Comprehensive evaluation method and device for granulation degree of anaerobic ammonia oxidation granular sludge

Abstract

The invention provides a comprehensive evaluation method and device for the granulation degree of anaerobic ammonia oxidation granular sludge, and relates to the technical field of granular sludge granulation, and the method specifically comprises the following steps: obtaining a parameter combination in a biofilter reactor during previous sewage treatment, the method comprises the following steps: collecting indexes such as functional bacteria abundance and microbial diversity of sludge at multiple sampling points after sewage treatment, constructing a granulation degree prediction model, performing training by taking reactor parameters as input and taking a mean value of microorganisms and physical characteristics as a label, and inputting a parameter combination in a current biological filter reactor into the prediction model to obtain a related index mean value; generating a particle stability coefficient, a particle performance coefficient and a comprehensive scoring coefficient, and comparing the comprehensive scoring coefficient with a preset threshold value to evaluate the granulation degree of the granular sludge in the current biological filter reactor. According to the method, multi-dimensional evaluation is realized, the accuracy and practicability of the granulation degree are improved, and a scientific basis is provided for sewage treatment.

Description

Technical Field

[0001] The present invention relates to the technical field of granular sludge granulation, and in particular to a comprehensive evaluation method and device for the granulation degree of anaerobic ammonia oxidation granular sludge. Background Art

[0002] Anaerobic ammonium oxidation (ANAMMOX) is a new denitrification process that uses nitrite as an electron acceptor to convert ammonium salts into nitrogen gas. Compared with the traditional nitrification and denitrification process, it has attracted widespread attention due to its advantages such as no need for an external carbon source, reduced aeration volume, high denitrification efficiency and low residual sludge production. At present, this process has been successfully applied to the treatment of high-temperature and high-ammonia nitrogen wastewater such as sludge digestion supernatant and landfill leachate. However, due to the limitation of temperature conditions, the anaerobic ammonium oxidation process for urban sewage has not been widely used. In addition, the anaerobic ammonium oxidizing bacteria (AAOB) have a long generation cycle, sludge is easy to lose, and are sensitive to environmental conditions, which makes it difficult to operate the process stably. Studies have shown that granulation is one of the most effective ways to improve biomass retention capacity by improving regulation ability, shock resistance and sedimentation ability. Therefore, how to quickly start ANAMMOX under medium temperature conditions and achieve sludge granulation is the key to solving the problem.

[0003] In the prior art, a comprehensive evaluation method for the degree of aerobic sludge granulation provided by publication number CN113479998A includes the following steps: by establishing a laboratory-scale sequencing batch reactor, setting the initial hydraulic and operating conditions for the experimental operation, and continuously observing the dynamic changes of the activated sludge from the floc state to the granular state during the entire cultivation process under these conditions, the observation results are characterized by the property parameters such as the average particle size, specific gravity, sedimentation velocity, and roundness coefficient of the sludge, and the three relatively independent average particle size, roundness coefficient, and specific gravity of the sludge are selected from the measured parameters as evaluation indicators to respectively characterize the size, morphology, and sedimentation performance of the sludge. This method classifies each indicator according to the range of variation of the measured data, and divides the aerobic sludge granulation process into four stages of "ungranulated-granulated-completely granulated-stable", and finally establishes a comprehensive evaluation system to comprehensively judge the real-time granulation degree of the sludge.

[0004] However, there are still some deficiencies. As can be seen from the above statements, the existing technology mainly uses the physical properties of sludge (such as average particle size, roundness coefficient and specific gravity) as evaluation indicators, lacks the collection and data processing of microbial indicators (such as functional bacteria abundance and microbial diversity), sedimentation and uniformity characteristics of particles, and fails to form a comprehensive and multi-dimensional evaluation system, resulting in the evaluation results of the degree of granulation being inconsistent with the actual situation.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide a comprehensive evaluation method and device for the granulation degree of anaerobic ammonium oxidation granular sludge, so as to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions: A comprehensive evaluation method for the granulation degree of anaerobic ammonium oxidation granular sludge, the specific steps comprising: S1. Obtain the parameter combination in the biofilter reactor when the sewage is treated based on the anaerobic ammonium oxidation method in the past, as well as the microbial index parameters and physical properties of the sludge at multiple sampling points after the sewage treatment, wherein the microbial index parameters include the abundance of functional bacteria and microbial diversity, and the physical properties include particle diameter, sedimentation and uniformity. The abundance of functional bacteria, microbial diversity, particle diameter, sedimentation and uniformity are averaged to obtain the mean value of the abundance of functional bacteria, the mean value of microbial diversity, the mean value of particle diameter, the mean value of sedimentation and the mean value of uniformity corresponding to each parameter combination, wherein the parameter combination includes dissolved oxygen, temperature, pH value, nitrogen load and hydraulic retention time; S2. Construct a prediction model for the degree of granulation of anaerobic ammonium oxidation particles, take various parameter combinations as input, and use the corresponding functional bacteria abundance mean, microbial diversity mean, particle diameter mean, sedimentation mean and uniformity mean as label training models to train the prediction model for the degree of granulation of anaerobic ammonium oxidation particles; S3. Obtain the parameter combination in the current biofilter reactor and input it into the granulation degree prediction model of the anaerobic ammonium oxidation particles to output the mean abundance of functional bacteria, the mean microbial diversity, the mean particle diameter, the mean sedimentation and the mean uniformity of the current biofilter reactor; S4. Perform data processing and correlation analysis on the mean abundance of functional bacteria and the mean microbial diversity output in S3 to generate a particle stability coefficient for evaluating the stability of particles; perform data processing and correlation analysis on the mean particle diameter, the mean sedimentation and the mean uniformity output in S3 to generate a particle performance coefficient for evaluating the performance of particles; perform data processing and correlation analysis on the particle stability coefficient and the particle performance coefficient to generate a comprehensive scoring coefficient; S5. Compare the comprehensive scoring coefficient with a preset threshold value, and evaluate the granulation degree of the granular sludge in the current biofilter reactor based on the comparison result.

[0008] Furthermore, the calculation method of the mean abundance of functional bacteria, the mean microbial diversity, the mean particle diameter, the mean sedimentation of granular sludge and the mean uniformity of granular sludge is as follows: ; in, For the The mean abundance of functional bacteria under the parameter combination, For the The first parameter combination The abundance of functional bacteria at each sampling point, is the index of the parameter combination, and , is the number of parameter combinations, is the index of the sampling point, , is the total number of sampling points; The Shannon index was used to represent microbial diversity; ; ; ; in, is the Shannon index, For the The relative abundance of species, For the The abundance of species, is the sum of the abundances of all species, is the mean value of microbial diversity, For the The first parameter combination The Shannon index of the sampling points, is the index of species, , is the total number of species; ; in, For the The mean particle diameter under the parameter combination is For the The first parameter combination The average particle diameter of each sampling point; The mean settleability of granular sludge was evaluated by measuring the settled volume of granular sludge within the settling time; ; in, For the The mean value of granular sludge settling performance under the combination of parameters is: For the The first parameter combination The granular sludge settling volume at each sampling point, For the The first parameter combination Granular sludge settling time at each sampling point; The uniformity of granular sludge was quantified by the coefficient of variation; ; ; in, For the The first parameter combination The mean uniformity of granular sludge at each sampling point is For the The coefficient of variation under the parameter combination is For the The first parameter combination The coefficient of variation of the sampling points, For the The standard deviation of particle diameter for each parameter combination.

[0009] Furthermore, the granularity prediction model is composed of a deep learning network based on a multilayer perceptron, wherein the deep neural network of the multilayer perceptron includes an input layer, a first hidden layer, a second hidden layer, a third hidden layer and an output layer, wherein the first hidden layer, the second hidden layer and the third hidden layer each have at least two neurons, and each uses ReLU as an activation function; The process of training the granulation degree prediction model is as follows: Each parameter combination is used as input, and the mean abundance of functional bacteria, the mean microbial diversity, the mean particle diameter, the mean sedimentation and the mean uniformity are used as output labels for training. The mean square error is used as the loss function. When the mean square error is When the particle size is within the range, the training of the granularity prediction model is completed.

[0010] Furthermore, the mean abundance of functional bacteria and the mean diversity of microorganisms in the current biofilter reactor were processed and correlated to generate a particle stability coefficient for evaluating the particle stability, according to the following formula: ; in, is the particle stability coefficient, which is used to reflect the particle stability of the current biofilter reactor from two levels: the mean abundance of functional bacteria and the mean diversity of microorganisms; , are the weight coefficients of the mean abundance of functional bacteria and the mean diversity of microorganisms, respectively. On the basis of .

[0011] Furthermore, the mean particle diameter, mean sedimentation and mean uniformity of the current biofilter reactor were subjected to data processing and correlation analysis, based on the following formula: ; in, The particle performance coefficient is used to reflect the particle performance of the current biofilter reactor from three levels: the mean value of particle diameter, the mean value of sedimentation, and the mean value of uniformity; is the weight coefficient of the mean particle diameter, is the weight coefficient of the mean sedimentation value, is the weight coefficient of the uniformity mean, On the basis of .

[0012] Furthermore, the particle stability coefficient and particle performance coefficient are subjected to data processing and correlation analysis to generate a comprehensive scoring coefficient based on the following formula: ; in, is a comprehensive scoring coefficient, which is used to combine the particle stability coefficient and the particle performance coefficient to comprehensively score the granularity of the granular sludge in the current biofilter reactor; and are the weights in the calculation of particle stability coefficient and particle performance coefficient, respectively, and and The specific value of is determined by the hierarchical analysis method.

[0013] Furthermore, the comprehensive scoring coefficient is compared with the preset threshold value, and the granulation degree of the granular sludge in the current biofilter reactor is evaluated according to the comparison result. The specific process is as follows: when , then the degree of granulation of granular sludge in the current biofilter reactor is high; when , then the granularity of the granular sludge in the current biofilter reactor is medium; when , then the granularity of the granular sludge in the current biofilter reactor is low; in, is the lower limit threshold of the granulation degree, indicating the critical value of low granulation degree. It is the upper limit threshold of the granularity level, indicating the critical value of a high granularity level.

[0014] To achieve the above object, the present invention also provides the following technical solutions: A device for comprehensively evaluating the granulation degree of anaerobic ammonium oxidation granular sludge, the device being used to execute any of the above-mentioned methods for comprehensively evaluating the granulation degree of anaerobic ammonium oxidation granular sludge, characterized in that it comprises: A data acquisition module is used to obtain the parameter combination in the biofilter reactor when the sewage is treated based on the anaerobic ammonium oxidation method in the past, as well as the microbial index parameters and physical properties of the sludge at multiple sampling points after the sewage treatment, wherein the microbial index parameters include the abundance of functional bacteria and the diversity of microorganisms, and the physical properties include particle diameter, sedimentation and uniformity. The abundance of functional bacteria, the diversity of microorganisms, the particle diameter, the sedimentation and the uniformity are averaged to obtain the mean of the abundance of functional bacteria, the mean of the diversity of microorganisms, the mean of the particle diameter, the mean of the sedimentation and the mean of the uniformity corresponding to each parameter combination, wherein the parameter combination includes dissolved oxygen, temperature, pH value, nitrogen load and hydraulic retention time; The prediction model building module is used to build a prediction model for the granulation degree of anaerobic ammonium oxidation particles. The various parameter combinations are used as input, and the corresponding functional bacteria abundance mean, microbial diversity mean, particle diameter mean, sedimentation mean and uniformity mean are used as label training models to train the prediction model for the granulation degree of anaerobic ammonium oxidation particles. The model output module is used to obtain the parameter combination in the current biofilter reactor and input it into the granulation degree prediction model of the anaerobic ammonium oxidation particles to output the mean abundance of functional bacteria, the mean microbial diversity, the mean particle diameter, the mean sedimentation and the mean uniformity of the current biofilter reactor; The data processing and analysis module is used to process and analyze the functional bacteria abundance mean and microbial diversity mean output in S3 to generate a particle stability coefficient for evaluating the particle stability; to process and analyze the particle diameter mean, sedimentation mean and uniformity mean output in S3 to generate a particle performance coefficient for evaluating the particle performance; to process and analyze the particle stability coefficient and the particle performance coefficient to generate a comprehensive scoring coefficient; The scoring module is used to compare the comprehensive scoring coefficient with a preset threshold value, and evaluate the granularity of the granular sludge in the current biofilter reactor based on the comparison result.

[0015] Compared with the prior art, the present invention has the following beneficial effects: The present invention not only collects the physical properties of sludge, including particle diameter, sedimentation and uniformity, to provide basic data for the evaluation of granulation degree, but also introduces biological indicators such as functional bacteria abundance and diversity to increase the depth and comprehensiveness of the evaluation. By constructing a prediction model for the degree of granulation, various parameter combinations are used as input, and the mean value of functional bacteria abundance, the mean value of microbial diversity, the mean value of particle diameter, the mean value of sedimentation and the mean value of uniformity are used as labels for training, a reference model is provided for the granulation degree of granular sludge in the current biofilter reactor, and the influence of various reactor parameter combinations on the particle stability coefficient and the particle performance coefficient is systematically analyzed and evaluated. On this basis, a particle stability coefficient, a particle performance coefficient and a comprehensive scoring coefficient are generated. These coefficients clarify the interaction between various indicators, thereby forming a comprehensive and multi-dimensional evaluation system to improve the evaluation results of the degree of granulation in the current biofilter reactor. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the overall method flow of the present invention; Figure 2 This is a block diagram of the module composition of the present invention. DETAILED DESCRIPTION

[0017] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0018] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0019] Embodiment 1: See also Figure 1 , the present invention provides a technical solution: A comprehensive evaluation method for the granulation degree of anaerobic ammonium oxidation granular sludge, the specific steps comprising: S1. Obtain the parameter combination in the biofilter reactor when the sewage is treated based on the anaerobic ammonium oxidation method in the past, as well as the microbial index parameters and physical properties of the sludge at multiple sampling points after the sewage treatment, wherein the microbial index parameters include the abundance of functional bacteria and microbial diversity, and the physical properties include particle diameter, sedimentation and uniformity. The abundance of functional bacteria, microbial diversity, particle diameter, sedimentation and uniformity are averaged to obtain the mean value of the abundance of functional bacteria, the mean value of microbial diversity, the mean value of particle diameter, the mean value of sedimentation and the mean value of uniformity corresponding to each parameter combination, wherein the parameter combination includes dissolved oxygen, temperature, pH value, nitrogen load and hydraulic retention time; S2. Construct a prediction model for the degree of granulation of anaerobic ammonium oxidation particles, take various parameter combinations as input, and use the corresponding functional bacteria abundance mean, microbial diversity mean, particle diameter mean, sedimentation mean and uniformity mean as label training models to train the prediction model for the degree of granulation of anaerobic ammonium oxidation particles; S3. Obtain the parameter combination in the current biofilter reactor and input it into the granulation degree prediction model of the anaerobic ammonium oxidation particles to output the mean abundance of functional bacteria, the mean microbial diversity, the mean particle diameter, the mean sedimentation and the mean uniformity of the current biofilter reactor; S4. Perform data processing and correlation analysis on the mean abundance of functional bacteria and the mean microbial diversity output in S3 to generate a particle stability coefficient for evaluating the stability of particles; perform data processing and correlation analysis on the mean particle diameter, the mean sedimentation and the mean uniformity output in S3 to generate a particle performance coefficient for evaluating the performance of particles; perform data processing and correlation analysis on the particle stability coefficient and the particle performance coefficient to generate a comprehensive scoring coefficient; S5. Compare the comprehensive scoring coefficient with a preset threshold value, and evaluate the granulation degree of the granular sludge in the current biofilter reactor based on the comparison result.

[0020] Based on the above embodiments, although anaerobic ammonium oxidation is mainly carried out in an anoxic environment, an appropriate dissolved oxygen level can promote the growth and metabolic activity of microorganisms. A lower DO is generally conducive to the reproduction of anaerobic microorganisms and particle formation. However, if the dissolved oxygen is too low, it may lead to insufficient microbial activity and affect granulation. The dissolved oxygen needs to be adjusted according to the specific situation to ensure the activity of the microorganisms and the formation of granular sludge.

[0021] Temperature has a significant effect on the metabolic rate and reaction rate of microorganisms. The appropriate temperature range can promote the growth of microorganisms and enhance the ability of sludge granulation. Generally speaking, too high or too low temperature will inhibit the activity of microorganisms, thus affecting the granulation process. Usually, the optimal temperature range for anaerobic ammonium oxidation is between 25°C and 35°C. Maintaining it within this range is conducive to the formation of particles.

[0022] The pH value directly affects the growth environment and metabolic activities of microorganisms. Anaerobic ammonia-oxidizing microorganisms are most active in a neutral to weakly alkaline environment (pH7-8.5). Too low or too high a pH value will inhibit microbial activity and reduce the effect of granulation.

[0023] Nitrogen load refers to the total amount of nitrogen input into water or soil in a specific period of time, usually expressed in mass units (such as kilograms). Appropriate nitrogen load is essential for promoting plant growth and maintaining the health of the ecosystem; however, excessive nitrogen load may lead to eutrophication of water bodies, causing algae to reproduce in large numbers, affecting water quality and ecological balance. On the contrary, too low nitrogen load may lead to restricted plant growth and affect crop yields. Therefore, it is necessary to reasonably adjust the nitrogen load according to the specific ecosystem needs and soil conditions to ensure the healthy growth of plants and the good ecological state of the water body.

[0024] Hydraulic retention time (HRT) refers to the average time that water stays in a specific water treatment system, usually in hours or days. Appropriate hydraulic retention time helps improve the efficiency of water treatment and ensure that pollutants are fully removed. In the sewage treatment process, too short a retention time may lead to incomplete sewage treatment and substandard effluent quality, while too long a retention time may lead to reduced system efficiency and waste of resources. Therefore, it is necessary to reasonably adjust the hydraulic retention time according to the specific water treatment process and influent water quality to optimize the treatment effect and ensure effluent water quality.

[0025] In summary, dissolved oxygen, temperature, pH value, nitrogen load and hydraulic retention time are important factors affecting the granulation degree of anaerobic ammonium oxidation granular sludge. These parameters affect the formation and stability of particles by affecting microbial activity, metabolic rate and reaction environment. Therefore, when conducting anaerobic ammonium oxidation granulation experiments, these parameters need to be reasonably optimized and controlled to promote the best granulation effect.

[0026] Based on the above embodiments, the abundance of functional bacteria refers to the number or relative abundance of microorganisms with specific ecological functions in a specific environment. These functional bacteria play an important role in the ecosystem, such as promoting nutrient cycling, decomposing organic matter, fixing nitrogen, and degrading pollutants.

[0027] Microbial diversity: The higher the diversity, the greater the stability and resistance of the ecosystem. Diverse microbial communities can more effectively promote the granulation process and form more stable granular sludge.

[0028] Particle diameter: The average diameter of the particles directly affects their sedimentation characteristics and processing efficiency. The appropriate diameter helps to optimize the sedimentation process and promote the aggregation and stabilization of particles.

[0029] Sedimentation: Sedimentation is the ability of granular sludge to settle in water. Good sedimentation usually means that the granular sludge can be effectively separated from the water phase, which is an important feature of granulation.

[0030] Uniformity: The uniformity of the particles determines the behavior of the particles in the reactor. The more uniform the particles are, the more stable the sedimentation process is, and the degree of granulation is usually higher.

[0031] In summary, microbial indicator parameters (abundance and diversity) and physical properties of granular sludge (particle diameter, sedimentation and uniformity) can effectively obtain the granulation degree of anaerobic ammonium oxidation granular sludge.

[0032] Among them, the collection methods of functional bacteria abundance, species abundance, particle diameter, granular sludge settling volume, and standard deviation of particle diameter are as follows: sewage samples are collected regularly from different levels or areas of the reactor, usually 500 ml, and the collected samples are filtered or centrifuged to separate the precipitated microorganisms to obtain the functional bacteria abundance; Collect wastewater samples regularly from different levels or areas of the reactor, use DNA extraction kits for gene sequencing, and calculate species abundance; A certain volume of sludge sample is taken out from the reactor, the sample is diluted to an appropriate concentration, and a laser particle size analyzer is used to perform particle size analysis to obtain the particle diameter; In the laboratory, use a standard sedimentation tube (such as a measuring cylinder or a sedimentation tank), put a certain volume (such as 100 ml) of sludge sample into the sedimentation tube, let it stand for 30 minutes to 1 hour, record the sludge volume after sedimentation, and measure the granular sludge sedimentation volume with a ruler; When measuring the particle diameter, record the particle size data of multiple measurements (for example, measure the diameter of 100 particles), and use the standard deviation calculation formula to obtain the standard deviation of the particle diameter.

[0033] Based on the above examples, the calculation method of the mean abundance of functional bacteria, the mean microbial diversity, the mean particle diameter, the mean sedimentation of granular sludge and the mean uniformity of granular sludge is as follows: ; in, For the The mean abundance of functional bacteria under the parameter combination, For the The first parameter combination The abundance of functional bacteria at each sampling point, is the index of the parameter combination, and , is the number of parameter combinations, is the index of the sampling point, , is the total number of sampling points; The Shannon index was used to represent microbial diversity; ; ; ; in, is the Shannon index, For the The relative abundance of species, For the The abundance of species, is the sum of the abundances of all species, is the mean value of microbial diversity, For the The first parameter combination The Shannon index of the sampling points, is the index of species, , is the total number of species; ; in, For the The mean particle diameter under the parameter combination is For the The first parameter combination The average particle diameter of each sampling point; The mean settleability of granular sludge was evaluated by measuring the settled volume of granular sludge within the settling time; ; in, For the The mean value of granular sludge settling performance under the combination of parameters is: For the The first parameter combination The granular sludge settling volume at each sampling point, For the The first parameter combination Granular sludge settling time at each sampling point; The uniformity of granular sludge was quantified by the coefficient of variation; ; ; in, For the The first parameter combination The mean uniformity of granular sludge at each sampling point is For the The coefficient of variation under the parameter combination is For the The first parameter combination The coefficient of variation of the sampling points, For the The standard deviation of particle diameter for each parameter combination.

[0034] On the basis of the above embodiment, the granularity prediction model is composed of a deep learning network based on a multilayer perceptron, and the deep neural network of the multilayer perceptron includes an input layer, a first hidden layer, a second hidden layer, a third hidden layer and an output layer, and the first hidden layer, the second hidden layer and the third hidden layer each have at least two neurons, and each uses ReLU as an activation function; In this embodiment, the input features of the deep learning network of the multilayer perceptron include five features: dissolved oxygen, temperature, pH value, nitrogen load, and hydraulic retention time.

[0035] The structure of the deep learning network of multi-layer perceptron is: Input layer: receives input of 5 features; The first hidden layer has 64 neurons and uses ReLU as the activation function. The second hidden layer has 32 neurons and also uses the ReLU activation function. The third hidden layer has 16 neurons and uses the ReLU activation function. Output layer: has 5 neurons, including the mean abundance of functional bacteria, the mean microbial diversity, the mean particle diameter, the mean sedimentation and the mean uniformity.

[0036] The process of training the granulation degree prediction model is as follows: Each parameter combination is used as input, and the mean abundance of functional bacteria, the mean microbial diversity, the mean particle diameter, the mean sedimentation and the mean uniformity are used as output labels for training. The mean square error is used as the loss function. When the mean square error is When the particle size is within the range, the training of the granularity prediction model is completed.

[0037] Based on the above examples, the correlation between the mean abundance of functional bacteria, the mean microbial diversity and particle stability is as follows: The mean abundance of functional bacteria is positively correlated with particle stability because within a certain range, moderately increasing the mean abundance of functional bacteria can enhance aggregation and flocculation to form larger particles, thereby improving the sedimentation and stability of particles.

[0038] The mean value of microbial diversity is positively correlated with particle stability because within a certain range, a moderate increase in the mean value of microbial diversity can enhance the physical and chemical stability of particles through their respective metabolic pathways and interactions; and microbial communities with higher diversity can more effectively respond to environmental changes (such as pH, temperature, dissolved oxygen, etc.), thereby enhancing the stability of particles under different conditions.

[0039] According to the correlation between the mean abundance of functional bacteria, the mean diversity of microorganisms and particle stability, the mean abundance of functional bacteria and the mean diversity of microorganisms of the current biofilter reactor were processed and correlated to generate a particle stability coefficient for evaluating the particle stability, according to the following formula: ; in, is the particle stability coefficient, which is used to reflect the particle stability of the current biofilter reactor from two levels: the mean abundance of functional bacteria and the mean diversity of microorganisms. The larger the particle stability coefficient, the higher the stability. , are the weight coefficients of the mean abundance of functional bacteria and the mean diversity of microorganisms, respectively, which are used to reflect the influence of different microbial index parameters on the particle stability coefficient; The reasons for setting the above function form to express the functional relationship between the particle stability coefficient and the mean abundance of functional bacteria and the mean microbial diversity are as follows: First, the average abundance of functional bacteria The higher the value, the stronger the aggregation and flocculation of the particles, thereby improving the sedimentation and stability of the particles. Taking the mean abundance of functional bacteria as a positive weighted factor can directly reflect the promoting effect of abundance on particle stability.

[0040] Second, the mean value of microbial diversity The higher the value, the more physical and chemical stability of the particle can be enhanced through multiple metabolic pathways and interactions, which can be used as a positive weighting factor to accurately represent the positive impact of diversity on particle stability.

[0041] Third, weight coefficient and It is a key parameter that reflects the relative importance of the abundance and diversity of functional bacteria in affecting particle stability. The abundance and diversity of functional bacteria affect particle stability at two different levels. By adjusting these coefficients, the model can be made more flexible to adapt to changes in different environments or conditions and highlight the degree of influence of different factors on particle stability.

[0042] When the abundance of functional bacteria is high, it can promote the aggregation and flocculation of particles to form larger particles, thereby improving sedimentation and stability. This direct impact is more significant. Although microbial diversity also plays an important role in stability, its impact is often reflected in the long-term stability and adaptability of the ecosystem. Relatively speaking, abundance has a greater impact on particle stability in the short term, making the weight coefficient of the mean abundance of functional bacteria greater than the weight coefficient of the mean microbial diversity.

[0043] Therefore, in On the basis of .

[0044] As an implementation method, The value range is an open interval of 0.5-0.6. The value range is an open interval of 0.4-0.5. The specific value is set by the technicians according to the actual situation and is not limited here.

[0045] Based on the above examples, the correlation between the mean particle diameter, the mean sedimentation, the mean uniformity and the particle performance is as follows: The mean particle diameter is positively correlated with particle performance, because the larger the particle diameter, the faster the sedimentation rate and the better the sedimentation performance of the particle.

[0046] The mean sedimentation value is positively correlated with the particle performance. Since sedimentation directly reflects the behavior of particles in the fluid, the better the sedimentation, the higher the performance coefficient of the particle.

[0047] The mean uniformity value is negatively correlated with the particle performance. This is because within a certain range, particles with high uniformity have greater variability in particle diameter. Meanwhile, particles with poor uniformity have wider distribution of particle diameter, which may lead to decreased sedimentation performance and thus reduce the overall performance of the particles.

[0048] According to the correlation between the mean particle diameter, the mean sedimentation, the mean uniformity and the particle performance, a functional relationship between the mean particle diameter, the mean sedimentation, the mean uniformity and the particle performance coefficient is constructed, and the formula is as follows: ; in, The particle performance coefficient is used to reflect the particle performance of the current biofilter reactor from three levels: the mean value of particle diameter, the mean value of sedimentation, and the mean value of uniformity. The larger the particle performance coefficient, the better the particle performance. is the weight coefficient of the mean particle diameter, is the weight coefficient of the mean sedimentation value, is the weight coefficient of the uniformity mean, which is used to reflect the influence of the physical characteristics of different granular sludge on the granular performance coefficient; The reasons for setting the above function form to express the functional relationship between the particle performance coefficient and the mean particle diameter, the mean sedimentation, and the mean uniformity are as follows: First,( ) expresses the positive effect of particle diameter and sedimentation on particle performance. Larger particle diameter and sedimentation mean may improve the performance coefficient of the particle, so it is set in the numerator.

[0049] second,( ) expresses the effect of mean uniformity, and since mean uniformity is negatively correlated with particle performance, placing it in the denominator reflects that poor uniformity will reduce the overall performance coefficient.

[0050] Third, the linear function can clearly reflect the positive and negative correlations between different variables. For example, the positive correlation between the mean particle diameter and the mean sedimentation and the particle performance coefficient can be reflected by a positive coefficient, while the negative impact of the mean uniformity can be represented by a negative coefficient. Therefore, the functional relationship between the mean particle diameter, the mean sedimentation, the mean uniformity and the particle performance coefficient is simplified to a linear relationship.

[0051] Fourth, weight coefficient , and It is a key parameter, reflecting the relative importance of mean particle diameter, mean sedimentation and mean uniformity in affecting particle performance. Mean particle diameter, mean sedimentation and mean uniformity affect particle performance at three different levels. By adjusting these coefficients, the model can be made more flexible to adapt to changes in different environments or conditions and highlight the influence of different factors on particle performance.

[0052] The corresponding factor is the mean particle diameter. Particle diameter usually has a significant effect on the performance of the particle. Studies have shown that larger particle diameters usually increase the settling velocity and effectiveness of the particle. Therefore, in the calculation of the performance coefficient, the influence of particle diameter is the greatest.

[0053] Corresponds to the mean sedimentation value. Sedimentation affects the behavior of particles in liquids. Although it is also important, its influence is usually slightly lower than that of particle diameter. This is because the particle diameter itself directly affects the sedimentation properties, so in some cases, the effect of particle diameter is considered to be the main factor.

[0054] The effect of uniformity on particle performance is usually negatively correlated (i.e., the worse the uniformity, the lower the performance), and within a certain range, the change in uniformity will not be as significant as the particle diameter and sedimentation. Therefore, The value of is generally smaller than the other two coefficients.

[0055] In summary, the weight coefficient of the mean particle diameter is greater than the weight coefficient of the mean sedimentation, and the weight coefficient of the mean sedimentation is greater than the weight coefficient of the mean uniformity. On the basis of .

[0056] As an implementation method, The value range is an open interval of 0.4-0.5. The value range is an open interval of 0.3-0.4. The value range is an open interval of 0.2-0.3. The specific value is set by the technicians according to the actual situation and is not limited here.

[0057] On the basis of the above embodiment, the particle stability coefficient and the particle performance coefficient are subjected to data processing and correlation analysis to generate a comprehensive scoring coefficient, based on the following formula: ; in, is the comprehensive scoring coefficient, which is used to combine the particle stability coefficient and the particle performance coefficient to comprehensively score the granulation degree of the granular sludge in the current biofilter reactor. The larger the comprehensive scoring coefficient, the higher the granulation degree. It should be noted that, as can be seen from the above description, the particle stability coefficient The larger the particle size, the higher the stability. The larger the particle size, the better the particle performance, so the comprehensive scoring coefficient Particle stability factor , particle performance coefficient All are positively correlated, so the calculation formula of the comprehensive scoring coefficient in the form of weighted summation is set; In the formula, and are the weights in the calculation of particle stability coefficient and particle performance coefficient, respectively, and and The specific value of is determined by the hierarchical analysis method, and the specific logic is as follows: The two indicators of particle stability coefficient and particle performance coefficient are marked, and the relative importance between them is determined by the nine-scale method to construct a judgment matrix, in which the index of particle stability coefficient is marked as 1 and the index of particle performance coefficient is marked as 2. The constructed judgment matrix for: ; in, , denotes the index of the coefficient, and , , indicating that the index is The importance of the index of relative to the index of v to the comprehensive scoring coefficient, The specific value is determined by relevant experts using a 1-9 scoring method. Indicates that the index is The index of is more important to the comprehensive score coefficient than the index of v. Indicates that the index is The coefficient of is extremely unimportant to the comprehensive score coefficient compared to the coefficient with index v; Each element value in the judgment matrix is ​​divided by the sum of its columns to obtain a normalized judgment matrix. The mean of the element values ​​in each row of the normalized judgment matrix is ​​calculated, and the mean of the element values ​​in the first row is used as the proportional coefficient of the particle stability coefficient, and the mean of the element values ​​in the second row is used as the proportional coefficient of the particle performance coefficient. With the constraint that the sum of the scaled values ​​is equal to 1, the two proportional coefficients are scaled in equal proportion, and the values ​​obtained after scaling are used as the weights of the corresponding coefficients.

[0058] On the basis of the above embodiment, the comprehensive scoring coefficient is compared with the preset threshold value, and the granularity of the granular sludge in the current biofilter reactor is evaluated according to the comparison result. The specific process is as follows: when , then the degree of granulation of granular sludge in the current biofilter reactor is high; when , then the granularity of the granular sludge in the current biofilter reactor is medium; when , then the granularity of the granular sludge in the current biofilter reactor is low; in, is the lower limit threshold of the granulation degree, indicating the critical value of low granulation degree. is the upper threshold of the granularity level, indicating the critical value of a high granularity level. By statistically analyzing a large amount of experimental data, the distribution of granularity scores can be determined. The maximum value, minimum value, mean value, and standard deviation in historical data can be used to set the threshold. For example, is set to a certain standard deviation below the mean, and Can be set to a certain standard deviation above the mean.

[0059] See also Figure 2 , the present invention also provides a technical solution: A device for comprehensively evaluating the granulation degree of anaerobic ammonium oxidation granular sludge, the device being used to execute any of the above-mentioned methods for comprehensively evaluating the granulation degree of anaerobic ammonium oxidation granular sludge, comprising: A data acquisition module is used to obtain the parameter combination in the biofilter reactor when the sewage is treated based on the anaerobic ammonium oxidation method in the past, as well as the microbial index parameters and physical properties of the sludge at multiple sampling points after the sewage treatment, wherein the microbial index parameters include the abundance of functional bacteria and the diversity of microorganisms, and the physical properties include particle diameter, sedimentation and uniformity. The abundance of functional bacteria, the diversity of microorganisms, the particle diameter, the sedimentation and the uniformity are averaged to obtain the mean of the abundance of functional bacteria, the mean of the diversity of microorganisms, the mean of the particle diameter, the mean of the sedimentation and the mean of the uniformity corresponding to each parameter combination, wherein the parameter combination includes dissolved oxygen, temperature, pH value, nitrogen load and hydraulic retention time; The prediction model building module is used to build a prediction model for the granulation degree of anaerobic ammonium oxidation particles. The various parameter combinations are used as input, and the corresponding functional bacteria abundance mean, microbial diversity mean, particle diameter mean, sedimentation mean and uniformity mean are used as label training models to train the prediction model for the granulation degree of anaerobic ammonium oxidation particles. The model output module is used to obtain the parameter combination in the current biofilter reactor and input it into the granulation degree prediction model of the anaerobic ammonium oxidation particles to output the mean abundance of functional bacteria, the mean microbial diversity, the mean particle diameter, the mean sedimentation and the mean uniformity of the current biofilter reactor; The data processing and analysis module is used to process and analyze the functional bacteria abundance mean and microbial diversity mean output in S3 to generate a particle stability coefficient for evaluating the particle stability; to process and analyze the particle diameter mean, sedimentation mean and uniformity mean output in S3 to generate a particle performance coefficient for evaluating the particle performance; to process and analyze the particle stability coefficient and the particle performance coefficient to generate a comprehensive scoring coefficient; The scoring module is used to compare the comprehensive scoring coefficient with a preset threshold value, and evaluate the granulation degree of the granular sludge in the current biofilter reactor based on the comparison result.

[0060] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0061] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by computer software, electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software depends on the specific application and design constraints of the technical solution.

[0062] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0063] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A comprehensive evaluation method for the granulation degree of anaerobic ammonium oxidation granular sludge, characterized by: The specific steps include: S1. Obtain the parameter combination in the biofilter reactor when the sewage is treated based on the anaerobic ammonium oxidation method in the past, as well as the microbial index parameters and physical properties of the sludge at multiple sampling points after the sewage treatment, wherein the microbial index parameters include the abundance of functional bacteria and microbial diversity, and the physical properties include particle diameter, sedimentation and uniformity. The abundance of functional bacteria, microbial diversity, particle diameter, sedimentation and uniformity are averaged to obtain the mean value of the abundance of functional bacteria, the mean value of microbial diversity, the mean value of particle diameter, the mean value of sedimentation and the mean value of uniformity corresponding to each parameter combination, wherein the parameter combination includes dissolved oxygen, temperature, pH value, nitrogen load and hydraulic retention time; S2. Construct a prediction model for the degree of granulation of anaerobic ammonium oxidation particles, take various parameter combinations as input, and use the corresponding functional bacteria abundance mean, microbial diversity mean, particle diameter mean, sedimentation mean and uniformity mean as label training models to train the prediction model for the degree of granulation of anaerobic ammonium oxidation particles; S3. Obtain the parameter combination in the current biofilter reactor and input it into the granulation degree prediction model of the anaerobic ammonium oxidation particles to output the mean abundance of functional bacteria, the mean microbial diversity, the mean particle diameter, the mean sedimentation and the mean uniformity of the current biofilter reactor; S4. Perform data processing and correlation analysis on the mean abundance of functional bacteria and the mean microbial diversity output in S3 to generate a particle stability coefficient for evaluating the stability of particles; perform data processing and correlation analysis on the mean particle diameter, the mean sedimentation and the mean uniformity output in S3 to generate a particle performance coefficient for evaluating the performance of particles; perform data processing and correlation analysis on the particle stability coefficient and the particle performance coefficient to generate a comprehensive scoring coefficient; S5. Compare the comprehensive scoring coefficient with a preset threshold value, and evaluate the granulation degree of the granular sludge in the current biofilter reactor based on the comparison result.

2. The comprehensive evaluation method for the granulation degree of anaerobic ammonium oxidation granular sludge according to claim 1 is characterized in that: Calculation method for the mean abundance of functional bacteria, the mean microbial diversity, the mean particle diameter, the mean sedimentation of granular sludge and the mean uniformity of granular sludge: ； in, For the The mean abundance of functional bacteria under the parameter combination, For the The first parameter combination The abundance of functional bacteria at each sampling point, is the index of the parameter combination, and , is the number of parameter combinations, is the index of the sampling point, , is the total number of sampling points; The Shannon index was used to represent microbial diversity; ； ； ； in, is the Shannon index, For the The relative abundance of species, For the The abundance of species, is the sum of the abundances of all species, is the mean value of microbial diversity, For the The first parameter combination The Shannon index of the sampling points, is the index of species, , is the total number of species; ； in, For the The mean particle diameter under each parameter combination is For the The first parameter combination The average particle diameter of each sampling point; The mean settleability of granular sludge was evaluated by measuring the settled volume of granular sludge within the settling time; ； in, For the The mean value of granular sludge settling performance under the combination of parameters is: For the The first parameter combination The granular sludge settling volume at each sampling point, For the The first parameter combination Granular sludge settling time at each sampling point; The uniformity of granular sludge was quantified by the coefficient of variation; ； ； in, For the The first parameter combination The mean uniformity of granular sludge at each sampling point is For the The coefficient of variation under the parameter combination is For the The first parameter combination The coefficient of variation of the sampling points, For the The standard deviation of particle diameter for each parameter combination.

3. The comprehensive evaluation method for the granulation degree of anaerobic ammonium oxidation granular sludge according to claim 1 is characterized in that: The granularity prediction model is composed of a deep learning network based on a multilayer perceptron, wherein the deep neural network of the multilayer perceptron includes an input layer, a first hidden layer, a second hidden layer, a third hidden layer and an output layer, wherein the first hidden layer, the second hidden layer and the third hidden layer each have at least two neurons, and each uses ReLU as an activation function; The process of training the granulation degree prediction model is as follows: Each parameter combination is used as input, and the mean abundance of functional bacteria, the mean microbial diversity, the mean particle diameter, the mean sedimentation and the mean uniformity are used as output labels for training. The mean square error is used as the loss function. When the mean square error is When the particle size is within the range, the training of the granularity prediction model is completed.

4. The comprehensive evaluation method for the granulation degree of anaerobic ammonium oxidation granular sludge according to claim 2 is characterized in that: The mean abundance of functional bacteria and the mean diversity of microorganisms in the current biofilter reactor were processed and correlated to generate a particle stability coefficient for evaluating the particle stability, according to the following formula: ； in, is the particle stability coefficient, which is used to reflect the particle stability of the current biofilter reactor from two levels: the mean abundance of functional bacteria and the mean diversity of microorganisms; , are the weight coefficients of the mean abundance of functional bacteria and the mean diversity of microorganisms, respectively. On the basis of .

5. The comprehensive evaluation method for the granulation degree of anaerobic ammonium oxidation granular sludge according to claim 4 is characterized in that: The mean particle diameter, mean sedimentation and mean uniformity of the current biofilter reactor were processed and correlated according to the following formula: ； in, The particle performance coefficient is used to reflect the particle performance of the current biofilter reactor from three levels: the mean value of particle diameter, the mean value of sedimentation, and the mean value of uniformity; is the weight coefficient of the mean particle diameter, is the weight coefficient of the mean sedimentation value, is the weight coefficient of the uniformity mean, On the basis of .

6. The comprehensive evaluation method for the granulation degree of anaerobic ammonium oxidation granular sludge according to claim 5 is characterized in that: The particle stability coefficient and particle performance coefficient are processed and correlated to generate a comprehensive scoring coefficient based on the following formula: ； in, is a comprehensive scoring coefficient, which is used to combine the particle stability coefficient and the particle performance coefficient to comprehensively score the granularity of the granular sludge in the current biofilter reactor; and are the weights in the calculation of particle stability coefficient and particle performance coefficient, respectively, and and The specific value of is determined by the hierarchical analysis method.

7. The comprehensive evaluation method for the granulation degree of anaerobic ammonium oxidation granular sludge according to claim 6 is characterized by: The comprehensive scoring coefficient is compared with the preset threshold value, and the granulation degree of the granular sludge in the current biofilter reactor is evaluated based on the comparison result. The specific process is as follows: when , then the degree of granulation of granular sludge in the current biofilter reactor is high; when , then the granularity of the granular sludge in the current biofilter reactor is medium; when , then the granularity of the granular sludge in the current biofilter reactor is low; in, is the lower limit threshold of the granulation degree, indicating the critical value of low granulation degree. It is the upper limit threshold of the granularity level, indicating the critical value of a high granularity level.

8. A device for comprehensively evaluating the granulation degree of anaerobic ammonium oxidation granular sludge, the device being used to execute a method for comprehensively evaluating the granulation degree of anaerobic ammonium oxidation granular sludge according to any one of claims 1 to 7, characterized in that: include: A data acquisition module is used to obtain the parameter combination in the biofilter reactor when the sewage is treated based on the anaerobic ammonium oxidation method in the past, as well as the microbial index parameters and physical properties of the sludge at multiple sampling points after the sewage treatment, wherein the microbial index parameters include the abundance of functional bacteria and the diversity of microorganisms, and the physical properties include particle diameter, sedimentation and uniformity. The abundance of functional bacteria, the diversity of microorganisms, the particle diameter, the sedimentation and the uniformity are averaged to obtain the mean of the abundance of functional bacteria, the mean of the diversity of microorganisms, the mean of the particle diameter, the mean of the sedimentation and the mean of the uniformity corresponding to each parameter combination, wherein the parameter combination includes dissolved oxygen, temperature, pH value, nitrogen load and hydraulic retention time; The prediction model building module is used to build a prediction model for the granulation degree of anaerobic ammonium oxidation particles. The various parameter combinations are used as input, and the corresponding functional bacteria abundance mean, microbial diversity mean, particle diameter mean, sedimentation mean and uniformity mean are used as label training models to train the prediction model for the granulation degree of anaerobic ammonium oxidation particles. The model output module is used to obtain the parameter combination in the current biofilter reactor and input it into the granulation degree prediction model of the anaerobic ammonium oxidation particles to output the mean abundance of functional bacteria, the mean microbial diversity, the mean particle diameter, the mean sedimentation and the mean uniformity of the current biofilter reactor; The data processing and analysis module is used to process and analyze the functional bacteria abundance mean and microbial diversity mean output in S3 to generate a particle stability coefficient for evaluating the particle stability; to process and analyze the particle diameter mean, sedimentation mean and uniformity mean output in S3 to generate a particle performance coefficient for evaluating the particle performance; to process and analyze the particle stability coefficient and the particle performance coefficient to generate a comprehensive scoring coefficient; The scoring module is used to compare the comprehensive scoring coefficient with a preset threshold value, and evaluate the granulation degree of the granular sludge in the current biofilter reactor based on the comparison result.

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