CN117521480A - Sewage biological treatment process simulation method - Google Patents
Sewage biological treatment process simulation method Download PDFInfo
- Publication number
- CN117521480A CN117521480A CN202310569109.6A CN202310569109A CN117521480A CN 117521480 A CN117521480 A CN 117521480A CN 202310569109 A CN202310569109 A CN 202310569109A CN 117521480 A CN117521480 A CN 117521480A
- Authority
- CN
- China
- Prior art keywords
- treatment process
- biological treatment
- sewage
- model
- sewage biological
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 112
- 239000010865 sewage Substances 0.000 title claims abstract description 99
- 230000008569 process Effects 0.000 title claims abstract description 71
- 238000004088 simulation Methods 0.000 title claims abstract description 27
- 238000012544 monitoring process Methods 0.000 claims abstract description 24
- 238000005842 biochemical reaction Methods 0.000 claims abstract description 18
- 238000004886 process control Methods 0.000 claims abstract description 10
- 238000010276 construction Methods 0.000 claims abstract description 5
- 238000011160 research Methods 0.000 claims abstract description 4
- 239000010802 sludge Substances 0.000 claims description 28
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 25
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 14
- 229910052760 oxygen Inorganic materials 0.000 claims description 14
- 239000001301 oxygen Substances 0.000 claims description 14
- XKMRRTOUMJRJIA-UHFFFAOYSA-N ammonia nh3 Chemical compound N.N XKMRRTOUMJRJIA-UHFFFAOYSA-N 0.000 claims description 7
- MMDJDBSEMBIJBB-UHFFFAOYSA-N [O-][N+]([O-])=O.[O-][N+]([O-])=O.[O-][N+]([O-])=O.[NH6+3] Chemical compound [O-][N+]([O-])=O.[O-][N+]([O-])=O.[O-][N+]([O-])=O.[NH6+3] MMDJDBSEMBIJBB-UHFFFAOYSA-N 0.000 claims description 6
- 238000004458 analytical method Methods 0.000 claims description 6
- 238000005070 sampling Methods 0.000 claims description 6
- 238000002474 experimental method Methods 0.000 claims description 4
- 238000013528 artificial neural network Methods 0.000 claims description 3
- 238000003556 assay Methods 0.000 claims description 3
- 125000000524 functional group Chemical group 0.000 claims description 3
- 238000009434 installation Methods 0.000 claims description 3
- 238000012360 testing method Methods 0.000 claims description 3
- 238000004422 calculation algorithm Methods 0.000 abstract description 6
- 238000011161 development Methods 0.000 abstract description 6
- 230000007246 mechanism Effects 0.000 abstract description 4
- 238000007418 data mining Methods 0.000 abstract description 3
- 238000010801 machine learning Methods 0.000 abstract description 2
- AHEWZZJEDQVLOP-UHFFFAOYSA-N monobromobimane Chemical compound BrCC1=C(C)C(=O)N2N1C(C)=C(C)C2=O AHEWZZJEDQVLOP-UHFFFAOYSA-N 0.000 abstract description 2
- 238000004062 sedimentation Methods 0.000 description 18
- 238000006243 chemical reaction Methods 0.000 description 17
- 239000007787 solid Substances 0.000 description 14
- 238000005273 aeration Methods 0.000 description 8
- 239000002351 wastewater Substances 0.000 description 8
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 7
- 241000894006 Bacteria Species 0.000 description 7
- 239000002028 Biomass Substances 0.000 description 6
- 238000005259 measurement Methods 0.000 description 5
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 4
- 229910052799 carbon Inorganic materials 0.000 description 4
- 238000011217 control strategy Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 229910052757 nitrogen Inorganic materials 0.000 description 4
- 238000001556 precipitation Methods 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 230000001651 autotrophic effect Effects 0.000 description 3
- 238000011156 evaluation Methods 0.000 description 3
- 238000006460 hydrolysis reaction Methods 0.000 description 3
- 230000010354 integration Effects 0.000 description 3
- 230000009467 reduction Effects 0.000 description 3
- 239000000523 sample Substances 0.000 description 3
- 238000012546 transfer Methods 0.000 description 3
- 230000015572 biosynthetic process Effects 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 150000001875 compounds Chemical class 0.000 description 2
- 238000011109 contamination Methods 0.000 description 2
- 238000012937 correction Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000005265 energy consumption Methods 0.000 description 2
- 230000007062 hydrolysis Effects 0.000 description 2
- 238000007654 immersion Methods 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 238000002156 mixing Methods 0.000 description 2
- 125000001477 organic nitrogen group Chemical group 0.000 description 2
- 230000003647 oxidation Effects 0.000 description 2
- 238000007254 oxidation reaction Methods 0.000 description 2
- 239000013618 particulate matter Substances 0.000 description 2
- 238000011112 process operation Methods 0.000 description 2
- 238000005086 pumping Methods 0.000 description 2
- 238000004065 wastewater treatment Methods 0.000 description 2
- VHUUQVKOLVNVRT-UHFFFAOYSA-N Ammonium hydroxide Chemical compound [NH4+].[OH-] VHUUQVKOLVNVRT-UHFFFAOYSA-N 0.000 description 1
- 238000005033 Fourier transform infrared spectroscopy Methods 0.000 description 1
- 230000005526 G1 to G0 transition Effects 0.000 description 1
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- QGZKDVFQNNGYKY-UHFFFAOYSA-N ammonia Natural products N QGZKDVFQNNGYKY-UHFFFAOYSA-N 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000004791 biological behavior Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 239000000701 coagulant Substances 0.000 description 1
- 230000001419 dependent effect Effects 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000003344 environmental pollutant Substances 0.000 description 1
- 238000005189 flocculation Methods 0.000 description 1
- 230000016615 flocculation Effects 0.000 description 1
- 230000004907 flux Effects 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 244000005700 microbiome Species 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006396 nitration reaction Methods 0.000 description 1
- 239000005416 organic matter Substances 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 231100000719 pollutant Toxicity 0.000 description 1
- 238000010992 reflux Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 229920006395 saturated elastomer Polymers 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 230000006641 stabilisation Effects 0.000 description 1
- 238000011105 stabilization Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/18—Details relating to CAD techniques using virtual or augmented reality
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Evolutionary Computation (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Artificial Intelligence (AREA)
- Computer Hardware Design (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Activated Sludge Processes (AREA)
Abstract
The invention discloses a sewage biological treatment process simulation method, which comprises the following steps: constructing a sewage treatment process virtual monitoring model and a biochemical reaction dynamics model; constructing a sewage biological treatment process control model taking virtual monitoring data of the virtual monitoring model as an input variable; the invention relates to the technical field of sewage biological treatment processes, in particular to a sewage biological treatment process, which utilizes modeling software and an AlgDesigner platform to carry out simulation; the invention establishes a virtual monitoring model by using a data mining and machine learning algorithm, can overcome the limitation of the technical development level of the sensor, provides monitoring data for real-time control, clarifies the influence mechanism of each state variable on the AAO and MBBR technological performances by researching the biochemical reaction mechanism and the biochemical reaction dynamics research of the sewage biological treatment, realizes the construction scheme of the advanced control algorithm of the sewage biological treatment process, and reveals the bottleneck problem restricting the advanced control implementation of the sewage biological treatment process.
Description
Technical Field
The invention relates to the technical field of sewage biological treatment processes, in particular to a sewage biological treatment process simulation method.
Background
The sewage biological treatment system is used as a core link in the sewage treatment process and plays an important role in controlling the water environment pollution. The efficient and stable operation of the sewage biological treatment system plays an important role in the sustainable development of economy and society. However, fluctuations in the influent load, changes in the operating conditions, and mechanical equipment failures of the sewage biological treatment system frequently occur in the sewage treatment process, and the instantaneous and uncertainty of the process operation bring about serious challenges for the stable operation and energy saving and consumption reduction of the sewage biological treatment system. Therefore, how to improve the automation level of the sewage treatment plant and realize the advanced control of the sewage treatment process are important to ensure the efficient and stable operation of the sewage treatment plant.
The existing sewage biological treatment mainly adopts the following three strategies: the SCADA (Supervisory ControlandDataAcquisition) system, namely data acquisition and monitoring control, is a computer-based DCS and electric power automatic monitoring system, and the current self-control instrument network configuration level of most municipal sewage treatment plants in China is not different from that of large sewage treatment plants in developed countries in Europe and America. And secondly, an advanced control strategy can be understood as establishing a biochemical reaction dynamics model to predict the performance of the sewage treatment system, and then timely adjusting controlled variables such as aeration rate, reflux rate, dosage, sludge discharge amount and the like of the system according to a prediction result. Such as coagulant addition control method based on multiparameter statistics, aeration control method based on ammonia nitrogen concentration, sewage biological denitrification external carbon source addition control method, etc. Thirdly, an Activated Sludge Model (ASM) published by the International Water Association is a platform for researching a sewage biological treatment process and simulating the process, and the sewage biological treatment process such as organic matter removal, nitration reaction, hydrolysis reaction and the like is explained in a form of a Monod equation.
However, the three modes have certain defects: because the self-control instrument of the SCADA system has higher level requirements on operation management personnel, the investment and maintenance cost of an instrument network which can be used for automatic control is higher, the SCADA system of a sewage treatment plant in China is in an idle state or is only used for data acquisition and storage functions, although the operation and maintenance level of equipment of the sewage treatment plant in developed countries is higher, the equipment is limited by the quantity of monitoring indexes and the quality of data, and the instrument monitoring data is only used for simple control of the sewage treatment process and lacks of systemicity. The sewage treatment plants at home and abroad are over dependent on the experience of operators to perform process operation to different degrees, and a large amount of on-line monitoring data is not reasonably checked, excavated and effectively utilized. In the advanced control strategy, a plurality of biochemical reaction processes in the sewage biological treatment process are all round, and most control strategies with strong practicability only consider 1-2 easy control links in the sewage treatment process, so that the operation of the sewage biological treatment system cannot be integrally optimized, and the energy saving and consumption reduction potential of the sewage biological treatment system also has a certain limitation. The method has the advantages that the correction difficulty of the model is increased due to the fact that a plurality of kinetic model parameters are simultaneously introduced into the activated sludge model, particularly, model parameters (such as the autotrophic bacteria specific growth rate) which cannot be directly measured influence model prediction accuracy, researchers early propose a method for estimating the kinetic model parameters by adopting batch experiments, along with the development of computer technology in recent years, a more convenient numerical method is gradually used for estimating the kinetic model parameters, but the correction accuracy of the model parameters with low sensitivity of the numerical method to bacteria specific growth rate and the like is low, and the characterization capability of the constructed kinetic model to the microorganism growth process is poor.
In summary, in order to explore the realization way of advanced control of the sewage biological treatment process and provide theoretical basis and scientific support for the intellectualization of a sewage treatment plant, the invention adopts a data mining algorithm to analyze the relationship between the online monitoring index of a sewage treatment system and the state variable of the process control, and constructs a virtual monitoring model to provide data support for advanced control of the biochemical reaction process; and (3) establishing a sewage treatment process simulation system which is based on the sewage biological treatment process and takes an advanced control algorithm model as a support.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a sewage biological treatment process simulation method, which solves the problems.
In order to achieve the above purpose, the invention is realized by the following technical scheme: a sewage biological treatment process simulation method comprises the following steps:
step one: constructing a sewage treatment process virtual monitoring model and a biochemical reaction dynamics model;
step two: constructing a sewage biological treatment process control model taking virtual monitoring data of the virtual monitoring model as an input variable;
step three: and (5) performing simulation by using modeling software and an AlgDesigner platform.
Preferably, the method for constructing the virtual monitoring model of the sewage treatment process in the first step comprises the following steps: the method comprises the steps of selecting a biochemical treatment unit of a sewage treatment plant as a research object, installing and adjusting an online monitoring instrument for pH, conductivity, ORP, sludge concentration, dissolved oxygen, ammonia nitrogen and nitrate nitrogen, collecting real-time monitoring data, retrieving the test analysis result of the sewage treatment plant of the past year, selecting a plurality of sampling points in a biological treatment system for continuous sampling, and constructing a virtual monitoring model of the sewage treatment process based on a least square regression and BP neural network.
Preferably, the method for constructing the biochemical reaction kinetic model in the first step comprises the following steps: start-up to employ A 2 The method comprises the steps of adjusting the installation positions of online meters of pH, conductivity, ORP, sludge concentration and dissolved oxygen, adding online monitoring meters of ammonia nitrogen and nitrate nitrogen, simultaneously taking a sewage water sample for assay analysis, collecting data in the running process, establishing a process running database, and establishing a biochemical reaction dynamics model for the pilot experiment device according to an ASM model.
Preferably, the method for constructing the sewage biological treatment process control model in the second step comprises the following steps:
step S1: taking activated sludge and biological film samples under different operation conditions, analyzing the changes of morphology structures, surface charges and surface functional groups of the activated sludge and the biological film by using a scanning electron microscope, a Zeta potential analyzer and a Fourier infrared spectrometer, and establishing a sludge floc and biological film property database in the operation process;
step S2: and establishing a sewage biological treatment process control model by taking the predicted value of the virtual monitoring model and real-time monitoring data of the online instrument as input quantity and combining the sewage biological treatment dynamics model.
Preferably, the modeling software in the third step includes, but is not limited to, R software, matlab, STOAT software, and GPS-X simulation software.
Advantageous effects
The invention provides a sewage biological treatment process simulation method. Compared with the prior art, the method has the following beneficial effects:
(1) The sewage biological treatment process simulation method adopts data mining and machine learning algorithms to establish a virtual monitoring model, so that the limitation of the technical development level of the sensor can be overcome, monitoring data is provided for real-time control, meanwhile, a plurality of town sewage treatment plants are selected to establish a database, state variables carrying significant process change information are selected to construct the virtual monitoring model, and the response relation of each state variable and other monitoring indexes can be analyzed.
(2) According to the sewage biological treatment process simulation method, the influence mechanism of each state variable on the AAO and MBBR process performance is clarified by researching the biochemical reaction mechanism and the biochemical reaction dynamics of the sewage biological treatment, the construction scheme of the advanced control algorithm of the sewage biological treatment process is realized, and the bottleneck problem of restricting the advanced control implementation of the sewage biological treatment process is disclosed.
(3) According to the sewage biological treatment process simulation method, the energy conservation and consumption reduction of a sewage treatment plant can be realized by monitoring the sewage biological treatment process and analyzing and diagnosing the big data, the energy consumption and the medicine consumption of the sewage treatment can be obviously reduced on the premise of ensuring the operation stability of the process by monitoring and controlling the sewage treatment process, and the running cost of the sewage treatment plant can be reduced by 20-50% in the future by applying the advanced monitoring and controlling model.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a flow chart of the development of a virtual monitoring model of a sewage treatment process according to the first embodiment of the invention;
FIG. 3 shows the conversion relationship between the components in the biochemical reaction kinetic model according to the first embodiment of the present invention;
FIG. 4 shows a structure of a biological reaction tank according to a first embodiment of the present invention;
FIG. 5 is a diagram of dynamic data of water inflow for dry weather in accordance with an embodiment of the present invention;
FIG. 6 is a diagram of dynamic data of water inflow in stormy weather in accordance with the first embodiment of the present invention;
FIG. 7 is a graph showing dynamic data of water inflow in a rainy day according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Embodiment one:
referring to fig. 1-7, a sewage biological treatment process simulation method comprises the following steps:
constructing a sewage treatment process virtual monitoring model and a biochemical reaction dynamics model;
the construction method of the sewage treatment process virtual monitoring model comprises the following steps: the biochemical treatment unit of the sewage treatment plant is selected as a research object, an online monitoring instrument for pH, conductivity, ORP, sludge concentration, dissolved oxygen, ammonia nitrogen and nitrate nitrogen is installed and adjusted, real-time monitoring data are collected, the test analysis result of the previous sewage treatment plant is reviewed, a plurality of sampling points are selected in a biological treatment system for continuous sampling, a virtual monitoring model of the sewage treatment process based on least square regression and BP neural network is constructed, and the development flow is shown in figure 2.
The construction method of the biochemical reaction kinetic model comprises the following steps: start-up to employ A 2 The method comprises the steps of adjusting the installation positions of online meters of pH, conductivity, ORP, sludge concentration and dissolved oxygen, adding online monitoring meters of ammonia nitrogen and nitrate nitrogen, simultaneously taking a sewage water sample for assay analysis, collecting data in the running process, establishing a process running database, and establishing a biochemical reaction dynamics model for the pilot experiment device according to an ASM model. The model components have 13 items, definition and symbols of each component are shown in table 1, and model parameters and meanings are shown in table 2. The stoichiometric parameters are listed in Table 3 and the kinetic parameters are listed in Table 4. The transformation relationship between the components in the model is shown in FIG. 3.
TABLE 1
TABLE 2
| Parameters (parameters) | Unit (B) | Numerical value |
| YA | Gram COD cell formation (gN oxidation) -1 | 0.24 |
| YH | Gram COD cell formation (gCOD oxidation) -1 | 0.67 |
| fP | Dimensionless | 0.08 |
| iXB | Gram of N.sup.in biomass (gCOD) -1 。 | 0.08 |
| iXP | Gram of n.m. (gCOD) in particulate matter -1 | 0.06 |
TABLE 3 Table 3
TABLE 4 Table 4
(1) Eight basic processes are used to describe the biological behavior of the system, and the reaction process rates are expressed as follows:
j=1: aerobic culture of heterotrophic bacteria
J=2: anoxic cultivation of heterotrophic bacteria
J=3: aerobic culture of autotrophic bacteria
J=4: attenuation of heterotrophic bacteria
ρ 4 ＝b H X B，H
J=5: attenuation of autotrophic bacteria
ρ 5 ＝b A X B，A
J=6: ammoniated soluble organic nitrogen
ρ 6 ＝k a S ND X B，H
J=7: hydrolysis of particulate degradable organics
J=8: hydrolysis of particulate degradable organic nitrogen
(2) Conversion (r) i ) As a result of the combination of various basic processes, the model component conversion rates are as follows:
οS I (i＝1)
r 1 ＝0
οS S (i＝2)
οX I (i＝3)
r 3 ＝0
οX S (i＝4)
r 4 ＝(1-f P )ρ 4 +(1-f P )ρ 5 -ρ 7
οX B，H (i＝5)
r 5 ＝ρ 1 +ρ 2 -ρ 4
οX B，A (i＝6)
r 6 ＝ρ 3 -ρ 5
οX P (i＝7)
r 7 ＝f P ρ 4 +f P ρ 5
οS O (i＝8)
οS NO (i＝9)
οS NH (i＝10)
οS ND (i＝11)
r 11 ＝-ρ 6 +ρ 8
οX ND (i＝12)
r 12 ＝(i XB -f P i XP )ρ 4 +(i XB -f P i XP )ρ 5 -ρ 8
οS ALK (i＝13)
(3) the structure of the biological reaction tank is shown in figure 4.
The biological reaction tank is characterized in that:
pool number: 5, a step of;
non-aeration tank: pool number 1-2;
and (3) an aeration tank: pool No. 3-4, fixed oxygen transfer coefficient (kla=10h—1=240 d-1).
Pool No. 5: by controlling KLa, the dissolved oxygen concentration (DO) was controlled to 2g (-COD) m -3 Is a level of (c).
In each cell, the flow rate: qk, concentration: zk
Volume:
non-aeration tank: v1=v2=1000m 3
And (3) an aeration tank: v3=v4=v5=1333m 3
Reaction rate: and rk.
(4) The formula for mass balance is as follows:
k=1 (pool No. 1)
Q 1 ＝Q a +Q r +Q 0
k=2 to 5 (pool No. 2 to 5)
Q k ＝Q k-1
Special cases of oxygen (SO, k)
Wherein the saturated concentration of oxygen is so=8g.m -3 。
Z a ＝Z 5
Z f ＝Z 5
Z w ＝Z r
Q f ＝Q 5 -Q a ＝Q e +Q r +Q w ＝Q e +Q u
(5) A secondary sedimentation tank part:
the model of the secondary sedimentation tank is 10 layers of non-reaction units (namely no biological reaction). Layer 6 (from bottom to top) is the water intake layer. The area (A) of the sedimentation tank is 1500m 2 . The height m (zm) of each layer is equal to 0.4m, the total height is 4m, and the volume of the sedimentation tank is equal to 6000m 3 。
The solids flux caused by gravity is js=vs (X) X, where X is the total sludge concentration. A double-index sedimentation velocity function is selected:
minimum nsxfx=f. The parameter values for the sedimentation velocity function are given in table 5.
| Parameters (parameters) | Unit (B) | Numerical value | |
| Maximum sedimentation velocity | v0 | m.d -1 | 250.0 |
| Maximum Vesilnd sedimentation velocity | v0 | m.d -1 | 474 |
| Parameter of subsidence in the hindered zone | rh | m 3 .(gSS) -1 | 0.000576 |
| Sedimentation parameters of flocculation zone | rp | m 3 .(gSS) -1 | 0.00286 |
| Part not easy to settle | fns | Dimensionless | 0.00228 |
TABLE 5
According to these symbols, the mass balance of the sludge:
intake layer (m=6)
Intermediate layers below the water inlet layer (m=2 to m=5)
Bottom layer (m=1)
Intermediate clear layer above water inlet layer (m=7 to m=9)
Top layer (m=10)
/>
The threshold concentration Xt is equal to 3000g.m -3 ；
For soluble components (including dissolved oxygen), each layer represents a well-mixed volume, the concentration of the soluble components being:
intake layer (m=6)
1-5 layers
7-10 layers
The concentrations of circulating water and wastewater are equal to the concentration of the first layer (bottom layer):
Z u ＝Z 1
calculating the sludge concentration according to the concentration of an activated sludge reaction tank No. 5:
wherein fr COD-SS =4/3. The same principle applies to Xu (precipitation Chi Deliu) and Xe (wastewater treatment plant outlet).
The concentration profile of the particulate matter in the circulating water and the wastewater was calculated, the ratio relative to the total solid concentration, assuming that they remained constant throughout the sedimentation tank:
this assumption means that the dynamics of the sedimentation tank inlet particle concentration will be directly transferred to the sedimentation tank underflow and overflow, regardless of the normal residence time in the sedimentation tank.
In the stable case, the sludge age calculation is based on the total biomass present in the system, i.e. the reaction and sedimentation tanks:
wherein TX is a Is the total amount of biomass present in the reaction cell:
TX s is the total amount of biomass present in the sedimentation tank:
φ e is the loss rate of biomass in wastewater:
φ e ＝(X B，H，m +X B，A，m )·Q e
m=10 and Φw is the loss rate of biomass in the wastewater:
φ w ＝(X B，H，u +X B，A，u )·Q w
in an actual sewage treatment plant, the sludge age is calculated from the total amount of solids present in the system:
wherein TX is fa Is the total amount of solids present in the reaction cell:
TX fs is the total amount of solids present in the sedimentation tank:
φ fe is the loss rate of solids in the wastewater:
φ fe ＝X f，m ·Q e
φ w is the loss rate of solids in the wastewater:
φ w ＝X f，u ·Q w
(6) the water intake related data are as follows:
the water inlet time is in days, and the flow rate is in m 3 .d -1 In units of concentration of g.m -3 In units of. The data are given in the following order:
time: s is S I S S X I X S X B，H X B，A X P S O S NO S NH S ND X ND S ALK Q 0
And (3) water inlet:
In any influent：S O ＝0g(-COD).m -3 ；X B，A ＝0g COD.m -3 ；S NO ＝0gN.m -3 ；X P ＝0g COD.m -3 ；S ALK ＝7mol.m
I. water intake in drought weather
This data contains two weeks of drought weather water intake dynamic data, as shown in figure 5.
II, water inflow in stormy weather
The data contains dynamic data of one week drought weather intake and two stormwater days superimposed on the second week drought weather data, as shown in fig. 6.
III, water inflow in rainy days
The data contained dynamic data for one week drought weather intake and long-term rainfall weather for the second week, as shown in fig. 7.
(7) Initialization of
Before using the drought weather file (14 days), a stabilization period of 100 days was completed in the closed loop, constant inputs (average drought weather flow, weighted inflow average concentration) were used, and the measurement was noiseless, then the weather file to be measured was used. The noise on the measurement is used with dynamic files. The dynamic load average value as input during the stationary phase (the remaining variables are all set to 0) is given in table 6.
| Variable(s) | Numerical value | Unit (B) |
| Q0,stab | 18446 | m 3 .d -1 |
| SS,stab | 69.50 | gCOD.m -3 |
| XB,H,stab | 28.17 | gCOD.m -3 |
| XS,stab | 202.32 | gCOD.m -3 |
| XI,stab | 51.20 | gCOD.m -3 |
| SNH,stab | 31.56 | gN.m -3 |
| SI,stab | 30.00 | gCOD.m -3 |
| SND,stab | 6.95 | gN.m -3 |
| XND,stab | 10.59 | gN.m -3 |
| SALK | 7.00 | mol.m -3 |
TABLE 6
(8) Open loop assessment
The stability period of the simulated sewage treatment plant was 100 days before using the drought weather file. The open loop evaluation default control variables have the following constants: qa=55, 338m 3 .d -1 And KLa (5) =3.5 h -1 (or 84 d) -1 ). Steady state values after 100 days are shown in tables 7 to 9.
Table 7: steady state (open loop) of biological reaction tank, wherein influent is water inlet and Unit is Unit
Table 8: precipitation Chi Wentai-concentration of solids and soluble Components in the precipitation layer (open Ring)
Table 9: state variables at settling tank steady state, discharge and underflow
To evaluate the fixed time period (t=t f -t 0 ) The simulation results in the model are calculated as follows:
flow (m) 3 .d -1 )
Compound Zk (mass m) in flow Q -3 ) The concentration of (2) must be proportional to the flow:
embodiment two:
constructing a sewage biological treatment process control model by taking virtual monitoring data of a virtual monitoring model as an input variable:
taking activated sludge and biological film samples under different operation conditions, analyzing the changes of morphology structures, surface charges and surface functional groups of the activated sludge and the biological film by using a scanning electron microscope, a Zeta potential analyzer and a Fourier infrared spectrometer (FT-IR), and establishing a sludge floc and biological film property database in the operation process.
And establishing a sewage biological treatment process control model by taking the predicted value of the virtual monitoring model and real-time monitoring data of the online instrument as input quantity and combining the sewage biological treatment dynamics model.
Default policy will NO in pool NO 3 The N concentration was kept at the set value (1 gm) -3 ) The dissolved oxygen concentration in the pool No. five was maintained at a set value (2 g (-COD) m) -3 )。
(1) Controller variable
No. 2 pool NO 3 N is measured as B0 level, the measurement range is 0-20 g N.m -3 . The minimum measurable by the sensor is 0g N.m -3 . Measurement noise equal to 0.5 g N.m -3 . The controlled variable is the internal circulation flow rate from pool 5 back to pool 1.
For DO control of pool No. 5, assuming DO probe as A level, measuring range is 0-10 g (-COD) m -3 The measurement noise is 0.25g (-COD) m -3 . The controlled variable is the oxygen transfer coefficient KL.
Limiting the recirculation flow, qa ranges from 0 to 5 times Q0, stab, external circulation flow Qr remains unchanged, set qr=q0, stab, and limit the oxygen transfer to pool No. 5: kla=0 to 10h -1 。
(2) Controller type
Both controllers are of PI type. The performance was evaluated by (i=1 for nitrate-PID; i=2 for oxygen-PID):
IAE (integral absolute error)
Wherein e i Error:
ISE (Square error integration)
Maximum deviation from the set point:
variance error:
controlled variable (u) i ) Variance of variation:
Δu i ＝|u i (t+dt)-u i (t)|
(3) performance evaluation
The weighted average of the flow rates of the wastewater concentrations over the three evaluation periods (drought, rain, stormwater weather: 7 days each) met the specifications of Table 10. Total nitrogen (Ntot) is calculated as the sum of SNO, e and SNKj, e, where SNKj is the kjeldahl nitrogen concentration.
| Variable | Value |
| N tot | ＜18gN.m -3 |
| COD t | ＜100g COD.m -3 |
| S NH | ＜4g N.m -3 |
| TSS | ＜30g SS.m -3 |
| BOD 5 | ＜10g BOD.m -3 |
Table 9: waste water quality limit, wherein Variable is a Variable and value is a numerical value
The percentage of time that the blowdown did not reach the limit is reported as the number of violations. The number of violations refers to the number of violations (from below the limit to above the limit).
Performance assessment is divided into two classes: the first stage involves a local control loop, assessed by IAE (absolute error integration) and ISE (squared error integration) criteria, maximum deviation of the setpoint, and variance error.
The second stage provides a measure of the performance impact of the control strategy on the wastewater treatment plant, which can be divided into four layers:
water outlet quality: because pollutants are discharged in the receiving water body, tax or fine payment is required;
weighting the emissions of compounds that have a significant impact on the received water quality and are normally contained in regional legislation, taking out the water quality (EQ) during the observation period T (d) (i.e. the second week or last 7 days of each weather file) (kg pollution unit d -1 ) An average value is calculated. The formula:
wherein:
S Nkj，e ＝S NH，e +S ND，e +X ND，e +i XB (X B，H，e +X X，A，e )+i XP (X P，e +X i，e )
SS e ＝0.75·(X S，e +X I，e +X B，H，e +X B，A，e +X P，e )
BOD 5，e ＝0.25·(S S，e +X S，e +(1-f P )·(X B，H，e +X B，A，e ))
COD e ＝S S，e +S I，e +X S，e +X I，e +X B，H，e +X B，A，e +X P，e
bi is a weighting factor for different types of contamination, which is converted to contamination units (Table 11). At a concentration of g.m -3 And (3) representing.
| Factors of | BSS | BCOD | BNKj | BNO | BBOD 5 |
| Numerical value | 2 | 1 | 30 | 10 | 2 |
TABLE 11
In addition, aqueous ammonia nitrogen (SNH, e 95), total nitrogen (Ntot, e 95) and total suspended solids (TSSe 95) 95% percentiles are shown. These percentiles represent SNH, ntot, and TSS effluent concentrations that exceeded the standard for 5% of the time.
(4) Cost of operation factor
Sludge yield SP (kg.d) -1 ) Calculated from the total solids flow from the waste stream and the amount of solids accumulated in the system over the considered time period (7 days per weather file). At time t, the amount of solids in the system TSS (t):
TSS(t)＝TSS a (t)+TSS s (t)
wherein the TSS a And (t) is the amount of solids in the reaction cell:
TSS s and (t) is the amount of solids in the sedimentation tank:
total sludge yield (SP) total )(kg.d -1 ) Considering the sludge to be disposed of and the sludge lost on the weirs:
aeration Energy (AE) (kWh. D -1 ) And Pumping Energy (PE) (kWh. D -1 ) The calculation formula of the pumping energy (of the internal and external circulation pumps) is:
the aeration energy AE was calculated from kLa according to the following relation, taking into account the particularities of the sewage treatment plant (diffuser type, bubble size, depth of immersion, etc.), valid for a degrimon DP230 porous disc with a depth of immersion of 4 meters:
d -1 and KLa given in i refers to the pool number.
Consumption of external carbon source (EC) (kg COD.d) -1 ) To enhance denitrification:
wherein q EC，i To add the external carbon flow of the ith pool, COD EC ＝400,000gCOD.m -3 Is the concentration of the easily degradable matrix in the external carbon source.
Mixing in the pool under the anoxic state to avoid precipitation. Mixing Energy (ME) (kWh. D -1 ) Is a function of the cell volume：
Calculating an Intake Quality (IQ) index:
S Nkj，0 ＝S NH，0 +S ND，0 +X ND，0 +i XB (X B，H，0 +X X，A，0 )+i XP (X P，0 +X i，0 )
SS 0 ＝0.75·(X S，0 +X I，0 +X B，H，0 +X B，A，0 +X P，0 )
BOD 5，0 ＝0.65·(S S，0 +X S，0 +(1-f P )·(X B，H，0 +X B，A，0 ))
COD 0 ＝S S，0 +S I，0 +X S，0 +X I，0 +X B，H，0 +X B，A，0 +X P，0
finally, the total cost index (OCI) is calculated:
OCI＝AE+PE+5·SP+3·EC+ME
and (5) performing simulation by using modeling software such as R software, matlab, STOAT software, GPS-X and the like and an AlgDesigner platform. The device mainly comprises the following modules: (1) model parameters: setting simulation time length, reaction tank and secondary sedimentation tank volume, controller parameters and the like. (2) Initial conditions: including usual data, historical data, etc. (3) Model calibration: describing each calibration parameter, reasonable parameters may be recommended where applicable. (4) Input: creating new items, creating new runs, opening stored items, opening data, and the like. (5) And (3) outputting: the component to be observed can be selected after the start-up.
And all that is not described in detail in this specification is well known to those skilled in the art.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (5)
1. The sewage biological treatment process simulation method is characterized by comprising the following steps of:
step one: constructing a sewage treatment process virtual monitoring model and a biochemical reaction dynamics model;
step two: constructing a sewage biological treatment process control model taking virtual monitoring data of the virtual monitoring model as an input variable;
step three: and (5) performing simulation by using modeling software and an AlgDesigner platform.
2. The sewage biological treatment process simulation method according to claim 1, wherein the sewage biological treatment process simulation method is characterized by comprising the following steps of: the method for constructing the virtual monitoring model of the sewage treatment process in the first step comprises the following steps: the method comprises the steps of selecting a biochemical treatment unit of a sewage treatment plant as a research object, installing and adjusting an online monitoring instrument for pH, conductivity, ORP, sludge concentration, dissolved oxygen, ammonia nitrogen and nitrate nitrogen, collecting real-time monitoring data, retrieving the test analysis result of the sewage treatment plant of the past year, selecting a plurality of sampling points in a biological treatment system for continuous sampling, and constructing a virtual monitoring model of the sewage treatment process based on a least square regression and BP neural network.
3. The sewage biological treatment process simulation method according to claim 1, wherein the sewage biological treatment process simulation method is characterized by comprising the following steps of: the method for constructing the biochemical reaction kinetic model in the first step comprises the following steps: start-up to employ A 2 The method comprises the steps of adjusting the installation positions of online meters of pH, conductivity, ORP, sludge concentration and dissolved oxygen, adding online monitoring meters of ammonia nitrogen and nitrate nitrogen, simultaneously taking a sewage water sample for assay analysis, collecting data in the running process, establishing a process running database, and establishing a biochemical reaction dynamics model for the pilot experiment device according to an ASM model.
4. The sewage biological treatment process simulation method according to claim 1, wherein the sewage biological treatment process simulation method is characterized by comprising the following steps of: the construction method of the sewage biological treatment process control model in the second step comprises the following steps:
step S1: taking activated sludge and biological film samples under different operation conditions, analyzing the changes of morphology structures, surface charges and surface functional groups of the activated sludge and the biological film by using a scanning electron microscope, a Zeta potential analyzer and a Fourier infrared spectrometer, and establishing a sludge floc and biological film property database in the operation process;
step S2: and establishing a sewage biological treatment process control model by taking the predicted value of the virtual monitoring model and real-time monitoring data of the online instrument as input quantity and combining the sewage biological treatment dynamics model.
5. The sewage biological treatment process simulation method according to claim 1, wherein the sewage biological treatment process simulation method is characterized by comprising the following steps of: the modeling software in the third step comprises, but is not limited to, R software, matlab, STOAT software and GPS-X simulation software.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310569109.6A CN117521480A (en) | 2023-05-19 | 2023-05-19 | Sewage biological treatment process simulation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310569109.6A CN117521480A (en) | 2023-05-19 | 2023-05-19 | Sewage biological treatment process simulation method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117521480A true CN117521480A (en) | 2024-02-06 |
Family
ID=89765154
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310569109.6A Pending CN117521480A (en) | 2023-05-19 | 2023-05-19 | Sewage biological treatment process simulation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117521480A (en) |
-
2023
- 2023-05-19 CN CN202310569109.6A patent/CN117521480A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102122134A (en) | Method and system for wastewater treatment of dissolved oxygen control based on fuzzy neural network | |
| CN101786721B (en) | Random process predicting method for outlet water organic substance concentration of municipal sewage treatment plant | |
| Simon-Várhelyi et al. | Reducing energy costs of the wastewater treatment plant by improved scheduling of the periodic influent load | |
| CN108088974B (en) | Soft measurement method for effluent nitrate nitrogen in anaerobic simultaneous denitrification methanogenesis process | |
| US20190359510A1 (en) | Cooperative optimal control method and system for wastewater treatment process | |
| US20230047297A1 (en) | Control method based on adaptive neural network model for dissolved oxygen of aeration system | |
| CN101121564B (en) | Fuzzy control device and method for A/O technique subsection water-feeding deep denitrogenation | |
| Olsson et al. | Operation and control of wastewater treatment—a Scandinavian perspective over 20 years | |
| Lu et al. | Automatic control and optimal operation for greenhouse gas mitigation in sustainable wastewater treatment plants: A review | |
| Chen et al. | Smart energy savings for aeration control in wastewater treatment | |
| Do et al. | A design of higher-level control based genetic algorithms for wastewater treatment plants | |
| CN103810309A (en) | Soft measurement modeling method of A2O municipal sewage treatment process based on constraint theory | |
| CN103792844B (en) | A kind of sewage treatment control method based on orderly cluster | |
| CN113044973A (en) | Sewage treatment control system and effluent TN control method | |
| CN115754207A (en) | Simulation method and system for biological sewage treatment process | |
| CN201191473Y (en) | A/O process segmental influent deep nitric removing fuzzy control teaching apparatus | |
| EP3504163B1 (en) | Treatment of wastewater | |
| CN115356930B (en) | Multi-objective optimization control system and method in sewage treatment process | |
| CN117521480A (en) | Sewage biological treatment process simulation method | |
| Harja et al. | Control of an activated sludge wastewater treatment process based on a Calibrated and modified BSM1 Model | |
| Haimi et al. | Advanced operation and control methods of municipal wastewater treatment processes in Finland | |
| Barbusiński et al. | Simulation of the influence of wastewater quality indicators and operating parameters of a bioreactor on the variability of nitrogen in outflow and bulking of sludge: Data mining approach | |
| Haimi et al. | Process automation in wastewater treatment plants: The Finnish experience | |
| Singureanu et al. | Obtaining a guide operator of wastewater treatment by SBR process using simulation and sensitivity analysis. | |
| CN216584395U (en) | Carbon-adding denitrification control structure |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |