CN116969582B - Intelligent regulation and control method and system for sewage treatment - Google Patents
Intelligent regulation and control method and system for sewage treatment Download PDFInfo
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- 239000010865 sewage Substances 0.000 title claims abstract description 63
- 238000000034 method Methods 0.000 title claims abstract description 50
- 230000033228 biological regulation Effects 0.000 title claims abstract description 32
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims abstract description 115
- 239000001301 oxygen Substances 0.000 claims abstract description 115
- 229910052760 oxygen Inorganic materials 0.000 claims abstract description 115
- 238000005273 aeration Methods 0.000 claims abstract description 99
- 238000005259 measurement Methods 0.000 claims abstract description 39
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 16
- 230000006870 function Effects 0.000 claims description 19
- 230000036961 partial effect Effects 0.000 claims description 19
- 238000004590 computer program Methods 0.000 claims description 10
- 238000005276 aerator Methods 0.000 claims description 9
- 230000002159 abnormal effect Effects 0.000 claims description 8
- 238000004458 analytical method Methods 0.000 claims description 8
- 230000002829 reductive effect Effects 0.000 claims description 7
- 230000001174 ascending effect Effects 0.000 claims description 3
- 238000013500 data storage Methods 0.000 claims description 3
- 238000000265 homogenisation Methods 0.000 claims description 3
- 238000012544 monitoring process Methods 0.000 claims description 3
- 230000001105 regulatory effect Effects 0.000 claims description 3
- 230000002441 reversible effect Effects 0.000 claims description 3
- 239000012528 membrane Substances 0.000 claims description 2
- 230000003287 optical effect Effects 0.000 claims description 2
- 230000008859 change Effects 0.000 abstract description 11
- 230000035945 sensitivity Effects 0.000 abstract description 6
- 230000007547 defect Effects 0.000 abstract description 3
- 238000005265 energy consumption Methods 0.000 abstract description 2
- 230000000694 effects Effects 0.000 description 10
- XKMRRTOUMJRJIA-UHFFFAOYSA-N ammonia nh3 Chemical compound N.N XKMRRTOUMJRJIA-UHFFFAOYSA-N 0.000 description 5
- 230000000052 comparative effect Effects 0.000 description 5
- 244000005700 microbiome Species 0.000 description 5
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- 230000009286 beneficial effect Effects 0.000 description 3
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- 241000894006 Bacteria Species 0.000 description 2
- 241000282414 Homo sapiens Species 0.000 description 2
- 238000011161 development Methods 0.000 description 2
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- 238000003491 array Methods 0.000 description 1
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- 238000004140 cleaning Methods 0.000 description 1
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Classifications
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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
- C02F3/006—Regulation methods for biological treatment
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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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- 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
- C02F2209/22—O2
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02W—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO WASTEWATER TREATMENT OR WASTE MANAGEMENT
- Y02W10/00—Technologies for wastewater treatment
- Y02W10/10—Biological treatment of water, waste water, or sewage
Abstract
The application belongs to the technical field of sewage treatment and intelligent control, and provides an intelligent regulation and control method and system for sewage treatment, which specifically comprises the following steps: after the dissolved oxygen sensors are initially arranged in the aeration tank for sewage treatment, obtaining actual measurement values of dissolved oxygen through the dissolved oxygen sensors, uploading all the actual measurement values to a server to obtain measured value data, then analyzing the dissolved oxygen demand by using the measured value data to obtain aeration oxygen dissimilarity, and finally performing intelligent regulation and control on aeration equipment in time according to the aeration oxygen dissimilarity. The time point of the collapse of the oxygen content of the aeration is marked efficiently and accurately, the feedback sensitivity to the change of the slowly-inclined polluted water quality or the change of the water quantity is improved, the position of the dissolved oxygen defect is prejudged, the fault tolerance of the system and the adaptability to the complex environment are greatly enhanced, the energy consumption is saved, the stability of the aeration system can be enhanced, and the practical efficiency of sewage treatment in the biological treatment stage can be ensured by reasonably distributing the aeration quantity.
Description
Technical Field
The application belongs to the technical field of sewage treatment and intelligent control, and particularly relates to an intelligent regulation and control method and system for sewage treatment.
Background
With the rapid development of social economy, the production capacity and living standard of people are greatly improved, the sewage generated by human activities is greatly increased, the living environment and the physical health of people are endangered, and the biodiversity and the ecological balance are influenced. Therefore, sewage treatment is highly valued, and through scientifically and effectively treating sewage, water resources can be protected, negative influence on an ecological system is reduced, ecological balance is maintained, sustainable development of human beings and nature is ensured, life quality of people is improved laterally, and social progress is promoted.
The sewage treatment comprises a plurality of stages, wherein the biological treatment stage is one of the most critical stages in the sewage treatment process, and the aeration is the key step of the stage, and the growth environment or living environment required by microorganisms is maintained by providing enough oxygen to the aerobic treatment unit, so that the microorganisms can fully play a role in cleaning the sewage, and pollutant organisms or ammonia nitrogen pollutants in the sewage are efficiently removed, thereby realizing the function of purifying the water body. However, in practical application, the water quantity and water quality of sewage change in real time, the aeration required by the sewage cannot be adjusted in time, if the aeration is too low, the activity or survivability of the clean-up microorganisms cannot be guaranteed, so that the efficiency of the biological treatment stage is greatly influenced, if the aeration is too high, unnecessary energy consumption is increased, the activity of the microorganisms is reduced under the influence of the rise of the pH value or the formation of oxidation products, and even the growth and propagation of partial microorganisms can be inhibited in some projects. Therefore, reasonable distribution of aeration quantity is of great significance to sewage treatment in biological treatment stage.
Aiming at the technical problems, a corresponding solution is provided by an aeration control method and an aeration control system of a sewage treatment process with the publication number of CN116165974A, namely, the feed-forward control of aeration quantity is performed by predicting the dissolved oxygen demand according to the quality of inlet water, and the feedback control is performed by utilizing the actual dissolved oxygen. However, the method can not adjust aeration quantity in time when the change amplitude of the dissolved oxygen is small and the speed is low, and the risk of insufficient feedback adjustment sensitivity exists, namely the problem of rationality of distributing aeration quantity in sewage treatment can not be thoroughly or accurately solved. Therefore, a real-time feedback method with higher precision is needed to optimally regulate and control the phenomenon of feedback regulation sensitivity deficiency in the sewage treatment process.
Disclosure of Invention
The application aims to provide an intelligent regulation and control method and system for sewage treatment, which are used for solving one or more technical problems in the prior art and at least providing a beneficial selection or creation condition.
In order to achieve the above object, according to an aspect of the present application, there is provided an intelligent regulation and control method for sewage treatment, the method comprising the steps of:
s100, arranging a dissolved oxygen sensor in an aeration tank for sewage treatment;
s200, obtaining measured values of dissolved oxygen through each dissolved oxygen sensor, and uploading all the measured values to a server to obtain measured value data;
s300, analyzing the dissolved oxygen demand by using the measured value data to obtain aeration oxygen dissimilarity;
s400, performing intelligent regulation and control on the aeration equipment in time according to the abnormal oxygen characteristics of the aeration.
In step S300, the method for obtaining the aeration oxygen dissimilarity by analyzing the dissolved oxygen demand with the measured value data includes the steps of comparing the first measured value with the second measured value to form a measured difference, obtaining a first drop point by the measured difference in the history data, calculating a first dissimilarity according to the first measured value and an average value of the measured differences in a period of time, and calculating the aeration oxygen dissimilarity by a change characteristic of a change characteristic value of the first dissimilarity;
or in step S300, the method for obtaining the aeration oxygen dissimilarity by analyzing the dissolved oxygen demand with the measured data includes forming a first lateral attribute by comparing the measured data, defining a polar point by using the first lateral attribute, obtaining a tracing-up value by calculating the probability of the polar point in a period, calculating a second lateral attribute based on the tracing-up value, and obtaining the aeration oxygen dissimilarity by quantitatively calculating the first lateral attribute and the second lateral attribute.
Further, in step S100, a method of disposing a dissolved oxygen sensor in an aeration tank of sewage treatment is: the top view of the aeration tank is square, the aeration tank is divided into a plurality of square areas with the same area and size on average, and the center point of each square area is marked as a depression phase point; meanwhile, the water level of the aeration tank is recorded as Hgt, and a dissolved oxygen sensor is arranged at a position 1/2Hgt away from the upper part of the bottom of the tank at each depression phase point, wherein the dissolved oxygen sensor is any one of an optical fluorescence dissolved oxygen sensor, an electrode dissolved oxygen sensor and a membrane dissolved oxygen sensor.
Further, in step S200, the measured values of the dissolved oxygen are obtained by each dissolved oxygen sensor, and all the measured values are uploaded to the server, and the method for obtaining the measured value data is as follows: taking a dissolved oxygen sensor as a node, setting a time period as a measurement interval Tgap, wherein Tgap is as large as 0.1,2 seconds, measuring and obtaining a value from each node in real time every Tgap, taking the value as an actual measurement value TLv, constructing actual measurement values obtained by sensing each dissolved oxygen at the same moment into a sequence, uploading the sequence to a server, and applying data stored by the server to big data storage; the difference value of the actual measurement value obtained at the current moment and the previous moment of a node is recorded as actual measurement difference GDs of the current moment, after the actual measurement difference values of all the nodes at the current moment are obtained, the node corresponding to the minimum value is used as a low-pass node, the actual measurement value obtained at the current moment of the low-pass node is used as a first actual measurement value FTL of the current moment, and the arithmetic average value of the actual measurement values of all the nodes is used as a second actual measurement value STL; the first measured value and the second measured value are classified as measured value data.
Further, in step S300, the dissolved oxygen demand analysis is performed using the measured data, and the method for obtaining the aerated oxygen profile is as follows: setting a numerical interval as a homogenization basic value ETN, ETN epsilon [5, 30 ]]The method comprises the steps of carrying out a first treatment on the surface of the Taking the difference between the second measured value and the first measured value at one moment as a measured difference VTL at the moment; taking the difference between the maximum value and the minimum value obtained from each measured difference obtained from one moment to the previous ETN moment as eVTLA; searching for the moment when the first measured difference VTL increases from the moment of the previous ETN at the current moment, taking the moment as the first difference drop point t1 at the current moment, and recording the first measured value corresponding to the moment t1 as the FTL t1 The method comprises the steps of carrying out a first treatment on the surface of the The time length between the first drop point and the current moment is recorded as tLen, the average value of each measured difference in the time period from the current moment to the first drop point is recorded as eVTLB, and the first abnormal potential VTrd at the current moment is obtained:
；
wherein exp () is an exponential function with a natural constant e as a base; if one time instant is greater than the eVTLA value of both its previous time instant and its subsequent time instant, or if one time instant is less than the eVTLA value of both its previous time instant and its subsequent time instant, the first anomaly potential at that time instant is reassigned to 0;
the average value of the first dissimilarity potential VTrd of which each value is not 0 in the previous ETN times of one time is taken as the aeration oxygen dissimilarity VRAer.
Since the phenomenon of insufficient historical reference exists in the process of acquiring the aeration oxygen dissimilarity, especially when the newly acquired actual measurement difference frequently rises and falls, the acquired aeration oxygen dissimilarity exposes a problem of a certain degree of accuracy and further affects the rationality of adjustment of an aeration system, however, the problem of accuracy and sliding cannot be solved in the prior art, in order to solve the problem faster or better, and eliminate the accuracy and sliding phenomenon, the application proposes a more preferable scheme as follows:
preferably, in step S300, the analysis of dissolved oxygen demand is performed using the measured data, and the method for obtaining the aerated oxygen profile is: setting a time interval as a monitoring time domain Tzn, tzn E [30,180] min; calculating a first partial side attribute Frbias at one moment by the first measured value and the second measured value, wherein Frbias=ln (1+FTL/STL); wherein ln () is a logarithmic function whose base is a natural constant e; acquiring first partial side attributes of each node in history, constructing a sequence as a first partial side sequence, and taking the moment corresponding to the element with the maximum value or the minimum value in the first partial side sequence as a polar point; searching the first polar site and the second polar site which are obtained by reverse time sequence at one moment and respectively serving as a first reference site and a second reference site at the moment; the number of times from one time to the first reference site and the number of times to the second reference site are respectively recorded as tcntF and tcntS, and the ratio between tcntF and tcntS is recorded as the ascending order value RecSp of the time;
forming a sequence by each first measured value from one moment to a second reference position thereof as a first measured reference sequence uftl_ls of the moment, and recording variances of each element in the first measured reference sequence as err_ul; calculating a second bias attribute Scbias at one moment:
；
wherein ovtMid { } is defined as a median folding function, medianFolding functions are ratios of differences between maximum and median and differences between median and minimum in the calling sequence; setting a Boolean variable as a first generic potential FATR for each moment, and setting a default value of the Boolean variable as Null; when the first partial side attribute of one moment is larger than that of the previous moment, assigning TRUE for the first generic potential of the moment, otherwise assigning FALSE; setting a Boolean variable as a second generic potential SATR for each moment, and setting a default value of the Boolean variable as Null; when the second partial side attribute of one moment is larger than the former moment, the TRUE is assigned to the second attribute of the moment, otherwise, the FALSE is assigned; setting a variable for each polar point as a parallel generic potential step value PrlSP, wherein the default value is 1; acquiring a first generic potential and a second generic potential at the current moment to be respectively recorded as FATR 0 And SATR 0 The method comprises the steps of carrying out a first treatment on the surface of the When the polar locus meets fatr=fatr 0 And satr=satr 0 Then assign 2 to its PrlSP when the polar locus meets FATR. Noteq.FATR 0 And SATR is not equal to SATR 0 Then assign 0 to its PrlSP; calculating to obtain aeration oxygen dissimilarity VRAer at the current moment:
；
wherein i1 is an accumulated variable, nD is the total amount of the inner polar locus of Tzn, exp () is an exponential function with a natural constant e as a base, prlSP i1 And ScBias i1 The parallel generic potential step value and the second lateral attribute of the i1 st polar point are respectively represented.
Wherein the elements in each sequence have a one-to-one correspondence with the elements;
the beneficial effects are that: because the aeration oxygen dissimilarity is obtained by calculating according to the node with low dissolved oxygen, the time point of the collapse of the aeration oxygen content can be accurately marked, so that the feedback sensitivity to the slow inclined pollution water quality change or water quantity change can be improved, the dissolved oxygen defect position is prejudged, the fault tolerance of the system and the adaptability to complex environments are greatly enhanced, and the reaction speed and the aeration regulation effect of the aeration regulation system are further improved.
Further, in step S400, the method for performing intelligent regulation and control on the aeration equipment in time according to the aeration oxygen heterogeneity is as follows: setting a time length as a regulating window TCtr, wherein TCtr is E [1,5] min; acquiring aeration oxygen dissimilarity values at all moments in the latest TCtr period to form a sequence as a dissimilarity sequence; the upper quartile value and the lower quartile value of the alien sequence are respectively marked as Up_VRA and Dw_VRA;
if the aeration oxygen dissimilarity between the current moment and the previous moment is larger than or equal to Up_VRA, the power of the aeration equipment is increased by 3-5%; if the oxygen dissimilarity of the aeration at the current moment and the previous moment is less than or equal to Dw_VRA, the power of the aeration equipment is reduced by 3-5%; wherein the aeration equipment is any one of a blast aerator, an underwater impeller aerator or a pump aerator.
Preferably, all undefined variables in the present application, if not explicitly defined, may be thresholds set manually.
The application also provides an intelligent sewage treatment regulation and control system, which comprises: the processor executes the computer program to implement steps in the intelligent sewage treatment control method, the intelligent sewage treatment control system can be operated in a computing device such as a desktop computer, a notebook computer, a palm computer and a cloud data center, and the operable system can include, but is not limited to, a processor, a memory and a server cluster, and the processor executes the computer program to operate in units of the following systems:
a scene arrangement unit for arranging a dissolved oxygen sensor in an aeration tank for sewage treatment;
the data acquisition unit is used for acquiring actual measurement values of the dissolved oxygen through each dissolved oxygen sensor and uploading all the actual measurement values to the server to obtain measured value data;
the analysis deduction unit is used for analyzing the dissolved oxygen demand by using the measured value data to obtain aeration oxygen dissimilarity;
and the real-time feedback unit is used for performing intelligent regulation and control on the aeration equipment in time according to the abnormal oxygen characteristics of the aeration.
The beneficial effects of the application are as follows: the application provides an intelligent regulation and control method and system for sewage treatment, which accurately marks the time point of collapse of oxygen content of aeration, thus being capable of improving feedback sensitivity to slow inclined pollution water quality change or water quantity change, prejudging the position of dissolved oxygen defect, greatly enhancing the fault tolerance of the system and the adaptability to complex environment, and being capable of enhancing the stability of the aeration system by high-efficiency quantitative planning to save time and resources. The aeration rate can be adjusted in time when the change amplitude of the dissolved oxygen is small and the speed is low, the feedback adjustment sensitivity is higher in reliability, the self-adaptive adjustment of the distributed aeration rate is accurately realized in the sewage treatment, and the practical efficiency of the sewage treatment in the biological treatment stage is further ensured by reasonably distributing the aeration rate.
Drawings
The above and other features of the present application will become more apparent from the detailed description of the embodiments thereof given in conjunction with the accompanying drawings, in which like reference characters designate like or similar elements, and it is apparent that the drawings in the following description are merely some examples of the present application, and other drawings may be obtained from these drawings without inventive effort to those of ordinary skill in the art, in which:
FIG. 1 is a flow chart of an intelligent regulation and control method for sewage treatment;
FIG. 2 shows a structure diagram of an intelligent regulation system for sewage treatment.
Detailed Description
The conception, specific structure, and technical effects produced by the present application will be clearly and completely described below with reference to the embodiments and the drawings to fully understand the objects, aspects, and effects of the present application. It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other.
The device arrangement of the comparative example refers to chinese patent publication No. CN 106242031B.
Example 1
The same device as the comparative example is adopted for intelligent regulation and control of the aeration system, and the method specifically comprises the following steps: a flow chart of an intelligent sewage treatment control method as shown in fig. 1, a method for intelligent sewage treatment control according to an embodiment of the present application will be described with reference to fig. 1, the method comprising the steps of:
s100, arranging a dissolved oxygen sensor in an aeration tank for sewage treatment;
s200, obtaining measured values of dissolved oxygen through each dissolved oxygen sensor, and uploading all the measured values to a server to obtain measured value data;
s300, analyzing the dissolved oxygen demand by using the measured value data to obtain aeration oxygen dissimilarity;
s400, performing intelligent regulation and control on the aeration equipment in time according to the abnormal oxygen characteristics of the aeration.
Further, in step S100, a method of disposing a dissolved oxygen sensor in an aeration tank of sewage treatment is: the top view of the aeration tank is square, the aeration tank is divided into a plurality of square areas with the same area and size on average, and the center point of each square area is marked as a depression phase point; meanwhile, the water level of the aeration tank is recorded as Hgt, and a dissolved oxygen sensor is arranged at 1/2Hgt above each depression phase point, wherein the dissolved oxygen sensor is an electrode type dissolved oxygen sensor.
Further, in step S200, the measured values of the dissolved oxygen are obtained by each dissolved oxygen sensor, and all the measured values are uploaded to the server, and the method for obtaining the measured value data is as follows: taking a dissolved oxygen sensor as a node, setting a time period as a measurement interval Tgap, measuring each node in real time every Tgap for 1 second, obtaining a value, taking the value as an actual measurement value TLv, constructing actual measurement values obtained by sensing each dissolved oxygen at the same moment into a sequence, uploading the sequence to a server, and applying data stored by the server to big data storage; the difference value of the actual measurement values obtained at the current moment and the previous moment of a node is recorded as actual measurement difference value GDs, after the actual measurement difference values of all the nodes at the current moment are obtained, the node corresponding to the minimum value is used as a low-pass node, the actual measurement value obtained at the current moment of the low-pass node is used as a first actual measurement value FTL at the current moment, and the arithmetic average value of the actual measurement values of all the nodes is used as a second actual measurement value STL; the first measured value and the second measured value are measured value data.
Further, in step S300, the dissolved oxygen demand analysis is performed using the measured data, and the method for obtaining the aerated oxygen profile is as follows: setting a numerical interval as a homogenization basic value ETN, wherein the ETN takes a value of 15; taking the difference between the second measured value and the first measured value at one moment as a measured difference VTL at the moment; the difference between the maximum value and the minimum value in each measured difference from one moment to the previous ETN moments is called eVTLA; searching for the moment when the first actual measurement difference VTL increases from the moment of the front ETN of the current moment, taking the moment as the difference first drop point t1 of the current moment, recording the time length between the difference first drop point and the current moment as tLen, recording the average value of the actual measurement differences in the time period from the current moment to the difference first drop point as eVTLB, and obtaining the first heterogeneous potential VTrd of the current moment:
；
wherein exp () is an exponential function with a natural constant e as a base; if one time instant is greater than the eVTLA value of both its previous time instant and its subsequent time instant, or if one time instant is less than the eVTLA value of both its previous time instant and its subsequent time instant, the first anomaly potential at that time instant is reassigned to 0;
the average value of the first dissimilarity potential VTrd of which each value is not 0 in the previous ETN times of one time is taken as the aeration oxygen dissimilarity VRAer.
Further, in step S400, the method for performing intelligent regulation and control on the aeration equipment in time according to the aeration oxygen heterogeneity is as follows: setting a time length as a regulating window TCtr, wherein the value of TCtr is 3 minutes; acquiring aeration oxygen dissimilarity values at all moments in the latest TCtr period to form a sequence as a dissimilarity sequence; the upper quartile value and the lower quartile value of the alien sequence are respectively marked as Up_VRA and Dw_VRA;
if the aeration oxygen dissimilarity between the current moment and the previous moment is more than or equal to Up_VRA, the power of the aeration equipment is increased by 3%; if the oxygen dissimilarity of the aeration at the current moment and the previous moment is less than or equal to Dw_VRA, the power of the aeration equipment is reduced by 3%; wherein the aeration equipment is any one of a blast aerator, an underwater impeller aerator or a pump aerator.
Example 2
The aeration control was performed by the method of example 1, wherein example 2 differs from example 1 in that dissolved oxygen demand analysis was performed using measured value data by:
setting a time interval as a monitoring time domain Tzn, tzn E [30,180] min; calculating a first partial side attribute Frbias at one moment by the first measured value and the second measured value, wherein Frbias=ln (1+FTL/STL); wherein ln () is a logarithmic function whose base is a natural constant e; acquiring each first partial side attribute in history to construct a sequence as a first partial side sequence, and taking the moment corresponding to the element with the maximum value or the minimum value in the first partial side sequence as a polar point; searching the first polar site and the second polar site which are obtained by reverse time sequence at one moment and respectively serving as a first reference site and a second reference site at the moment; the number of times from one time to the first reference site and the number of times to the second reference site are respectively recorded as tcntF and tcntS, and the ratio between tcntF and tcntS is recorded as the ascending order value RecSp of the time;
forming a sequence by each first measured value from one moment to a second reference position thereof as a first measured reference sequence uftl_ls of the moment, and recording variances of each element in the first measured reference sequence as err_ul; calculating a second bias attribute Scbias at one moment:
；
wherein, the ovtMid is defined as a median folding function, and the median folding function is the ratio of the difference between the maximum value and the median in the calling sequence to the difference between the median and the minimum value; setting a Boolean variable as a first generic potential FATR for each time instantAnd setting the default value as Null; when the first partial side attribute of one moment is larger than that of the previous moment, assigning TRUE for the first generic potential of the moment, otherwise assigning FALSE; setting a Boolean variable as a second generic potential SATR for each moment, and setting a default value of the Boolean variable as Null; when the second partial side attribute of one moment is larger than the former moment, the TRUE is assigned to the second attribute of the moment, otherwise, the FALSE is assigned; setting a variable for each polar point as a parallel generic potential step value PrlSP, wherein the default value is 1; acquiring a first generic potential and a second generic potential at the current moment to be respectively recorded as FATR 0 And SATR 0 The method comprises the steps of carrying out a first treatment on the surface of the When the polar locus meets fatr=fatr 0 And satr=satr 0 Then assign 2 to its PrlSP when the polar locus meets FATR. Noteq.FATR 0 And SATR is not equal to SATR 0 Then assign 0 to its PrlSP; calculating to obtain aeration oxygen dissimilarity VRAer at the current moment:
；
wherein i1 is an accumulated variable, nD is the total amount of the inner polar locus of Tzn, exp () is an exponential function with a natural constant e as a base, prlSP i1 And ScBias i1 The parallel generic potential step value and the second lateral attribute of the i1 st polar point are respectively represented.
The following table shows the comparison of water quality indexes after sewage is treated by the sewage treatment method:
the unit of single water power consumption in the table is KW.h.m -3 Reduce the unit COD electricity consumption and the unit NH 3 N electricity consumption, TN electricity consumption and COD equivalent are reduced to KW h t -1 The method comprises the steps of carrying out a first treatment on the surface of the The power saving rate 1 and the power saving rate 2 are the power saving rates of embodiment 1 and embodiment 2, respectively; the per equivalent COD reduction is calculated as follows, NH-N is converted to COD, equivalent cod=cod+nh 3 -N x 4.57; the power saving ratio was (comparative example power consumption-example power consumption)/(comparative example power consumption×100%).
It is clear from the table that both the embodiment 1 and the embodiment 2 have obvious energy saving effects, and the embodiment 2 with more accuracy has better energy saving effects than the embodiment 1 in all aspects, so that the superiority of the application to the existing oxygen aeration sewage treatment technology can be found. However, since ammonia nitrogen oxidation involves specific types of ammonia oxidizing bacteria, these bacteria may have a competitive relationship with biological bacteria involved in other treatment processes, so that the treatment effect of ammonia oxidizing bacteria is reduced, and the performance of reducing the unit ammonia nitrogen power consumption is insufficient, but the disadvantage is optimized in example 2, so that the difference between the performance of reducing the unit ammonia nitrogen power consumption in example 2 and the conventional comparative example is negligible, and meanwhile, if the performance of reducing the unit ammonia nitrogen power consumption is insufficient through optimization of ammonia oxidizing bacteria, the situation can be effectively dealt with. In summary, the oxygen-exposure sewage treatment technology of the embodiment has obvious superiority in energy saving effect.
An embodiment of the present application provides an intelligent sewage treatment control system, as shown in fig. 2, which is a structural diagram of the intelligent sewage treatment control system of the present application, and the intelligent sewage treatment control system of the embodiment includes: the intelligent sewage treatment control system comprises a processor, a memory and a computer program which is stored in the memory and can run on the processor, wherein the steps in the embodiment of the intelligent sewage treatment control system are realized when the processor executes the computer program.
The system comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to run in units of the following system:
a scene arrangement unit for arranging a dissolved oxygen sensor in an aeration tank for sewage treatment;
the data acquisition unit is used for acquiring actual measurement values of the dissolved oxygen through each dissolved oxygen sensor and uploading all the actual measurement values to the server to obtain measured value data;
the analysis deduction unit is used for analyzing the dissolved oxygen demand by using the measured value data to obtain aeration oxygen dissimilarity;
and the real-time feedback unit is used for performing intelligent regulation and control on the aeration equipment in time according to the abnormal oxygen characteristics of the aeration.
The intelligent sewage treatment regulation and control system can be operated in computing equipment such as a desktop computer, a notebook computer, a palm computer, a cloud server and the like. The intelligent sewage treatment control system can be operated by a processor and a memory. It will be appreciated by those skilled in the art that the examples are merely examples of one type of intelligent regulation system for sewage treatment and are not limiting of one type of intelligent regulation system for sewage treatment, and may include more or fewer components than examples, or may combine certain components, or different components, e.g., the one type of intelligent regulation system for sewage treatment may further include input and output devices, network access devices, buses, etc.
The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general processor can be a microprocessor or any conventional processor, and the like, and the processor is a control center of the running system of the intelligent sewage treatment control system and is connected with various parts of the running system of the whole intelligent sewage treatment control system by various interfaces and lines.
The memory can be used for storing the computer program and/or the module, and the processor can realize various functions of the intelligent sewage treatment regulation and control system by running or executing the computer program and/or the module stored in the memory and calling the data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
Although the present application has been described in considerable detail and with particularity with respect to several described embodiments, it is not intended to be limited to any such detail or embodiment or any particular embodiment so as to effectively cover the intended scope of the application. Furthermore, the foregoing description of the application has been presented in its embodiments contemplated by the inventors for the purpose of providing a useful description, and for the purposes of providing a non-essential modification of the application that may not be presently contemplated, may represent an equivalent modification of the application.
Claims (2)
1. An intelligent regulation and control method for sewage treatment is characterized by comprising the following steps:
s100, arranging a dissolved oxygen sensor in an aeration tank for sewage treatment;
s200, obtaining measured values of dissolved oxygen through each dissolved oxygen sensor, and uploading all the measured values to a server to obtain measured value data, wherein the measured value data comprise a first measured value and a second measured value;
s300, analyzing the dissolved oxygen demand by using the measured value data to obtain aeration oxygen dissimilarity;
s400, performing intelligent regulation and control on aeration equipment in time according to the abnormal nature of aeration oxygen;
wherein in step S100, the method of disposing the dissolved oxygen sensor in the aeration tank of the sewage treatment is: the top view of the aeration tank is square, the aeration tank is divided into a plurality of square areas with the same area and size on average, and the center point of each square area is marked as a depression phase point; meanwhile, the water level of the aeration tank is recorded as Hgt, a dissolved oxygen sensor is arranged at a position 1/2Hgt away from the upper part of the bottom of the tank at each depression phase point, and the dissolved oxygen sensor is any one of an optical fluorescence dissolved oxygen sensor, an electrode dissolved oxygen sensor and a membrane dissolved oxygen sensor;
in step S200, measured values of the dissolved oxygen are obtained by each dissolved oxygen sensor, and all the measured values are uploaded to the server, and the method for obtaining measured value data is as follows: taking a dissolved oxygen sensor as a node, setting a time period as a measurement interval Tgap, wherein Tgap is as large as 0.1,2 seconds, measuring and obtaining a value from each node in real time every Tgap, taking the value as an actual measurement value TLv, constructing actual measurement values obtained by sensing each dissolved oxygen at the same moment into a sequence, uploading the sequence to a server, and applying data stored by the server to big data storage; the difference value of the actual measurement value obtained at the current moment and the previous moment of a node is recorded as actual measurement difference GDs of the current moment, after the actual measurement difference values of all the nodes at the current moment are obtained, the node corresponding to the minimum value is used as a low-pass node, the actual measurement value obtained at the current moment of the low-pass node is used as a first actual measurement value FTL of the current moment, and the arithmetic average value of the actual measurement values of all the nodes is used as a second actual measurement value STL; classifying the first measured value and the second measured value as measured value data;
in step S300, the dissolved oxygen demand analysis is performed by using the measured data, and the method for obtaining the aerated oxygen dissimilarity is as follows: setting a numerical interval as a homogenization basic value ETN, ETN epsilon [5, 30 ]]The method comprises the steps of carrying out a first treatment on the surface of the Taking the difference between the second measured value and the first measured value at one moment as a measured difference VTL at the moment; taking the difference between the maximum value and the minimum value obtained from each measured difference obtained from one moment to the previous ETN moment as eVTLA; searching for the moment when the first measured difference VTL increases from the moment of the previous ETN at the current moment, taking the moment as the first difference drop point t1 at the current moment, and recording the first measured value corresponding to the moment t1 as the FTL t1 The method comprises the steps of carrying out a first treatment on the surface of the The time length between the first drop point and the current moment is recorded as tLen, the average value of each measured difference in the time period from the current moment to the first drop point is recorded as eVTLB, and the first abnormal potential VTrd at the current moment is obtained:
；
wherein exp () is an exponential function with a natural constant e as a base; if one time instant is greater than the eVTLA value of both its previous time instant and its subsequent time instant, or if one time instant is less than the eVTLA value of both its previous time instant and its subsequent time instant, the first anomaly potential at that time instant is reassigned to 0;
taking the average value of the first abnormal potential VTrd with each value not being 0 in the previous ETN time of one time as the aeration oxygen dissimilarity VRAer;
or in step S300, the dissolved oxygen demand analysis is performed by using the measured data, and the method for obtaining the aeration oxygen dissimilarity is as follows: setting a time interval as a monitoring time domain Tzn, tzn E [30,180] min; calculating a first partial side attribute Frbias at one moment by the first measured value and the second measured value, wherein Frbias=ln (1+FTL/STL); wherein ln () is a logarithmic function whose base is a natural constant e; acquiring first partial side attributes of each node in history, constructing a sequence as a first partial side sequence, and taking the moment corresponding to the element with the maximum value or the minimum value in the first partial side sequence as a polar point; searching the first polar site and the second polar site which are obtained by reverse time sequence at one moment and respectively serving as a first reference site and a second reference site at the moment; the number of times from one time to the first reference site and the number of times to the second reference site are respectively recorded as tcntF and tcntS, and the ratio between tcntF and tcntS is recorded as the ascending order value RecSp of the time;
forming a sequence by each first measured value from one moment to a second reference position thereof as a first measured reference sequence uftl_ls of the moment, and recording variances of each element in the first measured reference sequence as err_ul; calculating a second bias attribute Scbias at one moment:
；
wherein ovtMid { } is defined as a median folding function, which is the sum of differences between the maximum and median in the call sequenceA ratio of the difference between the median and the minimum; setting a Boolean variable as a first generic potential FATR for each moment, and setting a default value of the Boolean variable as Null; when the first partial side attribute of one moment is larger than that of the previous moment, assigning TRUE for the first generic potential of the moment, otherwise assigning FALSE; setting a Boolean variable as a second generic potential SATR for each moment, and setting a default value of the Boolean variable as Null; when the second partial side attribute of one moment is larger than the former moment, the TRUE is assigned to the second attribute of the moment, otherwise, the FALSE is assigned; setting a variable for each polar point as a parallel generic potential step value PrlSP, wherein the default value is 1; acquiring a first generic potential and a second generic potential at the current moment to be respectively recorded as FATR 0 And SATR 0 The method comprises the steps of carrying out a first treatment on the surface of the When the polar locus meets fatr=fatr 0 And satr=satr 0 Then assign 2 to its PrlSP when the polar locus meets FATR. Noteq.FATR 0 And SATR is not equal to SATR 0 Then assign 0 to its PrlSP; calculating to obtain aeration oxygen dissimilarity VRAer at the current moment:
；
wherein i1 is an accumulated variable, nD is the total amount of the inner polar locus of Tzn, exp () is an exponential function with a natural constant e as a base, prlSP i1 And ScBias i1 Respectively representing the parallel generic potential order value and the second lateral attribute of the ith polar point;
in step S400, the method for performing intelligent regulation and control on the aeration equipment in time according to the aeration oxygen dissimilarity is as follows: setting a time length as a regulating window TCtr, wherein TCtr is E [1,5] min; acquiring aeration oxygen dissimilarity values at all moments in the latest TCtr period to form a sequence as a dissimilarity sequence; the upper quartile value and the lower quartile value of the alien sequence are respectively marked as Up_VRA and Dw_VRA;
if the aeration oxygen dissimilarity between the current moment and the previous moment is larger than or equal to Up_VRA, the power of the aeration equipment is increased by 3-5%; if the oxygen dissimilarity of the aeration at the current moment and the previous moment is less than or equal to Dw_VRA, the power of the aeration equipment is reduced by 3-5%; wherein the aeration equipment is any one of a blast aerator, an underwater impeller aerator or a pump aerator.
2. The utility model provides a sewage treatment intelligent regulation and control system which characterized in that, a sewage treatment intelligent regulation and control system includes: the system comprises a processor, a memory and a computer program stored in the memory and capable of running on the processor, wherein the steps in the intelligent sewage treatment regulation and control method in claim 1 are realized when the processor executes the computer program, and the intelligent sewage treatment regulation and control system runs in computing equipment of a desktop computer, a notebook computer, a palm computer and a cloud data center.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108762082A (en) * | 2018-06-07 | 2018-11-06 | 北京工业大学 | A kind of sewage disposal process collaboration Optimal Control System |
| CN109928493A (en) * | 2019-02-25 | 2019-06-25 | 北控水务(中国)投资有限公司 | It is a kind of that method is controlled to adjust based on the accurate dissolved oxygen of big data and evolution algorithm |
| CN112241608A (en) * | 2020-10-13 | 2021-01-19 | 国网湖北省电力有限公司电力科学研究院 | Lithium battery life prediction method based on LSTM network and transfer learning |
| CN114879496A (en) * | 2022-05-07 | 2022-08-09 | 北京工业大学 | Data-driven prediction control method for random sampling model in municipal sewage treatment process |
| CN115456245A (en) * | 2022-08-12 | 2022-12-09 | 生态环境部华南环境科学研究所(生态环境部生态环境应急研究所) | Prediction method for dissolved oxygen in tidal river network area |
| WO2023020081A1 (en) * | 2021-08-18 | 2023-02-23 | 青岛海尔空调器有限总公司 | Method and apparatus for controlling air conditioner, and server |
| WO2023116062A1 (en) * | 2021-12-23 | 2023-06-29 | 荣耀终端有限公司 | Heart rate measurement method and apparatus |
-
2023
- 2023-09-22 CN CN202311226102.0A patent/CN116969582B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108762082A (en) * | 2018-06-07 | 2018-11-06 | 北京工业大学 | A kind of sewage disposal process collaboration Optimal Control System |
| CN109928493A (en) * | 2019-02-25 | 2019-06-25 | 北控水务(中国)投资有限公司 | It is a kind of that method is controlled to adjust based on the accurate dissolved oxygen of big data and evolution algorithm |
| CN112241608A (en) * | 2020-10-13 | 2021-01-19 | 国网湖北省电力有限公司电力科学研究院 | Lithium battery life prediction method based on LSTM network and transfer learning |
| WO2023020081A1 (en) * | 2021-08-18 | 2023-02-23 | 青岛海尔空调器有限总公司 | Method and apparatus for controlling air conditioner, and server |
| WO2023116062A1 (en) * | 2021-12-23 | 2023-06-29 | 荣耀终端有限公司 | Heart rate measurement method and apparatus |
| CN114879496A (en) * | 2022-05-07 | 2022-08-09 | 北京工业大学 | Data-driven prediction control method for random sampling model in municipal sewage treatment process |
| CN115456245A (en) * | 2022-08-12 | 2022-12-09 | 生态环境部华南环境科学研究所(生态环境部生态环境应急研究所) | Prediction method for dissolved oxygen in tidal river network area |
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