Method and device for rapidly obtaining effluent ammonia nitrogen concentration of sewage treatment system

Abstract

The invention belongs to the field of sewage treatment, and particularly relates to a method and a device for rapidly obtaining the effluent ammonia nitrogen concentration of a sewage treatment system. According to the specific technical scheme, the method comprises the following steps: measuring the real-time oxygen consumption rate and the highest oxygen consumption rate of ammonia oxidizing bacteria in the sewage treatment system; calculating K, wherein K = real-time oxygen consumption rate / maximum oxygen consumption rate; estimating the effluent ammonia nitrogen concentration according to the K value; and establishing an artificial neural network model, and comprehensively judging the concentration of ammonia nitrogen in effluent in combination with the K value. Through the method, the ammonia nitrogen concentration in the effluent of the sewage treatment system can be quickly estimated and calculated, and the method is particularly suitable for quickly detecting and judging whether the ammonia nitrogen in the effluent of the sewage treatment system reaches the standard or not.

Description

Technical Field

[0001] The invention belongs to the field of sewage treatment, and in particular relates to a method and a device for rapidly obtaining the ammonia nitrogen concentration of effluent from a sewage treatment system. Background Art

[0002] Ammonia nitrogen is one of the pollutants that most sewage treatment systems need to focus on and mainly remove. Exceeding the standard will bring great environmental hazards, such as polluting water quality, causing eutrophication of water bodies, affecting drinking water safety, and poisoning aquatic organisms. Therefore, it is necessary to conduct regular and irregular tests on the ammonia nitrogen in the effluent of the sewage treatment system to confirm whether the treatment meets the standard.

[0003] At present, the methods for detecting the ammonia nitrogen concentration in the effluent of sewage treatment systems mainly include spectrophotometry, electrode method, gas phase molecular absorption spectroscopy and distillation-acid titration. Spectrophotometry generally requires pretreatment of water samples, and the detection is easily interfered by other components in the environment such as metal ions and sulfides, resulting in inaccurate test results. The electrode method is easily affected by the pH, temperature, ionic strength, etc. of the water body, and has high requirements for equipment. Gas phase molecular absorption spectroscopy requires specific instruments and operating techniques. The distillation-acid titration method is cumbersome, time-consuming, and consumes a lot of reagents. Moreover, these detection methods generally have a certain time delay, and cannot perform real-time, online, and rapid detection and judgment of the ammonia nitrogen concentration in the water body. When it is detected that the ammonia nitrogen does not meet the standard, the substandard effluent may have been discharged into the environment in large quantities.

[0004] Therefore, if a simple, fast and highly accurate method for determining the effluent ammonia nitrogen concentration can be provided, it will have great practical application value. Summary of the invention

[0005] The purpose of the present invention is to provide a method and device for quickly obtaining the ammonia nitrogen concentration of a water body.

[0006] To achieve the above-mentioned purpose, the technical solution adopted by the present invention is: a method for calculating the ammonia nitrogen concentration in the effluent water of a sewage treatment system, the method comprising the following steps:

[0007] (1) Determine the real-time oxygen consumption rate and maximum oxygen consumption rate of ammonia oxidizing bacteria in water;

[0008] (2) Calculate K, K = real-time oxygen consumption rate / maximum oxygen consumption rate; obtain ammonia nitrogen concentration based on the K value.

[0009] Preferably, in step (1), the method for determining the real-time oxygen consumption rate comprises:

[0010] Take the sample to be tested in a container, add sodium chlorate solution, aerate until saturated, and record the dissolved oxygen demand after the decreasing rate of dissolved oxygen demand stabilizes. Calculate the decreasing rate of dissolved oxygen demand, which is recorded as R1 ; Then add allylthiourea solution and aerate again until saturation. After the dissolved oxygen demand decreases steadily, record the dissolved oxygen demand and calculate the decrease rate of the dissolved oxygen demand, which is recorded as R 2 ; Real-time oxygen consumption rate = R 1 -R 2 ,

[0011] Preferably, in step (1), the method for determining the maximum oxygen consumption rate comprises: taking the sample to be tested into a container, adding sodium chlorate solution, aerating to saturation, and after the decreasing rate of dissolved oxygen demand is stable, adding allyl thiourea solution, aerating to saturation again, and after the decreasing rate of dissolved oxygen demand is stable, recording the dissolved oxygen demand, and calculating the decreasing rate of dissolved oxygen demand, which is recorded as R 2 ; Take an equal amount of the sample to be tested in a container, add sodium chlorate solution, aerate until saturated, add ammonium chloride solution, stop aeration, and record DO after the dissolved oxygen demand decreases steadily. Calculate the rate of decrease of dissolved oxygen demand, which is recorded as R 3 ; Maximum oxygen consumption rate = R 3 -R 2 .

[0012] Preferably, the method for obtaining the ammonia nitrogen concentration includes: if K < 0.7, it is considered that the effluent ammonia nitrogen concentration is < 1.5 mg / L.

[0013] Preferably, the method further comprises:

[0014] (3) Calculate the ammonia nitrogen concentration through the artificial neural network model: the real-time oxygen consumption rate, suspended solid matter concentration and temperature of the water sample to be tested are used as input variables, and the ammonia nitrogen concentration of the effluent water body is used as the output variable. The "Neural Network Fitting APP" in the software establishes an artificial neural network model; the number of input variables of the artificial neural network model is 3, the number of hidden layers is 1, the number of hidden layer neurons is 6, and the number of output variables is 1. The Levenberg-Marquardt algorithm is used for training. After the model is established and trained, the model prediction value is obtained in real time using the model: effluent ammonia nitrogen concentration.

[0015] Preferably, the method for obtaining the ammonia nitrogen concentration comprises:

[0016] When K is less than 0.7 and the model prediction value is less than 1.5 mg / L, the ammonia nitrogen concentration in the water is judged to be less than 1.5 mg / L;

[0017] When K≥0.7 and the model predicted value≥1.5mg / L, the water ammonia nitrogen concentration is judged to be ≥1.5mg / L.

[0018] Accordingly, a method for controlling a sewage treatment system using the method comprises the following steps:

[0019] When K is less than 0.7 and the model prediction value is less than 1.5 mg / L, it is judged that the ammonia nitrogen in the water meets the standard and drainage is allowed;

[0020] When K≥0.7, but the model predicted value is <1.5mg / L; or when K<0.7, but the model predicted value is ≥1.5mg / L, it is judged as the intermediate area and the frequency of quantitative detection of ammonia nitrogen concentration needs to be increased;

[0021] When K≥0.7 and the model predicted value≥1.5mg / L, it is judged that the ammonia nitrogen in the water exceeds the standard, and it is necessary to stop drainage immediately and detect the ammonia nitrogen concentration in the current water body.

[0022] Accordingly, a device for calculating the ammonia nitrogen concentration in the effluent water of a sewage treatment system comprises an oxygen consumption rate detection module for detecting the real-time oxygen consumption rate and the maximum oxygen consumption rate of ammonia oxidizing bacteria in the water body;

[0023] The device includes a K value calculation module for calculating and outputting a K value in real time, where K=real-time oxygen consumption rate / maximum oxygen consumption rate;

[0024] The device comprises an ammonia nitrogen concentration estimation module, which is used to obtain the ammonia nitrogen concentration according to the K value.

[0025] Preferably, the device comprises a memory, a processor and a computer program stored in the memory, and the processor executes the computer program to construct and run the artificial neural network model.

[0026] Preferably, the device includes a control device for the sewage treatment system, which is used to control whether the sewage treatment system discharges sewage to the outside according to the K value, or according to the K value and the model prediction value.

[0027] The present invention has the following beneficial effects: Ammonia oxidizing bacteria are the main functional bacteria for removing ammonia nitrogen from water bodies, and their concentration is closely related to the removal of ammonia nitrogen. The present invention first discovered that since ammonia oxidizing bacteria have limited ability to consume ammonia nitrogen, the oxygen consumption rate of ammonia oxidizing bacteria is closely related to the ammonia nitrogen concentration in the water body. The Monod equation for microbial consumption of pollutants is: (r is the substrate degradation rate, r m is the maximum substrate degradation rate, S is the substrate concentration, K m is the half-saturation constant). The oxygen consumption rate of ammonia oxidizing bacteria when utilizing ammonia nitrogen can be calculated by multiplying the ammonia nitrogen consumption rate by the stoichiometric coefficient 18.04. Therefore, the oxygen consumption rate of ammonia oxidizing bacteria consuming ammonia nitrogen and the ammonia nitrogen concentration also conform to the Monod equation and can be expressed as This formula can be further transformed into The left side of the formula is the ratio of the real-time oxygen consumption rate of ammonia oxidizing bacteria to the maximum oxygen consumption rate, and the right side changes with the ammonia nitrogen concentration. mIt is generally a constant, and the empirical value is 1.0 mg / L. Therefore, it is inferred that in the activated sludge sewage treatment system, the ratio of the real-time oxygen consumption rate of ammonia oxidizing bacteria to the maximum oxygen consumption rate can be used to determine the concentration of ammonia nitrogen. If the ammonia nitrogen concentration is too high, then The accuracy of judging the ammonia nitrogen concentration is relatively reduced when K approaches 1. Therefore, the method of the present invention is more suitable for judging the activated sludge sewage treatment system with low effluent ammonia nitrogen concentration (K < 0.8 or K < 0.7).

[0028] The method of the present invention can be used to quickly estimate and calculate the ammonia nitrogen concentration in the water body, and is particularly suitable for quickly detecting and judging whether the ammonia nitrogen content in the effluent of a sewage treatment system meets the standards, and for quickly detecting and judging whether the water quality in the environment meets the standards. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 Error histogram for artificial neural network training;

[0030] Figure 2 Regression plot for artificial neural network training;

[0031] Figure 3 Schematic diagram of the relationship between the relative abundance of ammonia oxidizing bacteria and the effluent ammonia nitrogen concentration. DETAILED DESCRIPTION

[0032] The present invention provides a method for quickly calculating the ammonia nitrogen concentration in effluent from a sewage treatment system, which specifically comprises the following steps:

[0033] 1. Determine the real-time oxygen consumption rate (OUR) of ammonia oxidizing bacteria (AOB) in activated sludge of sewage treatment system rt ) and maximum oxygen consumption rate (OUR max )

[0034] Take a certain volume of activated sludge from the aerobic pool of the sewage treatment system in a container, add sodium chlorate solution at 20-30°C (preferably 25°C) and under continuous stirring. After the addition is completed, the molar concentration of sodium chlorate is 0.02 mol / L. Aerate until it is close to saturation, close the aeration valve to stop aeration, and after the decline rate of dissolved oxygen demand (DO) stabilizes, record DO regularly (recording time 1-3 minutes), calculate the DO decline rate, and record it as R 1 Then add allylthiourea (ATU) solution. After the addition, the ATU concentration is 5 mg / L. Re-aerate until it is close to saturation. Stop aeration. After the DO decrease rate stabilizes, record DO regularly (recording time 1 to 3 minutes) and calculate the DO decrease rate, which is recorded as R. 2 .

[0035] Take the same volume of activated sludge from the aerobic pool of the activated sludge sewage treatment system in a container, keep stirring at 20-30°C (preferably 25°C), add sodium chlorate solution, after the addition, the sodium chlorate concentration is 0.02mol / L, aerate until it is close to saturation, add ammonium chloride solution, make the ammonia nitrogen concentration 5mg / L, and stop aeration. After the DO decrease rate stabilizes, record DO regularly (recording time 1-3min), calculate the DO decrease rate, recorded as R 3 .

[0036] Real-time oxygen consumption rate (OUR rt )=R 1 -R 2 ; Maximum oxygen consumption rate (OUR max )=R 3 -R 2 .

[0037] Here is only one detection OUR rt and OUR max In practice, other existing methods that can detect OUR rt and OUR max or directly use a device that can detect OUR.

[0038] 2. Using AOB’s OUR rt and OUR max Quickly calculate and determine ammonia nitrogen concentration

[0039] K=OUR rt / OUR max , the value interval of K is [0,1].

[0040] The closer the K value is to 0, the lower the ammonia nitrogen concentration in the system effluent is; the closer the K value is to 1, the higher the ammonia nitrogen concentration in the system effluent is. Generally speaking, if the K value is less than 0.7, the ammonia nitrogen concentration in the system effluent is lower than 1.5 mg / L (Class IV water requirements of the Surface Water Environmental Quality Standard, GB3838-2002); if the K value is greater than 0.7, the ammonia nitrogen concentration in the system effluent is likely to be higher than 1.5 mg / L. Therefore, by calculating the K value, the ammonia nitrogen concentration in the test water sample can be quickly estimated to determine whether the ammonia nitrogen concentration meets the discharge standard or other standards.

[0041] Through steps 1 and 2, the current ammonia nitrogen concentration of the water sample can be quickly estimated and judged to meet some practical needs. In order to achieve long-term and stable detection of water quality, or to obtain more accurate results, or when the K value result exceeds the preset value and needs to be further consistent with the ammonia nitrogen concentration in the water sample, the preferred solution is that the method also includes:

[0042] 3. Calculate ammonia nitrogen concentration through artificial neural network model

[0043] Continuous measurement of OUR rt , suspended solids concentration (MLSS), water temperature and effluent ammonia nitrogen concentration data of the aerobic pool, with OUR rt , MLSS, and temperature as input variables, and effluent ammonia nitrogen concentration as output variable. The "Neural Network Fitting APP" in the software established an artificial neural network model. The number of input variables of the artificial neural network model is 3, the number of hidden layers is 1, the number of hidden layer neurons is 6, the number of output variables is 1, the training data accounts for 75%, the verification data accounts for 15%, and the test data accounts for 10%. The data division method is random, and the Levenberg-Marquardt algorithm is used for training. The performance evaluation parameters are the mean square error (MSE) and the determination coefficient R.

[0044] After the model is established and trained, it is used to predict the effluent ammonia nitrogen concentration of the activated sludge sewage treatment system in real time, and further determine whether it exceeds the set value, such as whether it exceeds the standard of 1.5 mg / L (Class IV water requirements of the "Surface Water Environmental Quality Standard", GB3838-2002), or whether it exceeds the standard of 5 mg / L ("Pollutant Emission Standards for Urban Wastewater Treatment Plants", GB 18918-2002, etc.).

[0045] Based on the method for calculating the ammonia nitrogen concentration of the effluent, the present invention also provides a method for quickly determining whether the effluent meets the Class IV water quality standard requirements of the Surface Water Environmental Quality Standard (ammonia nitrogen concentration <1.5 mg / L) and how to perform subsequent treatment, specifically including:

[0046] When K is less than 0.7 and the model prediction value is less than 1.5 mg / L, it is judged that the ammonia nitrogen in the water meets the standard;

[0047] When K≥0.7, but the model predicted value is <1.5mg / L; or when K<0.7, but the model predicted value is ≥1.5mg / L, it is judged as the middle area; this indicates that the judgment of the method of the present invention may be wrong due to some reasons, and there is a risk of exceeding the standard for ammonia nitrogen. It is necessary to increase the frequency of quantitative detection of ammonia nitrogen concentration and pay close attention to whether there is a risk of over-discharge in the effluent;

[0048] When K≥0.7 and the model predicted value≥1.5mg / L, it is judged that the ammonia nitrogen in the effluent exceeds the standard, and it is necessary to stop drainage immediately and detect the ammonia nitrogen concentration of the current effluent.

[0049] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Unless otherwise specified, the technical means used in the embodiments are conventional means well known to those skilled in the art. The data obtained are all average values ​​obtained after at least 3 repetitions, and all the data obtained in each repetition are valid data.

[0050] Example 1: Demonstration of the effect of K value in estimating the concentration of ammonia nitrogen in effluent

[0051] A sewage treatment plant A (plant A uses A / O-sulfur autotrophic process) and a sewage treatment plant B (plant B uses A / O-sulfur autotrophic process) located in a county of Chengdu were selected. 2 / O-MBR process) treated effluent, quickly detect and estimate the effluent ammonia nitrogen concentration.

[0052] 1. Take 5L of the activated sludge mixed liquid at the end of the aerobic tank of sewage plant A and place it in the respiration chamber of the OUR online detector (the equipment in CN211620509U can be used, or other existing equipment that can detect the oxygen consumption rate can be used), stir continuously at 26°C, add sodium chlorate solution to make the sodium chlorate molar concentration 0.02mol / L, aerate until it is close to saturation, stop aeration, and record DO for 3 minutes at regular intervals after the DO decrease rate stabilizes. Calculate the DO decrease rate, which is recorded as R 1 Add allylthiourea (ATU) solution to make the ATU concentration 5 mg / L, re-aerate until close to saturation, stop aeration, wait for DO to decrease steadily, record DO for 3 minutes at a fixed time, calculate the DO decrease rate, and record it as R 2 Take another equal volume of activated sludge mixed liquid and place it in the respiration chamber of the OUR online detector, stir it continuously at 26°C, add sodium chlorate solution to make the sodium chlorate molar concentration 0.02 mol / L, aerate it until it is close to saturation, add ammonium chloride solution to make the ammonia nitrogen concentration about 5 mg / L, stop aeration, and record DO3min regularly after the DO decrease rate stabilizes. Calculate the DO decrease rate, which is recorded as R 3 Calculate K = (R 1 -R 2 ) / (R 3 -R 2 ). At the same time, the effluent from the sedimentation tank was taken according to the hydraulic retention time of the sedimentation tank, and its ammonia nitrogen mass concentration (mg / L) was measured. The results are shown in Table 1.

[0053] Table 1 Comparison table of ammonia nitrogen concentration and K value of sewage treatment plant A

[0054]

[0055] 2. The K value of the effluent from Plant B was determined and calculated according to the method of Plant A. At the same time, the mixed liquid of the activated sludge in the MBR pool was filtered with qualitative filter paper, and the ammonia nitrogen mass concentration of the filtrate was determined. The results are shown in Table 2.

[0056] Table 2 Comparison of ammonia nitrogen concentration and K value of sewage treatment plant B

[0057] Serial number Ammonia nitrogen concentration (mg / L) K 1 0.2784 0.0323 2 0.5333 0.0714 3 0.6706 0.3846 4 0.5333 0.0244 5 0.1216 -0.333 6 0.2392 0.048 7 0.396 0.151 8 2.965 0.951 9 0.573 0.091 10 0.376 0.040 11 0.1608 -0.040 12 0.4745 0.222 13 0.4157 0.3077 14 1.2000 0.5185 15 0.3961 0.1190 16 0.2392 0.2093 17 0.3176 0.0909 18 7.4824 1.1842 19 0.3569 0.0000 20 0.4941 0.2927

[0058] The K values ​​calculated for groups 5 and 11 in Table 2 are negative because the K values ​​at this time are 0 or close to 0. Due to a certain error in measuring OUR, the calculated values ​​are negative. When the calculated K value is negative, it is considered to be K = 0. The results show that in Tables 1 and 2, when the calculated effluent K value is lower than 0.7, the corresponding ammonia nitrogen concentration is basically lower than 1.5 mg / L, which meets the general discharge standards.

[0059] Therefore, the K value can be used to quickly estimate the ammonia nitrogen concentration of the corresponding water body with a high accuracy. By randomly sampling 65 groups of different effluents from two different sewage treatment plants for testing and calculation, only the K values ​​and effluent ammonia nitrogen of groups 9, 21, and 36 in Table 1 did not conform to the rules, with an accuracy rate of up to 95.4%.

[0060] Estimating the ammonia nitrogen concentration through the K value can provide a preliminary reference for rapid detection in practice. The lower the ammonia nitrogen concentration in the water body, the lower the corresponding K value, and the higher the judgment accuracy; as the ammonia nitrogen concentration increases, the K value increases, approaches 1, and the accuracy decreases. However, when the K value is higher than 0.7 or even higher than 0.8, the possibility of ammonia nitrogen exceeding the standard in the water body is relatively high. Although it is impossible to accurately estimate the specific ammonia nitrogen concentration, it is enough to remind the tester that quantitative ammonia nitrogen testing is needed for the current effluent.

[0061] Example 2: Demonstration of the effect of calculating ammonia nitrogen concentration using an artificial neural network model

[0062] The real-time OUR of ammonia oxidizing bacteria at the end of the aerobic pool, the MLSS temperature of the aerobic pool, and the effluent ammonia nitrogen concentration data of the sewage plant A in Example 1 were continuously measured (a total of 94 sets of data were collected). The "Neural Network Fitting APP" in the software established an artificial neural network model with real-time OUR, MLSS concentration, and temperature of ammonia oxidizing bacteria as input variables and effluent ammonia nitrogen concentration as output variable. The number of input variables is 3, the number of hidden layers is 1, the number of hidden layer neurons is 6, the number of output variables is 1, the training data accounts for 75%, the verification data accounts for 15%, and the test data accounts for 10%. The data division method is random, and the Levenberg-Marquardt algorithm is used for training. The performance evaluation parameters are the mean square error (MSE) and the determination coefficient R. The results are shown in Table 3 and Figure 1 , 2 shown. Figure 1 is the error histogram of artificial neural network training, Figure 2 Regression plot for artificial neural network training.

[0063] Table 3 Artificial neural network training results

[0064] Dataset Dataset size MSE R Training set 71 0.4715 0.9711 Validation set 14 0.9513 0.8062 Test Set 9 0.6113 0.9699

[0065] The results show that the artificial neural network training results are good.

[0066] Then, the real-time OUR, MLSS and temperature of ammonia oxidizing bacteria corresponding to the actually measured ammonia nitrogen concentration in Table 1 were used as input variables, and the artificial neural network model obtained after training was used to calculate the ammonia nitrogen concentration in water. The results are shown in Table 4. The unit of ammonia nitrogen concentration is mg / L.

[0067] Table 4 Comparison table of ammonia nitrogen concentration calculated by artificial neural network model and actual ammonia nitrogen concentration data

[0068]

[0069]

[0070] Determine whether the effluent meets the standard according to the following method (taking <1.5mg / L as an example):

[0071] When K is less than 0.7 and the model prediction value is less than 1.5 mg / L, it is judged that the ammonia nitrogen in the water meets the standard;

[0072] When K≥0.7, but the model predicted value is <1.5mg / L; or when K<0.7, but the model predicted value is ≥1.5mg / L, it is judged as the intermediate zone, and the frequency of quantitative detection of ammonia nitrogen concentration needs to be increased, and close attention should be paid to whether there is a risk of over-discharge in the effluent;

[0073] When K≥0.7 and the model predicted value≥1.5mg / L, it is judged that the effluent ammonia nitrogen exceeds the standard, and it is necessary to stop drainage immediately and detect the ammonia nitrogen concentration of the effluent treated by the current sewage treatment system.

[0074] According to the above judgment method, according to the data in Table 4, only the judgment of group 9 is inconsistent with the actual effluent ammonia nitrogen situation, but it is also very close to the critical value of 1.5 mg / L. Compared with the judgment using only the K value, there are three groups 9, 21 and 36 that do not conform to the rules. After the joint model, there is only one group that does not conform to the rules. Only based on the judgment accuracy of sewage treatment plant A, the accuracy rate has increased from 93.3% to 97.8%. When the modeling data volume of the artificial neural network model is larger, the judgment accuracy of the comprehensive indicators will be higher. Therefore, by combining the artificial neural network and K value calculation, it is possible to quickly draw a conclusion on whether the effluent ammonia nitrogen meets the standard.

[0075] Example 3: Principle analysis based on the relationship between relative abundance of microorganisms and ammonia nitrogen removal

[0076] From March to May 2024, the activated sludge mixed liquor at the end of the aerobic pool of sewage treatment plant A was continuously sampled to detect the relative abundance of ammonia oxidizing bacteria and the corresponding effluent ammonia nitrogen concentration, and the maximum OUR of ammonia oxidizing bacteria and the total maximum OUR (the sum of the respiration rates of all microorganisms in a sample) were determined and calculated. During the measurement period, the influent ammonia nitrogen concentration ranged from 21 to 24 mg / L, and the aerobic pool DO was above 2 mg / L. The results are as follows Figure 3 shown.

[0077] according to Figure 3 In the activated sludge of the aerobic pool of the integrated sewage treatment equipment, ammonia oxidizing bacteria are mainly Nitrosomonas, including s_Nitrosomonas_sp._PY1, s_metagenome_g_Nitrosomonas and s_unclassified_g_Nitrosomonas. The ratio of the highest OUR of activated sludge ammonia oxidizing bacteria to the highest OUR of total microorganisms was 0.0355, 0.2237, 0.3388, and 0.4049, respectively. The sum of the relative abundance of various ammonia oxidizing bacteria was 0.0020%, 0.0526%, 0.0716%, and 0.0862%, respectively. The change trends of the two are the same, while the effluent ammonia nitrogen concentrations are 19.5 mg / L, 0.94 mg / L, 0.20 mg / L, and 0.16 mg / L, respectively, and the change trend is opposite to the previous one. It shows that the OUR of ammonia oxidizing bacteria is indeed closely related to the removal of ammonia nitrogen in the water body.

[0078] The embodiments described above are only descriptions of the preferred modes of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various deformations, modifications, and substitutions made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A method for calculating the ammonia nitrogen concentration in the effluent of a sewage treatment system, characterized in that: The method comprises the following steps: (1) Determine the real-time oxygen consumption rate and maximum oxygen consumption rate of ammonia oxidizing bacteria in water; (2) Calculate K, K = real-time oxygen consumption rate / maximum oxygen consumption rate; obtain ammonia nitrogen concentration based on the K value.

2. The method according to claim 1, characterized in that: In step (1), the method for determining the real-time oxygen consumption rate comprises: taking a sample to be tested into a container, adding a sodium chlorate solution, aerating to saturation, and recording the dissolved oxygen demand after the decreasing rate of the dissolved oxygen demand is stable, and calculating the decreasing rate of the dissolved oxygen demand, which is recorded as R1; then adding an allylthiourea solution, aerating to saturation again, and recording the dissolved oxygen demand after the decreasing rate of the dissolved oxygen demand is stable, and calculating the decreasing rate of the dissolved oxygen demand, which is recorded as R2; real-time oxygen consumption rate=R1-R2.

3. The method according to claim 1, characterized in that: In step (1), the method for determining the maximum oxygen consumption rate includes: taking a sample to be tested in a container, adding a sodium chlorate solution, aerating to saturation, and after the decreasing rate of the dissolved oxygen demand is stable, adding an allylthiourea solution, aerating to saturation again, and after the decreasing rate of the dissolved oxygen demand is stable, recording the dissolved oxygen demand, calculating the decreasing rate of the dissolved oxygen demand, and recording it as R2; taking another equal amount of the sample to be tested in a container, adding a sodium chlorate solution, aerating to saturation, adding an ammonium chloride solution, stopping aeration, and after the decreasing rate of the dissolved oxygen demand is stable, recording DO, calculating the decreasing rate of the dissolved oxygen demand, and recording it as R3; the maximum oxygen consumption rate = R3-R2.

4. The method according to claim 1, characterized in that: The method for obtaining the ammonia nitrogen concentration includes: if K is less than 0.7, it is judged that the ammonia nitrogen concentration of the water is less than 1.5 mg / L.

5. The method according to claim 1, characterized in that: The method further comprises: (3) Predicting ammonia nitrogen concentration through artificial neural network model: taking the real-time oxygen consumption rate, suspended solid matter concentration and temperature of the water sample to be tested as input variables and the ammonia nitrogen concentration of the effluent water body as output variable, The "Neural Network Fitting APP" in the software establishes an artificial neural network model; the number of input variables of the artificial neural network model is 3, the number of hidden layers is 1, the number of hidden layer neurons is 6, and the number of output variables is 1. The Levenberg-Marquardt algorithm is used for training. After the model is established and trained, the model prediction value is obtained in real time using the model: effluent ammonia nitrogen concentration.

6. The method according to claim 5, characterized in that: Methods for obtaining ammonia nitrogen concentration include: When K is less than 0.7 and the model prediction value is less than 1.5 mg / L, the ammonia nitrogen concentration in the water is judged to be less than 1.5 mg / L; When K≥0.7 and the model predicted value≥1.5mg / L, the water ammonia nitrogen concentration is judged to be ≥1.5mg / L.

7. A method for controlling a sewage treatment system using the method according to any one of claims 1 to 6, characterized in that: The steps include: When K is less than 0.7 and the model prediction value is less than 1.5 mg / L, it is judged that the ammonia nitrogen in the water meets the standard and drainage is allowed; When K≥0.7, but the model predicted value is <1.5mg / L; or when K<0.7, but the model predicted value is ≥1.5mg / L, it is judged as the intermediate area and the frequency of quantitative detection of ammonia nitrogen concentration needs to be increased; When K≥0.7 and the model predicted value≥1.5mg / L, it is judged that the ammonia nitrogen in the water exceeds the standard, and it is necessary to stop drainage immediately and detect the ammonia nitrogen concentration in the current water body.

8. A device for calculating the ammonia nitrogen concentration in the effluent of a sewage treatment system, characterized in that: The device includes an oxygen consumption rate detection module for detecting the real-time oxygen consumption rate and the maximum oxygen consumption rate of ammonia oxidizing bacteria in the water body; The device includes a K value calculation module for calculating and outputting a K value in real time, where K=real-time oxygen consumption rate / maximum oxygen consumption rate; The device comprises an ammonia nitrogen concentration estimation module, which is used to obtain the ammonia nitrogen concentration according to the K value.

9. The device according to claim 8, characterized in that: The device includes a memory, a processor and a computer program stored in the memory, and the processor executes the computer program to construct and run the artificial neural network model of claim 5.

10. The device according to claim 8 or 9, characterized in that: The device comprises a control device for a sewage treatment system, which is used to control whether the sewage treatment system discharges sewage externally according to a K value, or according to a K value and a model prediction value.

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