CN113867233B - Control method and system for granular sludge treatment based on pilot-scale research - Google Patents

Control method and system for granular sludge treatment based on pilot-scale research Download PDF

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CN113867233B
CN113867233B CN202111295549.4A CN202111295549A CN113867233B CN 113867233 B CN113867233 B CN 113867233B CN 202111295549 A CN202111295549 A CN 202111295549A CN 113867233 B CN113867233 B CN 113867233B
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granular sludge
sludge treatment
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treatment
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CN113867233A (en
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王驰
徐萍
占晓建
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Longyou County River Dredging Sand Resources Development Co ltd
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Longyou County River Dredging Sand Resources Development Co ltd
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
    • G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
    • G05B19/0423—Input/output
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/20—Pc systems
    • G05B2219/24—Pc safety
    • G05B2219/24215—Scada supervisory control and data acquisition
    • 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

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  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Activated Sludge Processes (AREA)

Abstract

The invention provides a pilot-scale research-based control method and system for granular sludge treatment, wherein the method comprises the following steps: obtaining first equipment information of first aerobic granular sludge reaction equipment; obtaining a parameter set according to the first equipment information; obtaining a first drug administration parameter, a first water distribution parameter, a first aeration parameter, a first screening parameter and a first drainage parameter according to the parameter set; constructing a first granular sludge treatment state space based on pilot plant research; inputting the parameters into a first granular sludge treatment state space to obtain first state parameters; inputting the first state parameter into a granular sludge treatment adjustment model to obtain a first granular sludge treatment parameter; judging whether the first granular sludge treatment parameter meets a preset treatment threshold value; and if so, adjusting the first aerobic granular sludge reaction equipment based on the first granular sludge treatment parameter to treat the granular sludge.

Description

Control method and system for granular sludge treatment based on pilot-scale research
Technical Field
The invention relates to the technical field of intelligent manufacturing, in particular to a control method and a system for granular sludge treatment based on pilot-scale research.
Background
The aerobic granular sludge has higher microbial biomass, has the capability of removing nitrogen and phosphorus and good sedimentation performance, has great application potential in the treatment of industrial wastewater and municipal sewage, and has the obvious advantages of small occupied area and low operating cost.
In the process of aerobic granular sludge sewage treatment, dosage, aeration parameters, sludge discharge devices, drainage parameters and the like need to be manually set according to the sewage treatment requirements, the manual participation degree is high, and different treatment parameters need to be determined based on experiments aiming at different sewage treatments.
However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the inventors of the present application find that the above-mentioned technology has at least the following technical problems:
aerobic granular sludge treatment in the prior art needs to manually perform experiments according to different sewage treatment requirements to determine treatment parameters such as dosage, aeration parameters, sludge discharge devices, drainage parameters and the like, and when different sewage treatment requirements are met, the experimental cost is high, the requirement that sewage treatment reaches the standard cannot be met, and the technical problem that the treatment parameters cannot be accurately and intelligently set according to the sewage treatment requirements exists.
Disclosure of Invention
The embodiment of the application provides a control method and a control system for granular sludge treatment based on pilot-scale research, which are used for manually carrying out experiments to determine treatment parameters such as dosage, aeration parameters, sludge discharge devices, drainage parameters and the like according to different sewage treatment requirements aiming at solving the problem that aerobic granular sludge treatment in the prior art needs to meet different sewage treatment requirements.
In view of the above problems, the embodiments of the present application provide a control method and system for granular sludge treatment based on pilot plant research.
In a first aspect of embodiments of the present application, there is provided a control method for granular sludge treatment based on pilot plant research, the method including: obtaining first equipment information of first aerobic granular sludge reaction equipment; acquiring a drug administration parameter set, a water distribution parameter set, an aeration parameter set, a screening parameter set and a drainage parameter set according to the first equipment information; obtaining a first drug administration parameter, a first water distribution parameter, a first aeration parameter, a first screening parameter and a first drainage parameter according to the drug administration parameter set, the water distribution parameter set, the aeration parameter set, the screening parameter set and the drainage parameter set; constructing a first granular sludge treatment state space based on pilot plant research; inputting the first dosing parameter, the first water distribution parameter, the first aeration parameter, the first screening parameter and the first drainage parameter into the first granular sludge treatment state space to obtain a first state parameter; inputting the first state parameter into a granular sludge treatment adjustment model to obtain a first granular sludge treatment parameter; judging whether the first granular sludge treatment parameter meets a preset treatment threshold value; and if so, adjusting the first aerobic granular sludge reaction equipment based on the first granular sludge treatment parameter to carry out granular sludge treatment.
In a second aspect of the embodiments of the present application, there is provided a pilot-scale study-based control system for granular sludge treatment, the system comprising: a first obtaining unit for obtaining first apparatus information of a first aerobic granular sludge reaction apparatus; a second obtaining unit, configured to obtain a drug administration parameter set, a water distribution parameter set, an aeration parameter set, a screening parameter set, and a drainage parameter set according to the first device information; a third obtaining unit, configured to obtain a first dosing parameter, a first water distribution parameter, a first aeration parameter, a first screening parameter, and a first drainage parameter according to the dosing parameter set, the water distribution parameter set, the aeration parameter set, the screening parameter set, and the drainage parameter set; a first construction unit for constructing a first granular sludge treatment status space based on pilot plant studies; the first treatment unit is used for inputting the first dosing parameter, the first water distribution parameter, the first aeration parameter, the first screening parameter and the first drainage parameter into the first granular sludge treatment state space to obtain a first state parameter; the second processing unit is used for inputting the first state parameter into a granular sludge treatment adjustment model to obtain a first granular sludge treatment parameter; a first judging unit for judging whether the first granular sludge treatment parameter meets a preset treatment threshold value; and the first management unit is used for adjusting the first aerobic granular sludge reaction equipment based on the first granular sludge treatment parameter to perform granular sludge treatment if the first aerobic granular sludge treatment parameter is met.
In a third aspect of the embodiments of the present application, there is provided a control system for granular sludge treatment based on pilot plant research, including: a processor coupled to a memory for storing a program that, when executed by the processor, causes a system to perform the steps of the method according to the first aspect.
One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:
according to the method provided by the embodiment of the application, a parameter set is obtained by obtaining first equipment information of first aerobic granular sludge reaction equipment for currently performing granular sludge sewage treatment, a first parameter is selected and obtained according to the parameter set, a first granular sludge treatment state space is constructed based on pilot plant research, the first parameter is input into the first granular sludge treatment state space, the first state parameter obtained after sewage treatment can be performed based on the first parameter, the first state parameter is input into a granular sludge treatment adjustment model to obtain a first granular sludge treatment parameter, whether the first granular sludge treatment parameter meets a preset treatment threshold value is judged, and if the first granular sludge treatment parameter meets the preset treatment threshold value, granular sludge treatment is performed. The embodiment of the application obtains the corresponding state parameters after the corresponding treatment parameters are correspondingly treated by obtaining the treatment parameters for carrying out the granular sludge sewage treatment and constructing the first granular sludge treatment state space based on the pilot plant research and the Markov decision process, judges whether the corresponding first granular sludge treatment parameters meet the preset treatment threshold value, further carrying out granular sludge sewage treatment, the embodiment of the application constructs a quantifiable control method for granular sludge sewage treatment, the granular sludge sewage treatment parameters can be set according to different sewage treatment requirements, the granular sludge sewage treatment parameters meeting the sewage treatment requirements can be accurately and intelligently obtained, the manual participation degree is reduced, the manual experiment cost is reduced, the sewage treatment efficiency is improved, and the technical effect of accurately and intelligently setting the sewage treatment parameters according to the sewage treatment requirements is achieved.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
FIG. 1 is a schematic flow chart of a control method for granular sludge treatment based on pilot scale research provided in an embodiment of the present application;
fig. 2 is a schematic flow chart of a first granular sludge treatment state space constructed in a pilot plant study-based granular sludge treatment control method according to an embodiment of the present application;
fig. 3 is a schematic diagram of a markov decision process in a control method for granular sludge treatment based on pilot scale research according to an embodiment of the present application;
FIG. 4 is a schematic structural diagram of a control system for granular sludge treatment based on pilot plant research according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.
Description of reference numerals: the device comprises a first obtaining unit 11, a second obtaining unit 12, a third obtaining unit 13, a first constructing unit 14, a first processing unit 15, a second processing unit 16, a first judging unit 17, a first managing unit 18, an electronic device 300, a memory 301, a processor 302, a communication interface 303 and a bus architecture 304.
Detailed Description
The embodiment of the application provides a control method and a system for granular sludge treatment based on pilot scale research, which are used for solving the technical problems that treatment parameters such as dosage, aeration parameters, sludge discharge devices, drainage parameters and the like can not be set accurately and intelligently according to sewage treatment requirements due to the fact that experiments are carried out manually to determine and set the treatment parameters according to different sewage treatment requirements when different sewage treatment requirements are met, and the experiment cost is high, so that the sewage treatment can not reach the standard.
According to the method provided by the embodiment of the application, a parameter set is obtained by obtaining first equipment information of first aerobic granular sludge reaction equipment for currently performing granular sludge sewage treatment, a first parameter is selected and obtained according to the parameter set, a first granular sludge treatment state space is constructed based on pilot plant research, the first parameter is input into the first granular sludge treatment state space, the first state parameter obtained after sewage treatment can be performed based on the first parameter, the first state parameter is input into a granular sludge treatment adjustment model to obtain a first granular sludge treatment parameter, whether the first granular sludge treatment parameter meets a preset treatment threshold value is judged, and if the first granular sludge treatment parameter meets the preset treatment threshold value, granular sludge treatment is performed. The embodiment of the application acquires the treatment parameters for treating the granular sludge sewage, constructs the first granular sludge treatment state space based on pilot study and Markov decision process, acquires the corresponding state parameters after the corresponding treatment parameters are correspondingly treated, judges whether the corresponding first granular sludge treatment parameters meet the preset treatment threshold value, further carrying out granular sludge sewage treatment, the embodiment of the application constructs a quantifiable control method for granular sludge sewage treatment, the granular sludge sewage treatment parameters can be set according to different sewage treatment requirements, the granular sludge sewage treatment parameters meeting the sewage treatment requirements can be accurately and intelligently obtained, the manual participation degree is reduced, the manual experiment cost is reduced, the sewage treatment efficiency is improved, and the technical effect of accurately and intelligently setting the treatment parameters according to the sewage treatment requirements is achieved.
Summary of the application
The aerobic granular sludge has higher microbial biomass, has the capability of removing nitrogen and phosphorus and good sedimentation performance, has great application potential in the treatment of industrial wastewater and municipal sewage, and has the obvious advantages of small occupied area and low operating cost. In the process of aerobic granular sludge sewage treatment, dosage, aeration parameters, sludge discharge devices, drainage parameters and the like need to be manually set according to the sewage treatment requirements, the manual participation degree is high, and different treatment parameters need to be determined based on experiments aiming at different sewage treatments. Aerobic granular sludge treatment in the prior art needs to manually perform experiments according to different sewage treatment requirements to determine treatment parameters such as dosage, aeration parameters, sludge discharge devices, drainage parameters and the like, and when different sewage treatment requirements are met, the experimental cost is high, the requirement that sewage treatment reaches the standard cannot be met, and the technical problem that the treatment parameters cannot be accurately and intelligently set according to the sewage treatment requirements exists.
In view of the above technical problems, the technical solution provided by the present application has the following general idea:
obtaining first equipment information of first aerobic granular sludge reaction equipment; acquiring a drug administration parameter set, a water distribution parameter set, an aeration parameter set, a screening parameter set and a drainage parameter set according to the first equipment information; obtaining a first drug administration parameter, a first water distribution parameter, a first aeration parameter, a first screening parameter and a first drainage parameter according to the drug administration parameter set, the water distribution parameter set, the aeration parameter set, the screening parameter set and the drainage parameter set; constructing a first granular sludge treatment state space based on pilot plant research; inputting the first dosing parameter, the first water distribution parameter, the first aeration parameter, the first screening parameter and the first drainage parameter into the first granular sludge treatment state space to obtain a first state parameter; inputting the first state parameter into a granular sludge treatment adjustment model to obtain a first granular sludge treatment parameter; judging whether the first granular sludge treatment parameter meets a preset treatment threshold value; and if so, adjusting the first aerobic granular sludge reaction equipment based on the first granular sludge treatment parameter to carry out granular sludge treatment.
Having described the basic principles of the present application, the following embodiments will be described in detail and fully with reference to the accompanying drawings, it being understood that the embodiments described are only some embodiments of the present application, and not all embodiments of the present application, and that the present application is not limited to the exemplary embodiments described herein. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application. It should be further noted that, for the convenience of description, only some but not all of the elements relevant to the present application are shown in the drawings.
Example one
As shown in fig. 1, the present application provides a control method for granular sludge treatment based on pilot plant research, which includes:
s100: obtaining first equipment information of first aerobic granular sludge reaction equipment;
specifically, Aerobic Granular Sludge (AGS) is Granular activated Sludge formed by microbial self-aggregation, and the Aerobic Granular Sludge technology has the obvious advantages of small floor area and low operation cost. The aerobic granular sludge technology belongs to a revolutionary technology in the aspect of aerobic biological treatment in the field of sewage treatment, can completely replace the aerobic treatment processes such as the traditional flocculent activated sludge method and the like, and has significant meaning for solving the problems of capacity expansion modification, quality improvement and efficiency improvement of the current sewage treatment plant.
The first aerobic granular sludge reaction equipment is equipment for performing aerobic granular sludge sewage treatment reaction, can be one equipment or a collection of a plurality of equipment, and comprises a water distribution device, an aeration device, a sludge screening device, a water discharge device, a control system and the like, and aerobic granular sludge sewage treatment is performed in the first aerobic granular sludge reaction equipment. The first equipment information is the equipment information of the first aerobic granular sludge reaction equipment, and specifically includes, but is not limited to, equipment model, specification, adjustable parameters, and the like.
S200: acquiring a drug administration parameter set, a water distribution parameter set, an aeration parameter set, a screening parameter set and a drainage parameter set according to the first equipment information;
specifically, the administration parameter set, the water distribution parameter set, the aeration parameter set, the screening parameter set, and the drainage parameter set are parameter sets that can be adjusted and obtained based on the first device information, and more preferably, the administration parameter set, the water distribution parameter, the aeration parameter, the screening parameter, and the drainage parameter set represent parameter sets within an adjustable range of the administration parameter, the water distribution parameter, the aeration parameter, the screening parameter, and the drainage parameter.
S300: obtaining a first drug administration parameter, a first water distribution parameter, a first aeration parameter, a first screening parameter and a first drainage parameter according to the drug administration parameter set, the water distribution parameter set, the aeration parameter set, the screening parameter set and the drainage parameter set;
specifically, in the above dosing parameter set, water distribution parameter set, aeration parameter set, screening parameter set and drainage parameter set, according to the current sewage treatment requirement, one parameter in each parameter set is selected to obtain the above first dosing parameter, first water distribution parameter, first aeration parameter, first screening parameter and first drainage parameter.
Further, the first dosing parameter, the first water distribution parameter, the first aeration parameter, the first screening parameter, and the first drainage parameter may be obtained through Computational Fluid Dynamics (CFD) modeling simulation. Specifically, based on CFD modeling simulation, the design of the anaerobic granular sludge reactor in the prior art is used for reference, and the more suitable parameters are obtained.
Illustratively, the first dosing parameters include the dosing amount and dosing rate of the aerobic granular sludge, and the like; the first water distribution parameters comprise water distribution design modes and the like, and different water distribution design modes are subjected to numerical processing and selected; the first aeration parameters comprise an aeration arrangement mode, aeration intensity parameters and the like; the first screening parameters comprise screening parameters of a sludge discharge device in the equipment, sludge discharge sealing measures and the like; the first drainage parameters comprise a drainage arrangement mode, a scum removal mode, an algae prevention mode and the like.
S400: constructing a first granular sludge treatment state space based on pilot plant research;
specifically, the pilot-scale study in the examples of the present application is an experimental study performed on aerobic granular sludge sewage treatment before the sewage treatment is quantitatively performed. In the pilot-scale research, the experimental effect of a laboratory is amplified for carrying out experimental balance of the parameters and the control conditions so as to achieve the aim of achieving the treatment standard by adopting aerobic granular sludge to carry out sewage treatment.
According to the embodiment of the application, a sufficient experiment data base is obtained according to the historical data and big data of the pilot test experiment, and a Markov Decision Process (MDP) is combined to construct a first granular sludge treatment state space, wherein the first granular sludge treatment state space comprises treatment node state data of first aerobic granular sludge reaction equipment and corresponding parameter adjustment behaviors.
The treatment node state data includes a sewage treatment state of the first aerobic granular sludge reaction equipment in a node state of a certain dosing parameter, a water distribution parameter, an aeration parameter, a screening parameter and a drainage parameter. And the parameter adjusting action comprises an adjusting action of adjusting the drug administration parameter, the water distribution parameter, the aeration parameter, the screening parameter and the drainage parameter based on the sewage treatment state, and after the parameter adjusting action is carried out, the first aerobic granular sludge reaction equipment can reach a new treatment node state.
S500: inputting the first dosing parameter, the first water distribution parameter, the first aeration parameter, the first screening parameter and the first drainage parameter into the first granular sludge treatment state space to obtain a first state parameter;
specifically, as described above, the first granular sludge treatment status space includes the treatment node status data of the first aerobic granular sludge reaction equipment, and the corresponding parameter adjustment behavior. And inputting the currently obtained first dosing parameter, the first water distribution parameter, the first aeration parameter, the first screening parameter and the first drainage parameter into a first granular sludge treatment state space to obtain a first state parameter which comprises corresponding node state data, wherein the node state data is obtained by processing the last node state data in the Markov chain through the first dosing parameter, the first water distribution parameter, the first aeration parameter, the first screening parameter and the first drainage parameter.
S600: inputting the first state parameter into a granular sludge treatment adjustment model to obtain a first granular sludge treatment parameter;
specifically, the first state parameter includes corresponding node state data, and the first state parameter is input into the granular sludge treatment adjustment model to obtain a first granular sludge treatment parameter, where the first granular sludge treatment parameter includes a sewage treatment parameter for the aerobic granular sludge sewage treatment reaction performed by the first aerobic granular sludge reaction device under the node state data of the first state parameter.
The granular sludge treatment adjustment model is a Neural network model in machine learning, and a Neural Network (NN) is a complex Neural network system formed by widely interconnecting a large number of simple processing units (called neurons), reflects many basic characteristics of human brain functions, and is a highly complex nonlinear dynamical learning system. And inputting a first state parameter into the granular sludge treatment adjustment model, namely inputting node state data corresponding to the first dosing parameter, the first water distribution parameter, the first aeration parameter, the first screening parameter and the first drainage parameter, so that the model can output the corresponding parameter and corresponding sewage, and the sewage treatment level reached by the first aerobic granular sludge reaction equipment is the first granular sludge treatment parameter. S700: judging whether the first granular sludge treatment parameter meets a preset treatment threshold value;
s800: and if so, adjusting the first aerobic granular sludge reaction equipment based on the first granular sludge treatment parameter to carry out granular sludge treatment.
Specifically, according to different sewage treatment requirements, after a first dosing parameter, a first water distribution parameter, a first aeration parameter, a first screening parameter and a first drainage parameter are set, a corresponding first state parameter is obtained, a corresponding first granular sludge treatment parameter is further obtained, whether the sewage treatment level corresponding to the first granular sludge treatment parameter meets the sewage treatment requirement is further judged, if yes, the current selected parameter is qualified, sewage treatment is carried out by adopting the parameter, or if not, the parameter needs to be updated, and test judgment is carried out again.
The preset treatment threshold is different according to different sewage treatment requirements, and is set according to the requirements of the technical personnel in the field on sewage treatment, and is judged in a numerical manner. For example, the preset processing threshold may include: the sludge elutriation rate of the sludge screening device technology reaching a specific sedimentation speed is more than 80 percent; the dead zone volume in the first aerobic granular sludge reaction equipment is less than or equal to 1 percent; compared with the traditional denitrification and dephosphorization sewage treatment process, the aerobic granular sludge process saves the energy consumption by more than or equal to 30 percent and the like.
The embodiment of the application obtains the corresponding state parameters after the corresponding treatment parameters are correspondingly treated by obtaining the treatment parameters for carrying out the granular sludge sewage treatment and constructing the first granular sludge treatment state space based on the pilot plant research and the Markov decision process, judges whether the corresponding first granular sludge treatment parameters meet the preset treatment threshold value, further carrying out granular sludge sewage treatment, the embodiment of the application constructs a quantifiable control method for granular sludge sewage treatment, the granular sludge sewage treatment parameters can be set according to different sewage treatment requirements, the granular sludge sewage treatment parameters meeting the sewage treatment requirements can be accurately and intelligently obtained, the manual participation degree is reduced, the manual experiment cost is reduced, the sewage treatment efficiency is improved, and the technical effect of accurately and intelligently setting the treatment parameters according to the sewage treatment requirements is achieved.
As shown in fig. 2, step S400 in the method provided in the embodiment of the present application includes:
s410: obtaining a first parameter node state comprising the first drug administration parameter, the first water distribution parameter, the first aeration parameter, the first screening parameter and the first drainage parameter and a corresponding first parameter adjustment behavior based on the historical experimental data of the pilot plant research;
s420: inputting the first parameter node state and the first parameter adjustment behavior into a granular sludge treatment evaluation model to obtain a first evaluation result;
s430: obtaining a second parameter node state according to the first evaluation result, wherein the second parameter node state is the parameter node state of the first parameter node state after being adjusted by the first parameter adjustment behavior;
s440: and constructing to obtain the first granular sludge treatment state space until an Nth evaluation result, an Nth parameter node state and an Nth parameter adjustment behavior are obtained.
Step S440 in the embodiment of the present application includes:
s441: obtaining a mapping relation between the Nth parameter node state and the Nth-1 parameter adjustment behavior;
s442: and constructing the first granular sludge treatment state space according to the mapping relation.
Specifically, the first parameter node state is a node state in a parameter adjustment process performed by the first aerobic granular sludge reaction equipment according to a sewage treatment requirement before sewage treatment, and each node state comprises a corresponding dosing parameter, a corresponding water distribution parameter, a corresponding aeration parameter, a corresponding screening parameter and a corresponding drainage parameter. The first parameter adjustment behavior is a parameter adjustment behavior made by a technician aiming at the first parameter node state, and the first aerobic granular sludge reaction equipment can reach a new parameter node state, namely a second parameter node state.
If the parameter corresponding to the first parameter node state cannot meet the sewage treatment requirement, for example, the water distribution parameter cannot meet the requirement, the parameter of the first parameter node state is adjusted through the first parameter adjustment behavior, so that the sewage treatment requirement is met. In the adjustment process of the parameters, the adjustment of a single parameter does not only affect the function corresponding to the parameter, but also affects the functions of other parts in the first aerobic granular sludge reactor, so that a first granular sludge treatment state space needs to be constructed to obtain the corresponding first parameter node state and the first parameter adjustment behavior.
The granular sludge treatment evaluation model is an Artificial Neural network model (Artificial Neural Networks) in machine learning, and the Artificial Neural network is a description of first-order characteristics of a human brain system. Briefly, it is a mathematical model. And (3) carrying out continuous self-training learning through training of a large amount of training data, wherein each set of training data comprises a first parameter node state and identification information for identifying a first evaluation result, and when the output information of the granular sludge treatment evaluation model reaches a preset accuracy rate/convergence state, the supervised learning process is ended. Then, the first parameter node state is input into the granular sludge treatment evaluation model, and an output result is obtained, wherein the output result comprises a first evaluation result.
And obtaining the node state of the second parameter based on the first evaluation result. The second parameter node state may be used to evaluate the tuning effect of the first parameter tuning action. And constructing and obtaining the first granular sludge treatment state space according to the first evaluation result, the first parameter node state and the first parameter adjustment behavior until the Nth evaluation result, the Nth parameter node state and the Nth parameter adjustment behavior are obtained. Figure 3 shows a possible schematic diagram of the markov decision process in an embodiment of the present application, the first aerobic granular sludge reaction facility at each time tIs in a state S1Based on this state S1The technician makes a first parameter adjustment action a1Acting on the first aerobic granular sludge reaction equipment, the first aerobic granular sludge reaction equipment is rewarded with R1And reaches the next state S2Thus, S is obtainedn。
The N-1 parameter adjustment behavior is used for carrying out targeted parameter adjustment on the N-1 parameter node state, when the N-1 parameter adjustment behavior is finished, the parameter state of the first aerobic granular sludge reaction equipment is changed, and the N parameter node state represents parameter state information after the N-1 parameter adjustment behavior is finished, so that the N-1 parameter node state and the N-1 parameter adjustment behavior have one-to-one mapping relation, and the N-1 parameter adjustment behavior can be obtained by obtaining the N-1 parameter node state, and vice versa. And constructing a first granular sludge treatment state space according to the mapping relation.
The embodiment of the application constructs the first granular sludge treatment state space based on the Markov decision process, can comprehensively reflect the real-time information of the parameter node state and the parameter adjustment behavior of the first aerobic granular sludge reaction equipment, after inputting the first dosing parameter, the first water distribution parameter, the first aeration parameter, the first screening parameter and the first drainage parameter, corresponding first granular sludge treatment parameters can be obtained, if the first granular sludge treatment parameters do not meet the preset treatment threshold, corresponding first parameter adjustment behaviors can be obtained based on the first granular sludge treatment state space, the parameters are adjusted until the corresponding parameter node state can meet the preset threshold value, the real-time parameter node state of the equipment can be accurately obtained, and the corresponding parameter adjustment behavior is obtained according to the state of the parameter node, so that the technical effect of accurately and intelligently obtaining the first state parameter data is achieved.
Step S410 in the embodiment of the present application includes:
s411: acquiring first parameter historical data of the first drug administration parameter, the first water distribution parameter, the first aeration parameter, the first screening parameter and the first drainage parameter based on historical experimental data of pilot plant research;
s412: acquiring processing node state historical data of the first aerobic granular sludge reaction equipment based on historical experimental data of pilot plant research, wherein processing node states in the processing node state historical data correspond to parameters in the first parameter historical data one to one;
s413: and obtaining the first parameter node state and the first parameter adjustment behavior according to the first parameter historical data and the processing node state historical data.
Specifically, based on the historical data and big data of pilot-scale research, first parameter historical data comprising a first dosing parameter, a first water distribution parameter, a first aeration parameter, a first screening parameter and a first drainage parameter are obtained, and processing node state historical data of a first aerobic granular sludge reaction device is obtained. And processing node states in the processing node state historical data correspond to parameters in the first parameter historical data one to one.
And acquiring a first parameter node state and a first parameter adjustment behavior based on the first parameter historical data and the processing node state historical data, wherein the two adjacent parameter node states respectively correspond to the processing node state historical data and the first parameter historical data, and the corresponding first parameter adjustment behavior can be acquired according to the parameter data difference between the two first parameter historical data. According to the embodiment of the application, accurate first parameter node state and first parameter adjustment behavior can be obtained through the historical data based on the pilot test experiment, so that a first granular sludge treatment state space is constructed, and the accuracy and the stability of the first granular sludge treatment state space can be improved.
Step S411 in the method provided in the embodiment of the present application includes:
s4111: acquiring parameter historical data of the first drug administration parameter, the first water distribution parameter, the first aeration parameter, the first screening parameter and the first drainage parameter based on historical experimental data of pilot plant research;
s4112: and performing principal component analysis linear dimensionality reduction on the parameter historical data to obtain the first parameter historical data.
Wherein step S4112 includes:
performing decentralized processing on the parameter historical data to obtain decentralized parameter historical data;
obtaining a first covariance matrix of the decentralized parameter historical data;
calculating the first covariance matrix to obtain a first eigenvalue and a first eigenvector of the first covariance matrix;
and projecting the parameter historical data to the first feature vector to obtain the first parameter historical data.
Specifically, because the data information amount in the first parameter historical data obtained based on the pilot study historical data is large and includes partially redundant data, the dimension reduction of the first parameter historical data is required, and the dimension reduction of the processing node state historical data can be performed to reduce the data dimension. The Principal Component Analysis (PCA) is a linear dimension reduction method for data processing, and can reduce the dimension of the original feature under the condition of ensuring that the information content is not lost as much as possible, that is, project the original feature to the dimension with the maximum projection information content as much as possible. And projecting the original features onto the dimensions to minimize the loss of information amount after dimension reduction.
Specifically, the extracted parameter history data is subjected to a digitization process, and processed parameter history data is obtained. And then centralizing each parameter data in the parameter historical data, firstly solving the average value of each parameter data in the parameter historical data, then subtracting the average value from each parameter data for all samples, and then obtaining new parameter data, wherein the decentralized parameter historical data is formed by the new parameter data and is a data matrix. And then, calculating the eigenvalue and the eigenvector of the first covariance matrix through matrix operation, wherein each eigenvalue corresponds to one eigenvector. And selecting the largest first K characteristic values and the corresponding characteristic vectors from the obtained first characteristic vectors, and projecting the original parameter data in the parameter historical data onto the selected characteristic vectors to obtain the first parameter historical data after dimension reduction.
According to the embodiment of the application, the parameter data in the parameter historical data are subjected to dimensionality reduction treatment through a principal component analysis method, and the redundant data are removed on the premise that the information quantity is ensured, so that the sample quantity of the parameter data in the first parameter historical data is reduced, the loss of the information quantity after dimensionality reduction is minimum, the calculation cost for constructing a first granular sludge treatment state space is reduced, and the technical effect of improving the treatment efficiency of the method is achieved.
Step S600 in the method provided in the embodiment of the present application includes:
s610: inputting the first state parameter into the granular sludge treatment adjustment model;
s620: the granular sludge treatment adjustment model is obtained by training a plurality of groups of training data to a preset state or convergence, wherein each group of training data in the plurality of groups of training data comprises: a first status parameter and identification information for identifying the first granular sludge treatment parameter;
s630: and obtaining an output result of the granular sludge treatment adjustment model, wherein the output result comprises the first granular sludge treatment parameter.
Specifically, the granular sludge treatment adjustment model can perform continuous self-training learning according to training data, and each group in a plurality of groups of training data comprises: the first state parameter and identification information for identifying the first granular sludge treatment parameter, the granular sludge treatment adjustment model continuously self-corrects, and when the output information of the granular sludge treatment adjustment model reaches a predetermined accuracy/convergence state, the supervised learning process is ended. Through carrying out data training to the granular sludge treatment adjustment model, the granular sludge treatment adjustment model is more accurate in processing input data, and further the first output granular sludge treatment parameter is more accurate, and the technical effect of accurately obtaining data information is achieved.
To sum up, in the embodiments of the present application, by obtaining processing parameters for granular sludge sewage treatment, processing historical parameter data by a principal component analysis method, constructing a first granular sludge treatment state space based on pilot plant research and a markov decision process, obtaining corresponding node state parameters after corresponding treatment of parameter adjustment behaviors, and determining whether the corresponding first granular sludge treatment parameters satisfy a preset treatment threshold, and further performing granular sludge sewage treatment, a quantifiable control method for granular sludge sewage treatment is constructed, and the granular sludge sewage treatment parameters can be set according to different sewage treatment requirements, so that the granular sludge sewage treatment parameters satisfying the sewage treatment requirements can be accurately and intelligently obtained, the degree of human involvement is reduced, the cost of human experiments is reduced, and the sewage treatment efficiency is improved, and accurate, and high-quality granular sludge treatment can be achieved, The technical effect of intelligently setting the sewage treatment parameters according to the sewage treatment requirements is achieved.
Example two
Based on the same inventive concept as the control method of granular sludge treatment based on pilot study in the previous embodiment, as shown in fig. 4, the present embodiment provides a control system of granular sludge treatment based on pilot study, wherein the system comprises:
a first obtaining unit 11, the first obtaining unit 11 being configured to obtain first device information of a first aerobic granular sludge reaction device;
a second obtaining unit 12, where the second obtaining unit 12 is configured to obtain a drug administration parameter set, a water distribution parameter set, an aeration parameter set, a screening parameter set, and a drainage parameter set according to the first device information;
a third obtaining unit 13, where the third obtaining unit 13 is configured to obtain a first dosing parameter, a first water distribution parameter, a first aeration parameter, a first screening parameter, and a first drainage parameter according to the dosing parameter set, the water distribution parameter set, the aeration parameter set, the screening parameter set, and the drainage parameter set;
a first construction unit 14, the first construction unit 14 being configured to construct a first granular sludge treatment status space based on a pilot study;
the first treatment unit 15 is used for inputting the first dosing parameter, the first water distribution parameter, the first aeration parameter, the first screening parameter and the first drainage parameter into the first granular sludge treatment state space to obtain a first state parameter;
the second processing unit 16, the second processing unit 16 is configured to input the first state parameter into a granular sludge treatment adjustment model, so as to obtain a first granular sludge treatment parameter;
a first judging unit 17, wherein the first judging unit 17 is used for judging whether the first granular sludge treatment parameter meets a preset treatment threshold value;
a first management unit 18, wherein the first management unit 18 is configured to adjust the first aerobic granular sludge reaction device based on the first granular sludge treatment parameter to perform granular sludge treatment if the first aerobic granular sludge treatment parameter is met.
Further, the system further comprises:
a fourth obtaining unit, configured to obtain, based on historical experimental data of a pilot plant study, a first parameter node state including the first dosing parameter, the first water distribution parameter, the first aeration parameter, the first screening parameter, and the first drainage parameter, and a corresponding first parameter adjustment behavior;
a third processing unit, configured to input the first parameter node state and the first parameter adjustment behavior into a granular sludge treatment evaluation model to obtain a first evaluation result;
a fourth processing unit, configured to obtain a second parameter node state according to the first evaluation result, where the second parameter node state is a parameter node state obtained after the first parameter node state is adjusted by the first parameter adjustment behavior;
and the second construction unit is used for constructing and obtaining the first granular sludge treatment state space until obtaining the Nth evaluation result, the Nth parameter node state and the Nth parameter adjustment behavior.
Further, the system further comprises:
a fifth processing unit, configured to obtain a mapping relationship between the nth parameter node state and an nth-1 parameter adjustment behavior;
a third construction unit configured to construct the first granular sludge treatment state space according to the mapping relationship.
Further, the system further comprises:
a fifth obtaining unit, configured to obtain first parameter historical data of the first drug administration parameter, the first water distribution parameter, the first aeration parameter, the first screening parameter, and the first drainage parameter based on historical experimental data of a pilot plant study;
a sixth obtaining unit, configured to obtain processing node state historical data of the first aerobic granular sludge reaction device based on historical experimental data of a pilot plant study, where processing node states in the processing node state historical data correspond to parameters in the first parameter historical data one to one;
a sixth processing unit, configured to obtain the first parameter node state and the first parameter adjustment behavior according to the first parameter history data and the processing node state history data.
Further, the system further comprises:
a seventh obtaining unit, configured to obtain parameter historical data of the first drug administration parameter, the first water distribution parameter, the first aeration parameter, the first screening parameter, and the first drainage parameter based on historical experimental data of a pilot plant study;
and the seventh processing unit is used for performing principal component analysis linear dimensionality reduction on the parameter historical data to obtain the first parameter historical data.
Further, the system further comprises:
the eighth processing unit is used for performing decentralized processing on the parameter historical data to obtain decentralized parameter historical data;
a ninth processing unit to obtain a first covariance matrix of the de-centralization parameter history data;
a tenth processing unit, configured to perform operation on the first covariance matrix to obtain a first eigenvalue and a first eigenvector of the first covariance matrix;
an eleventh processing unit, configured to project the parameter history data to the first feature vector, to obtain the first parameter history data.
Further, the system further comprises:
a twelfth processing unit for inputting the first state parameter into the granular sludge treatment adjustment model;
a thirteenth processing unit, configured to train the particle sludge treatment adjustment model to a predetermined state or to be obtained by convergence from multiple sets of training data, where each set of training data in the multiple sets of training data includes: a first status parameter and identification information for identifying the first granular sludge treatment parameter;
a fourteenth processing unit for obtaining an output result of the granular sludge treatment adjustment model, the output result including the first granular sludge treatment parameter.
Exemplary electronic device
The electronic device of the embodiment of the present application is described below with reference to figure 5,
based on the same inventive concept as the control method for granular sludge treatment based on pilot scale research in the foregoing embodiments, the present application embodiment further provides a control system for granular sludge treatment based on pilot scale research, including: a processor coupled to a memory, the memory for storing a program that, when executed by the processor, causes the system to perform the steps of the method of embodiment one.
The electronic device 300 includes: processor 302, communication interface 303, memory 301. Optionally, the electronic device 300 may also include a bus architecture 304. Wherein, the communication interface 303, the processor 302 and the memory 301 may be connected to each other through a bus architecture 304; the bus architecture 304 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus architecture 304 may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown in FIG. 5, but this is not intended to represent only one bus or type of bus.
Processor 302 may be a CPU, microprocessor, ASIC, or one or more integrated circuits for controlling the execution of programs in accordance with the teachings of the present application.
The communication interface 303 may be any device, such as a transceiver, for communicating with other devices or communication networks, such as an ethernet, a Radio Access Network (RAN), a Wireless Local Area Network (WLAN), a wired access network, and the like.
The memory 301 may be, but is not limited to, a ROM or other type of static storage device that can store static information and instructions, a RAM or other type of dynamic storage device that can store information and instructions, an electrically erasable Programmable read-only memory (EEPROM), a compact disk read-only memory (CD-ROM) or other optical disk storage, optical disk storage (including compact disk, laser disk, optical disk, digital versatile disk, blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory may be self-contained and coupled to the processor through a bus architecture 304. The memory may also be integral to the processor.
The memory 301 is used for storing computer-executable instructions for executing the present application, and is controlled by the processor 302 to execute. The processor 302 is used for executing computer-executable instructions stored in the memory 301, so as to implement a pilot-scale study-based control method for granular sludge treatment provided by the above-mentioned embodiments of the present application.
Optionally, the computer-executable instructions in this embodiment may also be referred to as application program codes, which is not specifically limited in this embodiment.
The embodiment of the application adopts the principal component analysis method to process historical parameter data by obtaining the processing parameters for the granular sludge sewage treatment, constructing a first granular sludge treatment state space based on pilot-scale research and Markov decision-making process, obtaining corresponding node state parameters after corresponding treatment of parameter adjustment behaviors, judging whether the corresponding first granular sludge treatment parameters meet preset treatment threshold values, further carrying out granular sludge sewage treatment, the embodiment of the application constructs a quantifiable control method for granular sludge sewage treatment, the granular sludge sewage treatment parameters can be set according to different sewage treatment requirements, the granular sludge sewage treatment parameters meeting the sewage treatment requirements can be accurately and intelligently obtained, the manual participation degree is reduced, the manual experiment cost is reduced, the sewage treatment efficiency is improved, and the technical effect of accurately and intelligently setting the sewage treatment parameters according to the sewage treatment requirements is achieved.
Those of ordinary skill in the art will understand that: the various numbers of the first, second, etc. mentioned in this application are only used for the convenience of description and are not used to limit the scope of the embodiments of this application, nor to indicate the order of precedence. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one" means one or more. At least two means two or more. "at least one," "any," or similar expressions refer to any combination of these items, including any combination of singular or plural items. For example, at least one (one ) of a, b, or c, may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or multiple.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device including one or more available media integrated servers, data centers, and the like. The usable medium may be a magnetic medium (e.g., a floppy Disk, a hard Disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a Solid State Disk (SSD)), among others.
The various illustrative logical units and circuits described in this application may be implemented or operated upon by design of a general purpose processor, a digital signal processor, an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, e.g., a digital signal processor and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a digital signal processor core, or any other similar configuration.
The steps of a method or algorithm described in the embodiments herein may be embodied directly in hardware, in a software element executed by a processor, or in a combination of the two. The software cells may be stored in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. For example, a storage medium may be coupled to the processor such the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. The processor and the storage medium may reside in an ASIC, which may be disposed in a terminal. In the alternative, the processor and the storage medium may reside as discrete components in a terminal. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Although the present application has been described in conjunction with specific features and embodiments thereof, it will be evident that various modifications and combinations can be made thereto without departing from the spirit and scope of the application. Accordingly, the specification and figures are merely exemplary of the present application as defined in the appended claims and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of the present application. It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations.

Claims (8)

1. A pilot-scale study-based control method for granular sludge treatment, wherein the method comprises the following steps:
obtaining first equipment information of first aerobic granular sludge reaction equipment;
acquiring a drug administration parameter set, a water distribution parameter set, an aeration parameter set, a screening parameter set and a drainage parameter set according to the first equipment information;
obtaining a first drug administration parameter, a first water distribution parameter, a first aeration parameter, a first screening parameter and a first drainage parameter according to the drug administration parameter set, the water distribution parameter set, the aeration parameter set, the screening parameter set and the drainage parameter set;
constructing a first granular sludge treatment state space based on pilot plant research;
inputting the first dosing parameter, the first water distribution parameter, the first aeration parameter, the first screening parameter and the first drainage parameter into the first granular sludge treatment state space to obtain a first state parameter;
inputting the first state parameter into a granular sludge treatment adjustment model to obtain a first granular sludge treatment parameter;
judging whether the first granular sludge treatment parameter meets a preset treatment threshold value;
if so, adjusting the first aerobic granular sludge reaction equipment based on the first granular sludge treatment parameter to carry out granular sludge treatment;
the establishing of the first granular sludge treatment state space based on pilot plant research comprises:
obtaining a first parameter node state comprising the first drug administration parameter, the first water distribution parameter, the first aeration parameter, the first screening parameter and the first drainage parameter and a corresponding first parameter adjustment behavior based on the historical experimental data of the pilot plant research;
inputting the first parameter node state and the first parameter adjustment behavior into a granular sludge treatment evaluation model to obtain a first evaluation result;
obtaining a second parameter node state according to the first evaluation result, wherein the second parameter node state is the parameter node state of the first parameter node state after being adjusted by the first parameter adjustment behavior;
and constructing to obtain the first granular sludge treatment state space until an Nth evaluation result, an Nth parameter node state and an Nth parameter adjustment behavior are obtained.
2. The method of claim 1, wherein the method further comprises:
obtaining a mapping relation between the Nth parameter node state and the Nth-1 parameter adjustment behavior;
and constructing the first granular sludge treatment state space according to the mapping relation.
3. The method of claim 1, wherein the obtaining of the first parameter node status comprising the first dosing parameter, the first water distribution parameter, the first aeration parameter, the first screening parameter, and the first drainage parameter, and the corresponding first parameter adjustment behavior based on historical experimental data of the pilot study comprises:
acquiring first parameter historical data of the first drug administration parameter, the first water distribution parameter, the first aeration parameter, the first screening parameter and the first drainage parameter based on historical experimental data of pilot plant research;
acquiring processing node state historical data of the first aerobic granular sludge reaction equipment based on historical experimental data of pilot plant research, wherein processing node states in the processing node state historical data correspond to parameters in the first parameter historical data one to one;
and obtaining the first parameter node state and the first parameter adjustment behavior according to the first parameter historical data and the processing node state historical data.
4. The method of claim 3, wherein the obtaining of the first parameter historical data of the first dosing parameter, the first water distribution parameter, the first aeration parameter, the first screening parameter and the first drainage parameter based on the historical experimental data of the pilot plant study comprises:
acquiring parameter historical data of the first drug administration parameter, the first water distribution parameter, the first aeration parameter, the first screening parameter and the first drainage parameter based on historical experimental data of pilot plant research;
and performing principal component analysis linear dimensionality reduction on the parameter historical data to obtain the first parameter historical data.
5. The method of claim 4, wherein performing principal component analysis linear dimensionality reduction on the parameter historical data to obtain the first parameter historical data comprises:
performing decentralized processing on the parameter historical data to obtain decentralized parameter historical data;
obtaining a first covariance matrix of the decentralized parameter historical data;
calculating the first covariance matrix to obtain a first eigenvalue and a first eigenvector of the first covariance matrix;
and projecting the parameter historical data to the first feature vector to obtain the first parameter historical data.
6. The method of claim 1, wherein said inputting the first state parameter into a granular sludge treatment adjustment model to obtain a first granular sludge treatment parameter comprises:
inputting the first state parameter into the granular sludge treatment adjustment model;
the granular sludge treatment adjustment model is obtained by training a plurality of groups of training data to a preset state or convergence, wherein each group of training data in the plurality of groups of training data comprises: a first status parameter and identification information for identifying the first granular sludge treatment parameter;
and obtaining an output result of the granular sludge treatment adjustment model, wherein the output result comprises the first granular sludge treatment parameter.
7. A pilot study-based control system for granular sludge treatment, wherein the system comprises:
a first obtaining unit for obtaining first apparatus information of a first aerobic granular sludge reaction apparatus;
a second obtaining unit, configured to obtain a drug administration parameter set, a water distribution parameter set, an aeration parameter set, a screening parameter set, and a drainage parameter set according to the first device information;
a third obtaining unit, configured to obtain a first dosing parameter, a first water distribution parameter, a first aeration parameter, a first screening parameter, and a first drainage parameter according to the dosing parameter set, the water distribution parameter set, the aeration parameter set, the screening parameter set, and the drainage parameter set;
a first construction unit for constructing a first granular sludge treatment status space based on pilot plant studies;
the first treatment unit is used for inputting the first dosing parameter, the first water distribution parameter, the first aeration parameter, the first screening parameter and the first drainage parameter into the first granular sludge treatment state space to obtain a first state parameter;
the second processing unit is used for inputting the first state parameter into a granular sludge treatment adjustment model to obtain a first granular sludge treatment parameter;
a first judging unit for judging whether the first granular sludge treatment parameter meets a preset treatment threshold value;
the first management unit is used for adjusting the first aerobic granular sludge reaction equipment based on the first granular sludge treatment parameter to perform granular sludge treatment if the first aerobic granular sludge treatment parameter is met;
a fourth obtaining unit, configured to obtain, based on historical experimental data of a pilot plant study, a first parameter node state including the first dosing parameter, the first water distribution parameter, the first aeration parameter, the first screening parameter, and the first drainage parameter, and a corresponding first parameter adjustment behavior;
a third processing unit, configured to input the first parameter node state and the first parameter adjustment behavior into a granular sludge treatment evaluation model to obtain a first evaluation result;
a fourth processing unit, configured to obtain a second parameter node state according to the first evaluation result, where the second parameter node state is a parameter node state obtained after the first parameter node state is adjusted by the first parameter adjustment behavior;
and the second construction unit is used for constructing and obtaining the first granular sludge treatment state space until an Nth evaluation result, an Nth parameter node state and an Nth parameter adjustment behavior are obtained.
8. A pilot-scale study-based control system for granular sludge treatment, comprising: a processor coupled to a memory for storing a program that, when executed by the processor, causes a system to perform the steps of the method of any of claims 1 to 6.
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Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1998003434A1 (en) * 1996-07-19 1998-01-29 Mitsubishi Chemical Corporation Device for controlling dissolved oxygen concentration of aeration tank, device for controlling temperature of aeration tank, device for controlling flow rate of raw water for homogeneous-flow liquid surface, and wastewater treatment equipment used in activated sludge process
CN109508811A (en) * 2018-09-30 2019-03-22 中冶华天工程技术有限公司 Parameter prediction method is discharged based on principal component analysis and the sewage treatment of shot and long term memory network
CN109857056A (en) * 2019-03-08 2019-06-07 中冶华天南京电气工程技术有限公司 A kind of Intelligent sewage processing control system
CN109879475A (en) * 2019-02-28 2019-06-14 重庆工商大学 Dynamic adjustment type sewage operating condition processing method
CN109976187A (en) * 2019-02-28 2019-07-05 重庆工商大学 The sewage management platform for being optimized based on biochemical wastewater treatment and being finely aerated
CN110378533A (en) * 2019-07-22 2019-10-25 中展环能(北京)技术有限公司 A kind of intelligence aeration management method based on big data analysis
CN110824923A (en) * 2019-11-25 2020-02-21 浙江嘉科电子有限公司 Sewage treatment control method and system based on deep learning and cloud computing
EP3699148A1 (en) * 2019-02-19 2020-08-26 Siemens Aktiengesellschaft Automated control of the oxygen concentration of the activated sludge tank of a wastewater treatment plant
CN111655633A (en) * 2018-02-02 2020-09-11 凯米拉公司 Method and system for providing at least one input parameter for a sludge dewatering process of a wastewater treatment plant
CN113087288A (en) * 2021-03-22 2021-07-09 上海工程技术大学 Water treatment control system and method for energy center of manufacturing enterprise

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1998003434A1 (en) * 1996-07-19 1998-01-29 Mitsubishi Chemical Corporation Device for controlling dissolved oxygen concentration of aeration tank, device for controlling temperature of aeration tank, device for controlling flow rate of raw water for homogeneous-flow liquid surface, and wastewater treatment equipment used in activated sludge process
CN111655633A (en) * 2018-02-02 2020-09-11 凯米拉公司 Method and system for providing at least one input parameter for a sludge dewatering process of a wastewater treatment plant
CN109508811A (en) * 2018-09-30 2019-03-22 中冶华天工程技术有限公司 Parameter prediction method is discharged based on principal component analysis and the sewage treatment of shot and long term memory network
EP3699148A1 (en) * 2019-02-19 2020-08-26 Siemens Aktiengesellschaft Automated control of the oxygen concentration of the activated sludge tank of a wastewater treatment plant
CN109879475A (en) * 2019-02-28 2019-06-14 重庆工商大学 Dynamic adjustment type sewage operating condition processing method
CN109976187A (en) * 2019-02-28 2019-07-05 重庆工商大学 The sewage management platform for being optimized based on biochemical wastewater treatment and being finely aerated
CN109857056A (en) * 2019-03-08 2019-06-07 中冶华天南京电气工程技术有限公司 A kind of Intelligent sewage processing control system
CN110378533A (en) * 2019-07-22 2019-10-25 中展环能(北京)技术有限公司 A kind of intelligence aeration management method based on big data analysis
CN110824923A (en) * 2019-11-25 2020-02-21 浙江嘉科电子有限公司 Sewage treatment control method and system based on deep learning and cloud computing
CN113087288A (en) * 2021-03-22 2021-07-09 上海工程技术大学 Water treatment control system and method for energy center of manufacturing enterprise

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