed that maintaining the dominant state of this bacterium will contribute to stable operation. One method for this is, for example, adding a substrate that contains a lot of proteins and amino acids that this bacterium prefers to assimilate. In this way, the present invention is expected to be applicable to extracting bacteria that are thought to be central to wastewater treatment and developing stabilization methods that correspond to their characteristics. [Explanation of symbols]

[0080] 1 Analysis device 10 Storage section 10A Microbial and gene function information storage area 10B Composition data storage area 10C Countermeasures information storage area 11. Base sequence data acquisition section 12 Metagenomics Department 13 Functional information extraction part 14 Fluctuation detection section 15 Prediction Department 16 Countermeasures Presentation Section

Claims

1. A method for biologically treating wastewater, comprising: A database is constructed in which information on a processing performance index or a function affecting the processing performance index (hereinafter referred to as functional information) is associated with a microbial species and / or a gene species (hereinafter referred to as indicator microbial species and / or indicator gene species) involved in fluctuations in the processing performance index or the functional information affecting the processing performance index, and stored; Obtaining time series data of at least one of the bacterial flora and genetic composition of the biological sludge (hereinafter referred to as the bacterial flora and / or genetic composition of the biological sludge); A method for biological treatment of wastewater, comprising the steps of: calculating an increase or decrease in the indicator microbial species and / or indicator gene species in the biological sludge from time-series data on the microbial flora and / or gene composition and the database; and estimating fluctuations in the treatment performance index based on the calculated value. However, in the case of a microorganism, the treatment performance index or the functional information affecting the treatment performance index includes at least utilization substance information, product information, and growth inhibition condition information, In the case of genes, it includes at least reactant information and product information.

2. The method for biological treatment of wastewater according to claim 1, characterized in that the possibility of a fluctuation in performance index to a level that leads to a deterioration in treatment performance is predicted from the fluctuation amount of the indicator microbial species and / or indicator gene species.

3. The method for biological treatment of wastewater according to claim 1 or 2, wherein the increase or decrease in the indicator microbial species and / or indicator gene species is a rate of change, a rate of change or an amount of change calculated based on amplicon sequencing, shotgun sequencing, expression analysis or electrophoresis, using the number of reads of a sequence characteristic of the microbial species and / or gene species, or a detection intensity replacing the same.

4. In any one of claims 1 to 3, the method for biological treatment of wastewater comprises:

13. A method for biologically treating wastewater, comprising the steps of: (a) detecting a propionic acid concentration in said treatment performance index; and (b) determining whether said indicator microbial species or indicator gene species is a microorganism having a propionic acid producing function or a propionic acid assimilation function.

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