CN115754207A - Simulation method and system for biological sewage treatment process - Google Patents
Simulation method and system for biological sewage treatment process Download PDFInfo
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Abstract
The invention belongs to the field of biological sewage treatment, and particularly relates to a biological sewage treatment process simulation method and system, which comprises the following steps: step one, establishing a virtual monitoring model of a sewage treatment process, and establishing an algorithm selection criterion; step two, establishing a biochemical reaction kinetic model of the sewage biological treatment process; step three, constructing a sewage treatment process advanced control model taking the virtual monitoring data as an input variable; the invention adopts data mining and machine learning algorithm to establish virtual monitoring model, overcomes the limitation of sensor technology development level, provides monitoring data for real-time control, analyzes the response relation of each state variable and other monitoring indexes, carries out biochemical reaction dynamic model parameter accurate correction mechanism and sewage biological treatment process advanced control algorithm construction scheme by researching the biochemical reaction mechanism and biochemical reaction dynamics research of sewage biological treatment, and realizes energy conservation and consumption reduction of sewage treatment plant by monitoring and big data analysis and diagnosis of sewage biological treatment process.
Description
Technical Field
The invention relates to the field of biological sewage treatment, in particular to a method and a system for simulating a biological sewage treatment process.
Background
The biological sewage treatment system is used as a core link in the sewage treatment process and plays an important role in controlling 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, the fluctuation of water inlet load, the change of operation conditions and the failure of mechanical equipment of the sewage biological treatment system are frequent in the sewage treatment process, and the transient and uncertain process operation bring serious challenges to the stable operation, energy conservation 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 is very important for ensuring the efficient and stable operation of the sewage treatment plant.
Model Predictive Control (MPC) is referred to as the only process high-level Control strategy. However, the biological sewage treatment process is composed of a plurality of highly complex and interrelated biochemical reaction processes, including hydrolysis of biodegradable suspended solids, oxidation of biodegradable organic matter, nitrification, denitrification, microbial endogenous respiration, ammoniation, anaerobic phosphorus release by phosphorus-accumulating bacteria, aerobic phosphorus uptake, and the like. How to obtain the real-time monitoring data of the state variables of each biochemical reaction process is a difficult point for realizing advanced control of the sewage biological treatment process. In addition, how to construct a biochemical reaction kinetic model for analyzing the relationship between the microbial activity and the pollutant migration change rule and finally serving an advanced control algorithm is another basic problem to be solved.
The prior art scheme is as follows:
a Supervisory Control and Data Acquisition (SCADA) system is a DCS and electric power automatic monitoring system based on a computer, and the network configuration level of automatic Control instruments of most municipal sewage treatment plants in China at present is not much different from that of large sewage treatment plants in developed countries in Europe and America.
2. The advanced control strategy can be understood as establishing a biochemical reaction kinetic model to predict the performance of the sewage treatment system, and then adjusting controlled variables such as aeration quantity, reflux quantity, dosage, sludge discharge quantity and the like of the system in time according to a prediction result. Such as coagulant adding control method based on multi-parameter statistics, aeration control method based on ammonia nitrogen concentration, sewage biological denitrification external carbon source adding control method and the like.
3. An Activated Sludge Model (ASM) published by the International Water society is a platform for researching sewage biological treatment processes and simulating the processes, and the sewage biological treatment processes such as organic matter removal, nitration reaction, hydrolysis reaction and the like are explained in the form of Monod equation. :
the prior art has the following problems:
the SCADA system has higher requirements on the level of operation managers due to the automatic control instrument, 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 mostly in an idle state or only used for a data acquisition and storage function, although the equipment operation and maintenance level of the sewage treatment plant in developed countries is higher and limited by the quantity of monitoring indexes and the quality of data, the instrument monitoring data is only used for simple control of a sewage treatment process, and the systematicness is lacked. The sewage treatment plants at home and abroad depend on the experience of operators to operate the process to different degrees, and a large amount of online monitoring data is not reasonably checked, mined and effectively utilized.
2. The advanced control strategy and several biochemical reaction processes of the sewage biological treatment process are linked, and most of the 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-reducing potential of the system also has certain limitations.
3. The activated sludge model, and the simultaneous introduction of multiple kinetic model parameters increase the difficulty of model correction. In particular, several model parameters (such as the rate of growth of autotrophic bacteria) that cannot be directly measured affect the accuracy of model prediction. Researchers have proposed methods for estimating kinetic model parameters using batch experiments in the early days, and with the development of computer technology in recent years, more convenient numerical methods are gradually used for kinetic model parameter estimation. However, the numerical method has lower accuracy of correcting model parameters with low sensitivity such as bacteria specific growth rate and the like, and the constructed kinetic model has poorer characterization capability on the microbial growth process.
In order to solve the problems, the application provides a simulation method for a sewage biological treatment process.
Disclosure of Invention
Objects of the invention
In order to solve the technical problems in the background art, the invention provides a simulation method and a system for a sewage biological treatment process, which are used for exploring the implementation way of advanced control of the sewage biological treatment process and providing theoretical basis and scientific support for the intellectualization of a sewage treatment plant; an experimental method for accurately correcting the parameters of the reaction kinetic model is designed and researched, model support is provided for the construction of an advanced control algorithm in the sewage biological treatment process, and a sewage treatment process simulation system which is based on the sewage biological treatment process and supported by the advanced control algorithm model is established.
(II) technical scheme
In order to solve the technical problem, the invention provides a sewage biological treatment process simulation method, which comprises the following steps:
step one, establishing a virtual monitoring model of a sewage treatment process, and establishing an algorithm selection criterion;
step two, establishing a biochemical reaction kinetic model of the sewage biological treatment process;
and step three, constructing a sewage treatment process advanced control model taking the virtual monitoring data as an input variable.
Preferably, in step one: selecting a biochemical treatment unit of a sewage treatment plant as a research object, installing and adjusting online monitoring instruments such as pH, conductivity, ORP, sludge concentration, dissolved oxygen, ammonia nitrogen, nitrate nitrogen and the like, and collecting real-time monitoring data;
the laboratory test analysis data of the sewage treatment plant in the past year is retrieved, a plurality of sampling points are selected in the biological treatment system for continuous sampling, and the migration and transformation rules of carbon, nitrogen, phosphorus and oxygen in the sewage treatment process are analyzed;
combining real-time monitoring data monitored on line with laboratory test analysis data, mining the data by adopting an unsupervised learning algorithm, analyzing unknown information hidden behind the data, and analyzing the collinearity and the correlation of each monitoring variable;
establishing a linear or nonlinear prediction model, namely a virtual monitoring model, for the state variable according to the collinearity and the correlation of the water quality on-line monitoring variable easy to monitor in real time and the biochemical reflection state variable difficult to monitor in real time;
and analyzing rules of a machine learning algorithm for constructing virtual monitoring models of different variables of different sewage treatment processes, and summarizing and providing a virtual monitoring model construction guiding principle in the sewage treatment process.
Preferably, the easy real-time monitoring variables are water quality on-line monitoring variables including: flow, pH, conductivity, ORP, ammonia nitrogen, suspended matters and the like;
difficult real-time monitoring variables are key state variables that directly participate in biochemical reactions, including: volatile Fatty Acids (VFA), soluble biodegradable COD, particulate biodegradable COD, organic nitrogen, and the like.
Preferably, in step two: starting an activated sludge system adopting an AAO process and a biofilm system pilot-scale experimental device adopting an MBBR process, adjusting the installation positions of online instruments such as pH, conductivity, ORP, sludge concentration, dissolved oxygen and the like, additionally installing online monitoring instruments for ammonia nitrogen and nitrate nitrogen, simultaneously taking a sewage sample for assay analysis, collecting data in the operation process, and establishing a process operation database;
quantitatively analyzing the activated sludge/biomembrane microbial community structure of the pilot-scale test system by adopting a high-throughput sequencing technology, revealing a response mechanism of a microbial community to the change of the water quality, the water quantity and the operation environment of the system, establishing a response model by combining a process operation database, and quickly and quantitatively analyzing the microbial community structure by adopting a machine learning algorithm;
according to the ASM2d model, the ASM3 model and the biomembrane model, the biochemical reaction kinetic model is respectively established for the two pilot systems, meanwhile, a closed respiration rate measuring device is adopted to measure the respiration rates of the activated sludge/biomembrane under different working conditions, and the parameters of the kinetic model are accurately corrected by combining with the structure data of the microbial community.
Preferably, a dynamic model parameter, a water quality, a water quantity, an operation condition, a microbial community structure response model and a sewage biological treatment dynamic model rapid modeling method under each operation condition are established by combining a process operation database.
Preferably, in step three: taking activated sludge and biofilm samples under different operating conditions, analyzing the morphology structures, surface charges and changes of surface functional groups of the activated sludge and the biofilms by using a scanning electron microscope, a Zeta potential analyzer and a Fourier infrared spectrometer (FT-IR), and establishing a sludge floc and biofilm property database in the operating process;
establishing an activated sludge/biomembrane-water quality fuzzy model by combining an activated sludge/biomembrane property database and a pilot-scale system water quality monitoring result, and analyzing the influence of water quality, water quantity and operation environment change on the appearance structure and surface chemical property of the activated sludge (biomembrane);
the method comprises the steps of establishing a sewage biological treatment process control model by taking a predicted value of a virtual monitoring model and real-time monitoring data of an online instrument as input quantities and combining a sewage biological treatment dynamics model, carrying out simulation on an AlgDesigner platform, and testing a process advanced control algorithm.
Preferably, the characteristics and the construction conditions of the advanced control algorithm of the sewage biological treatment process under different working conditions of different processes are statistically analyzed, and an intelligent control system of the sewage treatment plant is constructed.
The invention also provides a sewage biological treatment process simulation system which comprises the steps of establishing a sewage treatment overall process advanced control algorithm through the water treatment model which is researched and established by the virtual monitoring model input variable and dynamic model and the accurate correction basic method, analyzing the member rule of the virtual monitoring model, providing an algorithm selection criterion and integrating the industrial sewage treatment and resource simulation system.
Preferably, a virtual detection model is established based on the state variable data mining of the whole process of sewage treatment, and a kinetic model and an accurate correction basic method are established based on the research of the biochemical reaction mechanism of sewage biological treatment.
The technical scheme of the invention has the following beneficial technical effects:
1. the invention adopts data mining and machine learning algorithm to establish virtual monitoring model, which can overcome the limitation of sensor technology development level, provides monitoring data for real-time control, selects multiple town sewage treatment plants to establish database, selects state variables carrying significant process change information to construct virtual detection model, and can analyze the response relation of each state variable and other monitoring indexes.
2. By researching the biochemical reaction mechanism and the biochemical reaction kinetics of the sewage biological treatment, and combining the respiration rate of the activated sludge/biomembrane and the monitoring result of molecular biology, the influence mechanism of each state variable on the process performance of the AAO and MBBR is clarified, the accurate correction mechanism of the parameters of the biochemical reaction kinetics model and the construction scheme of the advanced control algorithm of the sewage biological treatment process are realized, and the bottleneck problem of restricting the advanced control implementation of the sewage biological treatment process is disclosed.
3. The monitoring and big data analysis and diagnosis of the sewage biological treatment process can realize the energy saving and consumption reduction of the sewage treatment plant, the monitoring and control of the sewage treatment process can obviously reduce the energy consumption and the medicine consumption of the sewage treatment on the premise of guaranteeing the process operation stability, and the application of the advanced monitoring and control model is expected to reduce the operation cost of the sewage treatment plant by 20 to 50 percent in the future.
Drawings
FIG. 1 is a flow chart of the simulation method for biological sewage treatment process according to the present invention;
FIG. 2 is a schematic view of a simulation system for biological sewage treatment process according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the accompanying drawings in combination with the embodiments. It should be understood that the description is intended to be exemplary only, and is not intended to limit the scope of the present invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.
Example one
As shown in FIG. 1, the simulation method for biological sewage treatment provided by the invention comprises the following steps:
step one, establishing a virtual monitoring model of a sewage treatment process, and establishing an algorithm selection criterion;
in the first step: selecting a biochemical treatment unit of a sewage treatment plant as a research object, installing and adjusting online monitoring instruments such as pH, conductivity, ORP, sludge concentration, dissolved oxygen, ammonia nitrogen, nitrate nitrogen and the like, and collecting real-time monitoring data;
the laboratory test analysis data of the sewage treatment plant in the past year is retrieved, a plurality of sampling points are selected in the biological treatment system for continuous sampling, and the migration and transformation rules of carbon, nitrogen, phosphorus and oxygen in the sewage treatment process are analyzed;
combining real-time monitoring data monitored on line with laboratory test analysis data, mining the data by adopting an unsupervised learning algorithm, analyzing unknown information hidden behind the data, and analyzing the collinearity and the correlation of each monitoring variable;
establishing a linear or nonlinear prediction model, namely a virtual monitoring model, for the state variable according to the collinearity and the correlation of the water quality on-line monitoring variable easy to monitor in real time and the biochemical reflection state variable difficult to monitor in real time;
and analyzing rules of a machine learning algorithm for constructing virtual monitoring models of different variables of different sewage treatment processes, and summarizing and providing a virtual monitoring model construction guiding principle in the sewage treatment process.
It should be noted that: the easy real-time monitoring variables are water quality on-line monitoring variables including: flow, pH, conductivity, ORP, ammonia nitrogen, suspended matters and the like;
difficult real-time monitoring variables are key state variables that directly participate in biochemical reactions, including: volatile Fatty Acids (VFA), soluble biodegradable COD, particulate biodegradable COD, organic nitrogen, and the like.
In one embodiment, the limitation of the development level of the sensor technology is overcome, the data mining algorithm is adopted to find out the internal connection between the state variable of the sewage biochemical treatment process and the water quality on-line monitoring index, the output variable of the virtual monitoring model is used as the input variable of the advanced control algorithm, the virtual monitoring model of the sewage treatment process is established, and the algorithm selection criterion is established.
Step two, establishing a biochemical reaction kinetic model of the sewage biological treatment process;
in the second step: starting an activated sludge system adopting an AAO process and a pilot-scale experimental device of a biological membrane system adopting an MBBR process, adjusting the installation positions of online instruments such as pH, conductivity, ORP, sludge concentration, dissolved oxygen and the like, additionally installing online ammonia nitrogen and nitrate nitrogen monitoring instruments, simultaneously taking a sewage sample for assay analysis, collecting data in the operation process, and establishing a process operation database;
quantitatively analyzing the activated sludge/biomembrane microbial community structure of the pilot-scale test system by adopting a high-throughput sequencing technology, revealing a response mechanism of a microbial community to the change of the water quality, the water quantity and the operation environment of the system, establishing a response model by combining a process operation database, and quickly and quantitatively analyzing the microbial community structure by adopting a machine learning algorithm;
according to the ASM2d model, the ASM3 model and the biomembrane model, the biochemical reaction kinetic model is respectively established for the two pilot systems, meanwhile, a closed respiration rate measuring device is adopted to measure the respiration rates of the activated sludge/biomembrane under different working conditions, and the parameters of the kinetic model are accurately corrected by combining with the structure data of the microbial community.
What needs to be supplemented is: and establishing a dynamic model parameter, a water quality, a water quantity, an operation condition, a microbial community structure response model and a sewage biological treatment dynamic model rapid modeling method under each operation working condition by combining a process operation database.
In another embodiment, the bottleneck problem restricting the implementation of advanced control in the sewage biological treatment process is disclosed, a sewage biological treatment biochemical reaction kinetic model is established, the influence mechanism of each state variable on the process performance of AAO and MBBR is clarified, and a response model is constructed. Meanwhile, the problem of accurate correction of parameters of a biochemical reaction kinetic model is solved, and a high-level control algorithm for the sewage biological treatment process is constructed.
And step three, constructing a sewage treatment process advanced control model taking the virtual monitoring data as an input variable.
The third step is as follows: taking activated sludge and biofilm samples under different operating conditions, analyzing the morphological structures, surface charges and changes of surface functional groups of the activated sludge and the biofilm by using a scanning electron microscope, a Zeta potential analyzer and a Fourier infrared spectrometer (FT-IR), and establishing a sludge floc and biofilm property database in the operating process;
establishing an activated sludge/biomembrane-water quality fuzzy model by combining an activated sludge/biomembrane property database and a pilot-scale system water quality monitoring result, and analyzing the influence of water quality, water quantity and operation environment change on the appearance structure and surface chemical property of the activated sludge (biomembrane);
the method comprises the steps of establishing a sewage biological treatment process control model by taking a predicted value of a virtual monitoring model and real-time monitoring data of an online instrument as input quantities and combining a sewage biological treatment dynamics model, carrying out simulation on an AlgDesigner platform, and testing a process advanced control algorithm.
Furthermore, the characteristics and the construction conditions of the advanced control algorithm of the sewage biological treatment process under different working conditions of different processes are statistically analyzed, and an intelligent control system of the sewage treatment plant is constructed.
In another embodiment, the predicted value of the virtual monitoring model and the real-time monitoring data of the on-line instrument are used as input quantities to carry out simulation on the sewage biological treatment process. According to the characteristics and the construction conditions of the advanced control algorithm of the sewage biological treatment process under different working conditions of different processes, the intelligent control system with strong practicability for the sewage treatment plant is constructed.
Example II,
A sewage biological treatment process simulation system is characterized in that a sewage treatment overall process advanced control algorithm is established through a water treatment model which is researched and established by a virtual monitoring model input variable and dynamic model and an accurate correction basic method, the rule of a virtual monitoring model component is analyzed, an algorithm selection criterion is provided, and an industrial sewage treatment and resource simulation system is integrated.
The method comprises the steps of mining and establishing a virtual detection model based on state variable data of the whole sewage treatment process, and establishing a kinetic model and researching an accurate correction basic method based on the research of a biochemical reaction mechanism of sewage biological treatment.
In the present embodiment, it is preferred that,
1. the realization of advanced control of the sewage biological treatment process is limited by the real-time acquisition of main state variable data, a virtual monitoring model is established by adopting a data mining and machine learning algorithm, the limitation of the technical development level of a sensor can be overcome, monitoring data are provided for real-time control, meanwhile, a plurality of town sewage treatment plants are selected to establish a database, state variables carrying remarkable process change information are selected to establish a virtual detection model, and the response relation of each state variable and other monitoring indexes can be analyzed.
2. By researching the biochemical reaction mechanism and the biochemical reaction dynamics of the sewage biological treatment and combining the respiration rate of the activated sludge/biomembrane and the molecular biology monitoring result, the influence mechanism of each state variable on the process performance of AAO and MBBR is clarified, the accurate correction mechanism of the parameters of the biochemical reaction dynamics model and the construction scheme of the advanced control algorithm of the sewage biological treatment process are realized, and the bottleneck problem restricting the advanced control implementation of the sewage biological treatment process is revealed.
3. The monitoring and big data analysis and diagnosis of the sewage biological treatment process can realize the energy saving and consumption reduction of the sewage treatment plant, the monitoring and control of the sewage treatment process can obviously reduce the energy consumption and the medicine consumption of the sewage treatment on the premise of guaranteeing the process operation stability, and the application of the advanced monitoring and control model is expected to reduce the operation cost of the sewage treatment plant by 20 to 50 percent in the future.
It is to be understood that the above-described embodiments of the present invention are merely illustrative of or explaining the principles of the invention and are not to be construed as limiting the invention. Therefore, any modification, equivalent replacement, improvement and the like made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. Further, it is intended that the appended claims cover all such variations and modifications as fall within the scope and boundaries of the appended claims or the equivalents of such scope and boundaries.
Claims (9)
1. A sewage biological treatment process simulation method is characterized by comprising the following steps:
step one, establishing a virtual monitoring model of a sewage treatment process, and establishing an algorithm selection criterion;
step two, establishing a biochemical reaction kinetic model of the sewage biological treatment process;
and step three, constructing a sewage treatment process advanced control model taking the virtual monitoring data as an input variable.
2. The simulation method for sewage biological treatment process simulation according to claim 1, wherein in the first step: selecting a biochemical treatment unit of a sewage treatment plant as a research object, installing and adjusting online monitoring instruments such as pH, conductivity, ORP, sludge concentration, dissolved oxygen, ammonia nitrogen, nitrate nitrogen and the like, and collecting real-time monitoring data;
the laboratory test analysis data of the sewage treatment plant in the past year is retrieved, a plurality of sampling points are selected in the biological treatment system for continuous sampling, and the migration and transformation rules of carbon, nitrogen, phosphorus and oxygen in the sewage treatment process are analyzed;
combining real-time monitoring data monitored on line with laboratory test analysis data, mining the data by adopting an unsupervised learning algorithm, analyzing unknown information hidden behind the data, and analyzing the collinearity and the correlation of each monitoring variable;
establishing a linear or nonlinear prediction model, namely a virtual monitoring model, for the state variable according to the collinearity and the correlation of the water quality on-line monitoring variable easy to monitor in real time and the biochemical reflection state variable difficult to monitor in real time;
and analyzing rules of a machine learning algorithm for constructing virtual monitoring models of different variables of different sewage treatment processes, and summarizing and providing a virtual monitoring model construction guiding principle in the sewage treatment process.
3. The simulation method of a wastewater biological treatment process according to claim 2, wherein the easily real-time monitoring variables are water quality on-line monitoring variables comprising: flow, pH, conductivity, ORP, ammonia nitrogen, suspended matters and the like;
difficult real-time monitoring variables are key state variables that directly participate in biochemical reactions, including: volatile Fatty Acid (VFA), soluble biodegradable COD, particulate biodegradable COD, organic nitrogen, and the like.
4. The sewage biological treatment process simulation method according to claim 1, wherein in the second step: starting an activated sludge system adopting an AAO process and a biofilm system pilot-scale experimental device adopting an MBBR process, adjusting the installation positions of online instruments such as pH, conductivity, ORP, sludge concentration, dissolved oxygen and the like, additionally installing online monitoring instruments for ammonia nitrogen and nitrate nitrogen, simultaneously taking a sewage sample for assay analysis, collecting data in the operation process, and establishing a process operation database;
quantitatively analyzing the microbial community structure of the activated sludge/biomembrane of the pilot-scale system by adopting a high-throughput sequencing technology, revealing the response mechanism of the microbial community to the change of the water quality, the water quantity and the operating environment of the system, establishing a response model by combining a process operation database, and quickly and quantitatively analyzing the microbial community structure by adopting a machine learning algorithm;
according to the ASM2d model, the ASM3 model and the biomembrane model, the biochemical reaction kinetic model is respectively established for the two pilot systems, meanwhile, a closed respiration rate measuring device is adopted to measure the respiration rates of the activated sludge/biomembrane under different working conditions, and the parameters of the kinetic model are accurately corrected by combining with the structure data of the microbial community.
5. The simulation method for the sewage biological treatment process according to claim 4, wherein a kinetic model parameter, a water quality, a water quantity, an operation condition, a microbial community structure response model and a sewage biological treatment kinetic model rapid modeling method under each operation condition are established by combining a process operation database.
6. The sewage biological treatment process simulation method of claim 1, wherein in the third step: taking activated sludge and biofilm samples under different operating conditions, analyzing the morphological structures, surface charges and changes of surface functional groups of the activated sludge and the biofilm by using a scanning electron microscope, a Zeta potential analyzer and a Fourier infrared spectrometer (FT-IR), and establishing a sludge floc and biofilm property database in the operating process;
establishing an activated sludge/biomembrane-water quality fuzzy model by combining an activated sludge/biomembrane property database and a pilot system water quality monitoring result, and analyzing the influence of water quality, water quantity and operation environment change on the morphological structure and surface chemical property of the activated sludge (biomembrane);
and establishing a sewage biological treatment process control model by taking the predicted value of the virtual monitoring model and the real-time monitoring data of the online instrument as input quantities and combining a sewage biological treatment dynamic model, and performing simulation and test process advanced control algorithm on the AlgDesigner platform.
7. The simulation method for the biological sewage treatment process according to claim 6, wherein the intelligent control system of the sewage treatment plant is constructed by statistically analyzing the characteristics and construction conditions of the advanced control algorithm for the biological sewage treatment process under different working conditions of different processes.
8. A sewage biological treatment process simulation system is characterized in that a water treatment model which is researched and built through a virtual monitoring model input variable and dynamic model and an accurate correction basic method is used for establishing a sewage treatment overall process advanced control algorithm, analyzing the rule of a virtual monitoring model component, proposing an algorithm selection criterion, and integrating an industrial sewage treatment and resource simulation system.
9. The simulation system of a wastewater biological treatment process according to claim 8, wherein a virtual detection model is established based on the state variable data mining of the whole wastewater treatment process, and a kinetic model and a precise correction basic method are established based on the biochemical reaction mechanism research of wastewater biological treatment.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117602767A (en) * | 2023-12-20 | 2024-02-27 | 石家庄正中科技有限公司 | Efficient intensive denitrification and dephosphorization sewage treatment process |
| CN117950382A (en) * | 2024-03-27 | 2024-04-30 | 中国电子工程设计院股份有限公司 | Method and device for constructing simulation model of pure water preparation system of semiconductor factory |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117602767A (en) * | 2023-12-20 | 2024-02-27 | 石家庄正中科技有限公司 | Efficient intensive denitrification and dephosphorization sewage treatment process |
| CN117950382A (en) * | 2024-03-27 | 2024-04-30 | 中国电子工程设计院股份有限公司 | Method and device for constructing simulation model of pure water preparation system of semiconductor factory |
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