CN116253446A - Intelligent aeration setting method for sewage treatment - Google Patents
Intelligent aeration setting method for sewage treatment Download PDFInfo
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- 238000005273 aeration Methods 0.000 title claims abstract description 160
- 239000010865 sewage Substances 0.000 title claims abstract description 61
- 238000011282 treatment Methods 0.000 title claims abstract description 59
- 238000000034 method Methods 0.000 title claims abstract description 42
- XKMRRTOUMJRJIA-UHFFFAOYSA-N ammonia nh3 Chemical compound N.N XKMRRTOUMJRJIA-UHFFFAOYSA-N 0.000 claims abstract description 52
- 230000004044 response Effects 0.000 claims abstract description 25
- 230000008569 process Effects 0.000 claims abstract description 11
- 238000005457 optimization Methods 0.000 claims abstract description 8
- 238000012216 screening Methods 0.000 claims abstract description 5
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 117
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 60
- 229910052760 oxygen Inorganic materials 0.000 claims description 60
- 239000001301 oxygen Substances 0.000 claims description 60
- 239000000126 substance Substances 0.000 claims description 30
- 229910052757 nitrogen Inorganic materials 0.000 claims description 13
- 238000010992 reflux Methods 0.000 claims description 10
- ORILYTVJVMAKLC-UHFFFAOYSA-N Adamantane Natural products C1C(C2)CC3CC1CC2C3 ORILYTVJVMAKLC-UHFFFAOYSA-N 0.000 claims description 2
- 230000002068 genetic effect Effects 0.000 claims description 2
- 230000007547 defect Effects 0.000 abstract description 2
- 206010063385 Intellectualisation Diseases 0.000 abstract 1
- 238000005265 energy consumption Methods 0.000 description 11
- 230000000875 corresponding effect Effects 0.000 description 10
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 8
- 238000013461 design Methods 0.000 description 8
- 238000004364 calculation method Methods 0.000 description 6
- 230000000694 effects Effects 0.000 description 5
- 238000006243 chemical reaction Methods 0.000 description 4
- 230000009467 reduction Effects 0.000 description 4
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 3
- 229910052799 carbon Inorganic materials 0.000 description 3
- 230000001276 controlling effect Effects 0.000 description 3
- 239000010802 sludge Substances 0.000 description 3
- 239000002699 waste material Substances 0.000 description 3
- VHUUQVKOLVNVRT-UHFFFAOYSA-N Ammonium hydroxide Chemical compound [NH4+].[OH-] VHUUQVKOLVNVRT-UHFFFAOYSA-N 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000012937 correction Methods 0.000 description 2
- 230000002596 correlated effect Effects 0.000 description 2
- 230000005611 electricity Effects 0.000 description 2
- VTEIFHQUZWABDE-UHFFFAOYSA-N 2-(2,5-dimethoxy-4-methylphenyl)-2-methoxyethanamine Chemical compound COC(CN)C1=CC(OC)=C(C)C=C1OC VTEIFHQUZWABDE-UHFFFAOYSA-N 0.000 description 1
- 241000894006 Bacteria Species 0.000 description 1
- 230000006978 adaptation Effects 0.000 description 1
- 238000007792 addition Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 230000008713 feedback mechanism Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011369 optimal treatment Methods 0.000 description 1
- 230000035515 penetration Effects 0.000 description 1
- 238000011897 real-time detection Methods 0.000 description 1
- 238000007711 solidification Methods 0.000 description 1
- 230000008023 solidification Effects 0.000 description 1
- 239000002352 surface water Substances 0.000 description 1
- 239000000725 suspension Substances 0.000 description 1
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- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F7/00—Aeration of stretches of water
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- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F3/00—Biological treatment of water, waste water, or sewage
- C02F3/02—Aerobic processes
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
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- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F2101/00—Nature of the contaminant
- C02F2101/10—Inorganic compounds
- C02F2101/16—Nitrogen compounds, e.g. ammonia
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- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F2209/00—Controlling or monitoring parameters in water treatment
- C02F2209/001—Upstream control, i.e. monitoring for predictive control
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- Y02W—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO WASTEWATER TREATMENT OR WASTE MANAGEMENT
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Abstract
The invention discloses an intelligent aeration setting method for sewage treatment, and relates to the technical field of sewage treatment. The method comprises the steps of accumulating relevant parameters of a sewage treatment system, screening obvious response variables related to aeration quantity, using a data fitting tool to take the aeration quantity as an output value and the response variables as input values to form an aeration quantity theoretical function model, and correcting the theoretical aeration quantity obtained by the theoretical function model by using the difference value between actual effluent ammonia nitrogen and set effluent ammonia nitrogen after the system enters an intelligent operation period to obtain the actual aeration quantity. And (3) constructing a loss function between the actual aeration quantity and the theoretical aeration quantity at fixed intervals, correcting the correlation coefficient of the theoretical function model through an optimization algorithm, and minimizing the loss function. The invention solves the defects of poor process adaptability, inaccurate control and the like caused by the fact that the traditional sewage treatment aeration method depends on specific response parameters and fixed theoretical models. By self-selecting response parameters, a function model is automatically generated, and the refinement and the intellectualization of aeration are realized.
Description
Technical Field
The invention relates to the field of sewage treatment, in particular to an intelligent aeration setting method for sewage treatment.
Background
The carbon emission of the sewage treatment industry in China accounts for 1-2% of the total emission of the whole society, and the first ten carbon emission industries and the first ten energy consumption units are non-negligible emission reduction fields. Sewage treatment involves energy consumption mainly from the electricity consumed by aeration. The conventional manual control has the defects of rough management, lag adjustment and the like, is not beneficial to the system to reach the standard stably on one hand, and has great waste of electricity consumption on the other hand. The sewage treatment adopts an automatic control system to reduce the waste of energy consumption through fine control to a certain extent.
The related aspects of the prior art are mainly studied:
CN104914897a reports an aeration control method of a continuous activated sludge process by obtaining the current influent chemical oxygen demand and the current influent flow rate of sewage in a reaction tank, as the current influent chemical oxygen demand and the current influent flow rate, respectively; according to the data reflecting the relation between the chemical oxygen demand of the sewage in the reaction tank and the water inflow and the standard air quantity, obtaining the standard air quantity corresponding to the current water inflow chemical oxygen demand and the current water inflow as the current standard air quantity; and adjusting the operation frequency of the air blower in the reaction tank to the current standard reaching frequency corresponding to the current standard reaching air quantity for aeration. Thereby realizing the purpose of achieving sewage treatment indexes with the lowest energy consumption. The method only considers the chemical oxygen demand of the inlet water and the flow rate of the inlet water as response parameters of aeration air quantity, ignores the complexity of an actual sewage disposal system and has a plurality of influencing factors, and potential response parameters such as the inlet water flow rate, the inlet water total nitrogen, the inlet water ammonia nitrogen, the inlet water COD, the reflux ratio, the temperature, the dissolved oxygen, the outlet water total nitrogen, the outlet water ammonia nitrogen, the outlet water COD, the outlet water BOD and the like can influence the aeration quantity, so that the aeration refinement degree is weaker.
CN 215161450U reports a rapid intelligent aeration system, which combines the real-time detection of dissolved oxygen content in a reactor by presetting a control dissolved oxygen target value, and adjusts the dosage of an aeration device to the reactor according to the difference between the set dissolved oxygen target value and the actual value. The essence is to control the change of the air quantity of the fan so as to ensure the concentration of the dissolved oxygen of the system to be constant. The method has the following problems that under the condition of different water quality concentrations of inflows, the optimal dissolved oxygen content required by an actual sewage treatment system is different, and particularly, the method has higher requirements on the process of the transmission of the oxygen by the medium such as a biomembrane method, when the water quality concentration is too high, the dissolved oxygen of the system needs to be controlled to reach higher level so as to ensure that the penetration depth of the oxygen in the biomembrane is increased, and when the water quality concentration is too low, the dissolved oxygen can be controlled to be at lower level so as to reduce the energy consumption of dosing as much as possible. Therefore, even the same sewage treatment system can realize the optimal treatment working condition of the system by controlling the difference of the concentration of the dissolved oxygen when the concentration of the water quality changes.
CN 110577275B reports an intelligent aeration control system and method for sewage treatment, wherein aeration control logic calculates the dosage by combining the oxygen demand and the aeration quantity conversion formula based on the aeration quantity formula in the outdoor drainage design standard (GB 50014-2006), and adjusts every more than 10 minutes. Firstly, in the actual operation process of a calculation model, the control of the oxygen utilization rate, the heterotrophic bacteria attenuation coefficient and the sludge yield correction coefficient is difficult, and real-time calculation cannot be realized; secondly, the precision and the type of the instrument are required to be higher, and project investment is increased.
CN104671462a reports a sewage treatment energy-saving control method based on a bivariate two-dimensional table, by establishing a bivariate two-dimensional table n taking the respectively gradually increased inflow and the water chemical oxygen demand as variables, intercepting the current inflow and the current water chemical oxygen demand at the same time periodically; according to the two, carrying out cross positioning in a bivariate two-dimensional table n to obtain a theoretical output frequency value of the current blower and the theoretical workbench number of the blower; and adjusting the output frequency of the blower and the actual workbench number in real time according to the current theoretical output frequency value and the theoretical workbench number. According to the invention, firstly, only chemical oxygen demand is selected as a response parameter in parameter selection, the representativeness is poor, the coverage is small, and the correlation with the aeration quantity is difficult to obtain; and secondly, the theoretical aeration rate is obtained through a two-dimensional table of the correlation between the aeration rate and the chemical oxygen demand, so that the adaptability is poor, and the variable operation environment of sewage treatment is difficult to meet.
CN104671462a reports a sewage treatment energy-saving control method based on a bivariate two-dimensional table, by establishing a bivariate two-dimensional table n taking the respectively gradually increased inflow and the water chemical oxygen demand as variables, intercepting the current inflow and the current water chemical oxygen demand at the same time periodically; according to the two, carrying out cross positioning in a bivariate two-dimensional table n to obtain a theoretical output frequency value of the current blower and the theoretical workbench number of the blower; and adjusting the output frequency of the blower and the actual workbench number in real time according to the current theoretical output frequency value and the theoretical workbench number. Thereby realizing intelligent control of the variable frequency speed regulation of the blower and avoiding the resource waste caused by incomplete or overlarge sewage treatment when the blower frequency is too small. According to the invention, firstly, only the chemical oxygen demand is selected as a corresponding parameter in parameter selection, the representativeness is poor, the coverage is small, and the correlation with the aeration quantity is difficult to obtain; and secondly, the theoretical aeration rate is obtained through a two-dimensional table of the correlation between the aeration rate and the chemical oxygen demand, so that the adaptability is poor, and the variable operation environment of sewage treatment is difficult to meet.
CN114132980a reports a short-range intelligent accurate aeration control method, equipment and system for sewage treatment, the system uses parameters such as water inflow, total nitrogen, ammonia nitrogen and BOD as response variables, adopts a fixed theoretical formula to construct a model, generates theoretical aeration, constructs a feedback and compensation mechanism through a loss function, forms actual aeration, and realizes energy consumption reduction through refined aeration control. On the one hand, the patent also faces the problem of too few types of response variables, and on the other hand, the feedback mechanism is controlled by using a stepwise fixed value, so that the accuracy is poor. In addition, since a fixed model is adopted, the system parameters which are changed in stages cannot be flexibly dealt with, and therefore, the actual use effect may be greatly different from the envisaged effect.
As can be seen, the current automatic control system for sewage treatment has the following problems: first, there are problems in response parameter selection such as solidification and poor representativeness. The factors influencing the aeration rate of sewage treatment need specific analysis, and different conditions and different processes can lead to the difference of corresponding parameters related to the aeration rate, so that the response parameters of the aeration rate of the automatic control system really meeting the requirements of the sewage treatment industry are selected by the system. Secondly, in the control logic, the system stability is expected to be realized by controlling constant DO concentration, and when the actual inflow water quality fluctuates greatly, the optimal DO of the system also changes, so that the system is unfavorable for ensuring the stability of the outflow water to reach the standard and realizing energy conservation and consumption reduction; in addition, in terms of control algorithm, most of the current automatic control systems adopt a model based on an International Water Assistant (IWA) ASM model or an outdoor drainage design standard (GB 50014-2006) to form a fixed model, on one hand, the model is formed by aiming at the activated sludge method with a larger application scale at present, but with the development of industry, the process advantages of a biomembrane method and the like are gradually highlighted, and the adaptability of the model needs to be further verified. On the other hand, the adaptability of the fixed model to different sewage treatment processes may be different; in summary, in order to further optimize the existing intelligent aeration setting method for sewage treatment, thereby further realizing energy saving and consumption reduction of sewage treatment, it is necessary to improve the intelligent aeration setting method of the existing sewage treatment system.
Disclosure of Invention
The invention aims to provide an intelligent aeration setting method for sewage treatment, which is characterized in that a theoretical function model of aeration rate is formed by using a data fitting tool through correlation calculation of relevant operation parameters and aeration rate of a sewage treatment system, the actual aeration rate is obtained by setting theoretical aeration rate obtained by correcting the theoretical function model of effluent ammonia nitrogen after the system enters an intelligent operation period, a loss function between the actual aeration rate and the theoretical aeration rate is constructed at fixed time intervals, and the correlation coefficient of the theoretical function model is corrected through an optimization algorithm, so that the loss function is minimum.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
an intelligent aeration setting method for sewage treatment comprises the following steps:
step 1: according to the sewage treatment system, setting and controlling the ammonia nitrogen index of the effluent to be N c According to the actual running condition of the sewage treatment system, collecting related running parameter data in the aerobic tank to form a parameter sequence; the related operation parameter data comprise aeration quantity, water inflow quantity, dissolved oxygen quantity, ammonia nitrogen concentration of inflow water, suspension concentration of inflow water, TN concentration of inflow water, chemical oxygen demand of outflow water, TN concentration of outflow water, reflux ratio, temperature, air pressure and the like.
The selection of other relevant operation parameters except the aeration amount is not fixed, and can be performed according to actual conditions.
Step 2: confirming parameter correlation; because the influence degree of each parameter on the aeration rate is different, the pearson correlation coefficient, the cosine similarity, the Euclidean distance and the Sperman rank correlation coefficient between the aeration rate and other relevant operation parameters are required to be calculated, and the average value of the pearson correlation coefficient, the cosine similarity, the Euclidean distance and the Sperman rank correlation coefficient is used as the correlation coefficient between the aeration rate and each relevant operation parameter, and the influence of each relevant operation parameter on the aeration rate is judged according to the magnitude of the correlation coefficient.
Step 3: response parameter screening; and reserving the relevant operation parameters with large influence on the aeration quantity as response parameters, and deleting the relevant operation parameters with small influence on the aeration quantity so as to reduce analysis data and calculated quantity as much as possible.
Step 4: fitting a model; and taking the aeration quantity in the aerobic tank as an output value, taking the reserved response parameter as an input value, and utilizing a data fitting tool to fit and form a theoretical aeration quantity function model of the aerobic tank.
Step 5: obtaining theoretical aeration quantity; the sewage treatment system enters an intelligent operation period, and the theoretical aeration quantity Q of the aerobic tank is automatically obtained by utilizing a theoretical aeration quantity function model T 。
Step 6: correcting theoretical aeration quantity; the actual ammonia nitrogen value of the effluent of the aerobic tank is measured to be N e To control the ammonia nitrogen index N of the effluent c For reference, the feedback compensation is performed when |N e -N c |≤10％N c When the system is used, the system outlet water is considered to fluctuate around the control value in a small amplitude and does not have the risk of exceeding the standard, so that the feedback compensation is not carried out; when N is e -N c >10％N c When the ammonia nitrogen concentration of the system effluent is considered to be higher, the risk of exceeding the standard possibly exists, and the aeration quantity is further adjusted upwards on the basis of theoretical aeration quantity by setting the compensation coefficient k to be 1.2; when N is c -N e >10％N c Considering that the ammonia nitrogen concentration of the effluent of the system is too low, in order to save the aeration energy consumption, a compensation coefficient k can be set to be 0.9, and the aeration quantity is further adjusted downwards on the basis of theoretical aeration quantity; output the actual aeration quantity Q A＝ kQ T 。
Step 7: correcting the model; constructing Q with data accumulated in this stage every 30 days A -Q T And (3) correcting the correlation coefficient of the theoretical aeration quantity function model by adopting an optimization algorithm to minimize the loss function, and repeating the steps 5 to 7.
Preferably, step 1 specifically includes:
in the process of accumulating the original data, the related operation parameter data of the aerobic tank is collected for not less than 30 days, and the related operation parameter data collected each time is a group of related operation parameter data.
Preferably, step 2 specifically includes:
calculating a pearson correlation coefficient, a cosine similarity, a Euclidean distance and a Sperman rank correlation coefficient between the aeration quantity and the dissolved oxygen quantity, taking the average value of the pearson correlation coefficient, the cosine similarity, the Euclidean distance and the Sperman rank correlation coefficient as the correlation coefficient between the aeration quantity and the dissolved oxygen quantity, and judging the influence of the dissolved oxygen quantity on the aeration quantity according to the magnitude of the correlation coefficient;
calculating the correlation coefficients of aeration quantity and water inflow quantity, water inflow ammonia nitrogen concentration, water inflow suspended matter concentration, water inflow TN concentration, water inflow chemical oxygen demand, water outflow TN concentration, reflux ratio, temperature, air pressure and the like by the same method, and judging the influence of the water inflow quantity, water inflow ammonia nitrogen concentration, water inflow suspended matter concentration, water inflow TN concentration, water inflow chemical oxygen demand, water outflow TN concentration, reflux ratio, temperature, air pressure on the aeration quantity;
all the obtained correlation coefficients are arranged into a correlation coefficient column from large to small, the first 80% in the correlation coefficient column is taken as a significant correlation coefficient, and the correlation operation parameter data corresponding to the significant correlation coefficient is the correlation operation parameter data with great influence on the aeration amount; and taking the later 20% of the relation series as an insignificant correlation coefficient, wherein the correlated operation parameter data corresponding to the insignificant correlation coefficient is correlated operation parameter data with small influence on the aeration amount.
Preferably, the data fitting tool described in step 4 is recommended as 1stop software or the like.
Preferably, the ammonia nitrogen value of the effluent is designed to be N s The ammonia nitrogen index N of the discharged water is controlled c Should satisfy N s -N c ≥30％N s . The standard ammonia nitrogen index of the effluent is controlled to be a standard value set in the sewage treatment system, which is smaller than the ammonia nitrogen value N of the effluent s 。
Preferably, the optimization algorithm adopted in the step 7 is gradient descent or Adam algorithm or genetic algorithm, etc.
Compared with the prior art, the invention has the following beneficial technical effects:
1) The water outlet standard is high; through the fine aeration quantity regulation and control, the ammonia nitrogen index can be stably superior to the II-type water standard of the surface water environment quality standard (GB 3838-2002), and the high-standard discharge and stable standard of a sewage plant can be met.
2) The effluent quality is stable and the impact resistance is strong; the water quality variation coefficient (the ratio of standard deviation to average value, CV) of the water outlet is less than 0.3 and is lower than that of the water inlet.
3) The running cost is low; through intelligent control, the sewage treatment aeration according to the needs is realized, and compared with the traditional manual control, the aeration energy consumption can be reduced by more than 20%.
4) The process adaptability is good; the algorithm model does not depend on the existing international water assistant ASM or (GB 50014-2006) inherent model, but is self-fitted after the original data are accumulated, so that the algorithm model is more in line with the actual requirements of a specific sewage treatment system, and can be suitable for different process types.
5) The control is more intelligent, the effect is more stable, and the energy consumption and the carbon emission are lower; the actual aeration quantity is corrected according to the feedback compensation, and the control is more accurate, so that the aeration according to the requirement of sewage treatment is further improved, and the aeration energy consumption is saved; in addition, every time a period of time, according to the operation condition correction algorithm model in the stage, the control is more accurate.
Drawings
FIG. 1 is a flow chart of the present invention.
FIG. 2 shows the ammonia nitrogen concentration of the inlet and outlet water of the sewage treatment system in the intelligent control period of the aeration quantity in the embodiment 1.
FIG. 3 is the ammonia nitrogen concentration of the inlet and outlet water of the sewage treatment system in the aeration rate manual control period in example 2.
FIG. 4 shows the ammonia nitrogen concentration of the inlet and outlet water of the sewage treatment system in the intelligent control period of the aeration quantity in the embodiment 2.
Detailed Description
The following description of the embodiments of the invention will be given with reference to the accompanying drawings and examples:
example 1
The water quality of the water inlet and outlet of a certain sewage treatment project is designed as shown in table 1, wherein the water quantity of the sewage treatment project is designed to be 1 ten thousand tons/d. The intelligent aeration setting is carried out by adopting the following steps, and the ammonia nitrogen index of the discharged water is set to be 3mg/L.
TABLE 1 design of inlet and outlet Water quality for certain Sewage treatment project
| Index (I) | COD cr | SS | TN | NH 3 -N | TP | pH |
| Design of water inflow | 500 | 260 | 72 | 60 | 8.0 | 6～9 |
| |
40 | 10 | 15 | 5 | 0.4 | 6～9 |
Step 1: accumulating original data; according to the actual operation condition of the sewage treatment system, operation data including aeration quantity, water inflow quantity, dissolved oxygen quantity, water inflow ammonia nitrogen concentration, water inflow suspended matter concentration, water inflow chemical oxygen demand, water outflow TN concentration, reflux ratio, temperature, air pressure and the like are collected for 100 continuous days.
Step 2: confirming parameter correlation; and calculating a pearson correlation coefficient, a cosine similarity, a Euclidean distance and a Sperman rank correlation coefficient between the aeration quantity and each related operation parameter, taking an average value of the pearson correlation coefficient, the cosine similarity, the Euclidean distance and the Sperman rank correlation coefficient as the correlation coefficient between the aeration quantity and each related operation parameter, and judging the influence of each related operation parameter on the aeration quantity according to the magnitude of the correlation coefficient.
Specifically, a pearson correlation coefficient, a cosine similarity, a Euclidean distance and a Sperman rank correlation coefficient between the aeration rate and the water inflow are calculated, an average value of the pearson correlation coefficient, the cosine similarity, the Euclidean distance and the Sperman rank correlation coefficient is used as the correlation coefficient between the aeration rate and the water inflow, and the influence of the water inflow on the aeration rate is judged according to the magnitude of the correlation coefficient.
The correlation coefficient between aeration quantity and inflow water quantity, dissolved oxygen quantity, inflow ammonia nitrogen concentration, inflow suspended matter concentration, inflow chemical oxygen demand, outflow TN concentration, reflux ratio, temperature and air pressure and the like is calculated by the same method, and the influence of inflow water quantity, dissolved oxygen quantity, inflow ammonia nitrogen concentration, inflow suspended matter concentration, inflow chemical oxygen demand, outflow TN concentration, reflux ratio, temperature and air pressure and the like on the aeration quantity is judged.
All the obtained average correlation coefficients are arranged into a correlation coefficient row from large to small, the first 80% in the phase relation number row is taken as a significant correlation coefficient, and the correlation operation parameter data corresponding to the significant correlation coefficient is the correlation operation parameter data with great influence on the aeration amount; the last 20% of the phase relation series are non-obvious correlation coefficients, and the relevant operation parameter data corresponding to the non-obvious correlation coefficients are relevant operation parameter data with small influence on the aeration quantity.
Step 3: response parameter screening; the calculation result of the correlation coefficient is shown in table 2, wherein the temperature and the air pressure are the related operation parameter data with small influence on the aeration amount, and the related operation parameter data are deleted; and the other data are relevant operation parameter data with great influence on the aeration amount, and are response parameters.
TABLE 2 correlation coefficients between aeration and various related operating parameters
Step 4: fitting a model; and taking the aeration quantity in the aerobic tank as an output value, taking the reserved response parameter as an input value, and fitting by using 1stop software to form a theoretical aeration quantity function model of the aerobic tank.
Step 5: obtaining theoretical aeration quantity; the sewage treatment system enters an intelligent operation period, and the theoretical aeration quantity Q of the aerobic tank is automatically obtained by utilizing a theoretical aeration quantity function model T 。
Step 6: correcting theoretical aeration quantity; performing feedback compensation by taking the set effluent ammonia nitrogen as a reference, and when the actual effluent ammonia nitrogen value of the aerobic tank>When the concentration is 3.3mg/L, setting the compensation coefficient k to be 1.2; when the ammonia nitrogen value of the actual effluent of the aerobic tank<2.7mg/L, and setting the compensation coefficient k to be 0.9; output the actual aeration quantity Q A ＝kQ T 。
Step 7: correcting the model; constructing Q with data accumulated in this stage at 1 quarter interval A -Q T And (3) correcting the correlation coefficient of the theoretical aeration quantity function model by adopting an optimization algorithm to minimize the loss function, and repeating the steps 5 to 7.
The operation result of the intelligent control period is shown in fig. 2, and from the water outlet effect, the system continuously operates for more than 340 days, the ammonia nitrogen in the water inlet during the operation period shows obvious fluctuation, the average value and standard deviation of the ammonia nitrogen in the water inlet are respectively 44.44mg/L and 13.05, and the accounting variation coefficient CV reaches 0.29. The ammonia nitrogen in the effluent is stably lower than the effluent standard, the average value and standard deviation of the ammonia nitrogen in the effluent are respectively 2.71mg/L and 0.71, and the calculated variation coefficient CV is 0.26; from the aspect of operation cost, the project is controlled intelligently and finely, so that the on-site operation and maintenance difficulty is reduced, and the power consumption is reduced by 24% compared with manual control.
Example 2
The water treatment project of some micro-polluted water is designed to have 130 ten thousand tons/d of treated water, and the water quality of inlet and outlet water of the project is designed as shown in Table 3. The intelligent aeration setting is carried out by adopting the following steps, and the ammonia nitrogen index of the discharged water is set to be 0.3mg/L.
TABLE 3 design of inlet and outlet Water quality for a slightly polluted Water treatment project
| Project | CODcr | SS | TP | NH3-N |
| Design of |
40 | 60 | 1.5 | 6.0 |
| Design effluent | 30 | 50 | 1.0 | 0.5 |
Step 1: accumulating original data; according to the actual running condition of the sewage treatment system, running data including aeration quantity, water inflow quantity, dissolved oxygen quantity, water inflow ammonia nitrogen concentration, water inflow chemical oxygen demand, water outflow chemical oxygen demand, temperature and other parameters are collected for 60 continuous days.
Step 2: confirming parameter correlation; and calculating a pearson correlation coefficient, a cosine similarity, a Euclidean distance and a Sperman rank correlation coefficient between the aeration quantity and each related operation parameter, taking an average value of the pearson correlation coefficient, the cosine similarity, the Euclidean distance and the Sperman rank correlation coefficient as the correlation coefficient between the aeration quantity and each related operation parameter, and judging the influence of each related operation parameter on the aeration quantity according to the magnitude of the correlation coefficient.
Specifically, a pearson correlation coefficient, a cosine similarity, a Euclidean distance and a Sperman rank correlation coefficient between the aeration rate and the water inflow are calculated, an average value of the pearson correlation coefficient, the cosine similarity, the Euclidean distance and the Sperman rank correlation coefficient is used as the correlation coefficient between the aeration rate and the water inflow, and the influence of the water inflow on the aeration rate is judged according to the magnitude of the correlation coefficient.
The correlation coefficient between the aeration quantity and the dissolved oxygen quantity, the concentration of the inflowing ammonia nitrogen, the chemical oxygen demand of the inflowing water, the chemical oxygen demand of the outflowing water, the temperature and the like is calculated by the same method, and the influence of the dissolved oxygen quantity, the concentration of the inflowing ammonia nitrogen, the chemical oxygen demand of the inflowing water, the chemical oxygen demand of the outflowing water, the temperature and the like on the aeration quantity is judged.
All the obtained correlation coefficients are arranged into a correlation coefficient column from large to small, the first 80% in the correlation coefficient column is taken as a significant correlation coefficient, and the correlation operation parameter data corresponding to the significant correlation coefficient is the correlation operation parameter data with great influence on the aeration amount; the last 20% of the phase relation series are non-obvious correlation coefficients, and the relevant operation parameter data corresponding to the non-obvious correlation coefficients are relevant operation parameter data with small influence on the aeration quantity.
Step 3: response parameter screening; the calculation result of the response coefficient is shown in table 4, and according to the calculation result, the dissolved oxygen is the related operation parameter data with small influence on the aeration amount, and the dissolved oxygen is deleted; and the other data are relevant operation parameter data with great influence on the aeration amount, and are response parameters.
TABLE 4 correlation coefficient between aeration and various related operating parameters
| Operating parameters | Water inflow amount | Dissolved oxygen amount | Chemical oxygen demand of incoming water | Concentration of ammonia nitrogen in feed water | Chemical oxygen demand of effluent | Temperature (temperature) |
| Response coefficient | 0.57 | 0.15 | 0.66 | 0.69 | 0.24 | 0.27 |
| Whether or not to reserve | Is that | Whether or not | Is that | Is that | Is that | Is that |
Step 4: fitting a model; and taking the aeration quantity in the aerobic tank as an output value, taking the reserved response parameter as an input value, and fitting by using 1stop software to form a theoretical aeration quantity function model of the aerobic tank.
Step 5: obtaining theoretical aeration quantity; the sewage treatment system enters an intelligent operation period, and the theoretical aeration quantity Q of the aerobic tank is automatically obtained by utilizing a theoretical aeration quantity function model T 。
Step 6: correcting theoretical aeration quantity; performing feedback compensation by taking the set effluent ammonia nitrogen as a reference, and when the actual effluent ammonia nitrogen value of the aerobic tank>When the concentration is 0.33mg/L, the compensation coefficient k is set to be 1.2; when the ammonia nitrogen value of the actual effluent of the aerobic tank<0.27mg/L, and setting the compensation coefficient k to be 0.9; output the actual aeration quantity Q A ＝kQ T 。
Step 7: correcting the model; constructing Q with data accumulated in this stage every 30 days A -Q T And (3) correcting the correlation coefficient of the theoretical aeration quantity function model by adopting an optimization algorithm to minimize the loss function, and repeating the steps 5 to 7.
The intelligent control period operation result is shown in fig. 4, and the manual control period operation result is shown in fig. 3. During manual control, the average value and standard deviation of the ammonia nitrogen in the water are respectively 1.92mg/L and 0.59, and the calculated variation coefficient CV reaches 0.31. The ammonia nitrogen mean value and standard deviation of the effluent are respectively 0.27mg/L and 0.11, and the calculated variation coefficient CV is 0.42; after intelligent control is changed, the system continuously runs for approximately 1 year, the ammonia nitrogen in the water inlet presents obvious fluctuation during the running period, the average value and standard deviation of the ammonia nitrogen in the water inlet are respectively 2.50mg/L and 070, and the accounting variation coefficient CV reaches 0.28. The ammonia nitrogen in the effluent is stable and reaches the standard, the average value and the standard deviation are respectively 0.28mg/L and 0.07, and the calculated variation coefficient CV is 0.26. Compared with manual control, the water quality of the effluent in the intelligent control period is more stable; from the operation cost, the aeration power consumption is saved by 20% through intelligent aeration.
It should be understood that the above description is not intended to limit the invention to the particular embodiments disclosed, but to limit the invention to the particular embodiments disclosed, and that the invention is not limited to the particular embodiments disclosed, but is intended to cover modifications, adaptations, additions and alternatives falling within the spirit and scope of the invention.
Claims (6)
1. The intelligent aeration setting method for sewage treatment is characterized by comprising the following steps of:
step 1: accumulating original data; according to the sewage treatment system, setting and controlling the ammonia nitrogen index of the effluent to be N c According to the actual running condition of the sewage treatment system, collecting related running parameter data in the aerobic tank to form a parameter sequence; the related operation parameter data comprise aeration quantity, water inflow quantity, dissolved oxygen quantity, water inflow ammonia nitrogen concentration, water inflow suspended matter concentration, water inflow TN concentration, water inflow chemical oxygen demand, water outflow TN concentration, reflux ratio, temperature and air pressure;
step 2: confirming parameter correlation; calculating pearson correlation coefficient, cosine similarity, euclidean distance and Sperman rank correlation coefficient between the aeration quantity and other relevant operation parameters, taking average values of the pearson correlation coefficient, the cosine similarity, the Euclidean distance and the Sperman rank correlation coefficient as the correlation coefficient between the aeration quantity and the relevant operation parameters, and judging the influence of the relevant operation parameters on the aeration quantity according to the correlation coefficient;
step 3: response parameter screening; the relevant operation parameters with large influence on the aeration quantity are reserved as response parameters, and the relevant operation parameters with small influence on the aeration quantity are deleted;
step 4: fitting a model; taking the aeration quantity in the aerobic tank as an output value, taking the reserved response parameter as an input value, and utilizing a data fitting tool to fit and form a theoretical aeration quantity function model of the aerobic tank;
step 5: obtaining theoretical aeration quantity; the sewage treatment system enters an intelligent operation period, and the theoretical aeration quantity Q of the aerobic tank is automatically obtained by utilizing a theoretical aeration quantity function model T ；
Step 6: correcting theoretical aeration quantity; the actual ammonia nitrogen value of the effluent of the aerobic tank is measured to be N e To control the ammonia nitrogen index N of the effluent c For reference, the feedback compensation is performed when |N e -N c |≤10％N c When in time, no feedback compensation is performed; when N is e -N c >10％N c Setting the compensation coefficient k to be 1.2; when N is c -N e >10％N c Setting a compensation coefficient k to be 0.9; output the actual aeration quantity Q A＝ kQ T ；
Step 7: correcting the model; constructing Q with data accumulated in this stage every 30 days A -Q T And (3) correcting the correlation coefficient of the theoretical aeration quantity function model by adopting an optimization algorithm to minimize the loss function, and repeating the steps 5 to 7.
2. The intelligent aeration setting method for sewage treatment according to claim 1, wherein the step 1 specifically comprises:
in the process of accumulating the original data, the related operation parameter data of the aerobic tank is collected for not less than 30 days, and the related operation parameter data collected each time is a group of related operation parameter data.
3. The intelligent aeration setting method for sewage treatment according to claim 1, wherein step 2 specifically comprises:
calculating a pearson correlation coefficient, a cosine similarity, a Euclidean distance and a Sperman rank correlation coefficient between the aeration quantity and the dissolved oxygen quantity, taking an average value of the pearson correlation coefficient, the cosine similarity, the Euclidean distance and the Sperman rank correlation coefficient as the correlation coefficient between the aeration quantity and the dissolved oxygen quantity, and judging the influence of the dissolved oxygen quantity on the aeration quantity according to the magnitude of the correlation coefficient;
calculating the correlation coefficient between aeration quantity and water inflow quantity, water inflow ammonia nitrogen concentration, water inflow suspended matter concentration, water inflow TN concentration, water inflow chemical oxygen demand, water outflow TN concentration, reflux ratio, temperature and air pressure by the same method, and judging the influence of the water inflow quantity, water inflow ammonia nitrogen concentration, water inflow suspended matter concentration, water inflow TN concentration, water inflow chemical oxygen demand, water outflow TN concentration, reflux ratio, temperature and air pressure on the aeration quantity;
all the obtained correlation coefficients are arranged into a correlation coefficient column from large to small, the first 80% in the correlation coefficient column is taken as a significant correlation coefficient, and the correlation operation parameter data corresponding to the significant correlation coefficient is the correlation operation parameter data with great influence on the aeration amount; the last 20% of the phase relation series are non-obvious correlation coefficients, and the relevant operation parameter data corresponding to the non-obvious correlation coefficients are relevant operation parameter data with small influence on the aeration quantity.
4. The intelligent aeration setting method for sewage treatment according to claim 1, wherein the data fitting tool in the step 4 is 1stop software.
5. The intelligent aeration setting method for sewage treatment according to claim 1, wherein the ammonia nitrogen value of the effluent is designed to be N s The ammonia nitrogen index N of the discharged water is controlled c Should satisfy N s -N c ≥30％N s 。
6. The intelligent aeration setting method for sewage treatment according to claim 1, wherein the optimization algorithm adopted in the step 7 is a gradient descent or Adam algorithm or a genetic algorithm.
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| WO2024198229A1 (en) * | 2023-03-24 | 2024-10-03 | 青岛思普润水处理股份有限公司 | Intelligent aeration setting method for sewage treatment |
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