CN109857056A - A kind of Intelligent sewage processing control system - Google Patents
A kind of Intelligent sewage processing control system Download PDFInfo
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- CN109857056A CN109857056A CN201910178028.7A CN201910178028A CN109857056A CN 109857056 A CN109857056 A CN 109857056A CN 201910178028 A CN201910178028 A CN 201910178028A CN 109857056 A CN109857056 A CN 109857056A
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02W—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO WASTEWATER TREATMENT OR WASTE MANAGEMENT
- Y02W10/00—Technologies for wastewater treatment
- Y02W10/10—Biological treatment of water, waste water, or sewage
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Abstract
The invention discloses a kind of Intelligent sewages to handle control system, including sewage automation collection module for acquiring equipment, the operation data of instrument and status information, and controls equipment operation；Sewage control adjustment module is used for accurate aeration；Sewage forecast analysis module feeds back to control and regulation module for predicting dissolved oxygen numerical value；It produces visualization model mutually to call, run in such a way that hybrid programming, modularization can load, the information such as intuitive various parameters, alarm, model shown in sewage disposal process；Again by with true sewage plant Experimental Comparison, obtain in the case that water outlet preferably aeration tank enter water index and dissolved oxygen value at this time as sample, the supporting vector machine model of particle swarm algorithm optimization is used according to sample training, finally realize the accurate prediction of dissolved oxygen under the conditions of difference enters water, pass through hybrid programming method again, adjusting the result of prediction quickly send oxygen amount to realize accurate aeration, and then optimizes effluent quality.
Description
Technical field
The present invention relates to technical field of sewage, specially a kind of Intelligent sewage handles control system.
Background technique
Sewage treatment is constantly in unsteady state, and influent quality and Inlet and outlet water water moment are among variation, cause
Other parameters and effluent characteristics constantly change, and purification mechanism is mainly the microorganism that adheres in the sludge of aeration tank in oxygen appropriate
Under the conditions of gas, by metabolic decomposing pollutant, the purification of sewage is realized.Therefore the control of dissolved oxygen is the most key.
Wherein, aeration excessively not only causes waste of energy, increase operation cost, and dissolved oxygen to be brought to by interior reflux scarce
Oxygen area influences denitrifying effect, and dissolved oxygen, which is brought to anaerobic zone by exteenal reflux, influences the process of anaerobic phosphorus release.Aeration quantity is not
Foot, influences microbial activity in aeration tank, influences effluent quality.It may also result in and denitrification occurs in secondary settling tank, Mud up-floating,
Influence water outlet.
Sewage disposal system is a large time delay, non-linear, close coupling industrial stokehold, due to discharge, at
Divide uncertainty, is still a difficulty for sewage treatment intelligent control, on-line prediction and control dissolved oxygen, realization accurate aeration
Point.
Dissolved oxygen accurately can not be predicted and be controlled online in country's sewage disposal process at present, the precision and standard of prediction model
Exactness is to be improved；Secondly, mostly in domestic sewage disposal process is to reach effluent quality using excess aeration, increase run at
This；In addition, domestic sewage disposal process accurate aeration rests on the experimental stage, practical application effect is to be improved；And it is domestic dirty
Sewage control manufacturer in water treatment procedure, artificial intelligence manufacturer, mathematical model manufacturer do not form the Intelligent sewage of complete set
Control method；The real time monitoring to each parameter of equipment may be implemented in the Intelligent sewage processing control system of a set of maturation, and passes through
The algorithm of intelligence adjusts processing strategie according to different sewage qualities, adjusts the parameter setting in committed step.
Summary of the invention
The purpose of the present invention is to provide a kind of Intelligent sewages to handle control system, by true sewage plant Experimental Comparison,
Obtain in the case that water outlet preferably aeration tank enter water index and dissolved oxygen value at this time as sample, according to sample training
The supporting vector machine model optimized using particle swarm algorithm finally realizes the accurate pre- of dissolved oxygen under the conditions of difference enters water
It surveys, then by hybrid programming method, adjusts the result of prediction quickly and oxygen amount is sent to realize accurate aeration, and then optimize water outlet water
Matter, to solve the problems mentioned in the above background technology.
To achieve the above object, the invention provides the following technical scheme:
A kind of Intelligent sewage processing control system, including automation collection module, control and regulation module, forecast analysis module
With production visualization model, automation collection module and control to adjust module foundation be bi-directionally connected, control and regulation module respectively with
Forecast analysis module, production visualization model foundation are bi-directionally connected, and are established between forecast analysis module and production visualization model
It is bi-directionally connected；The production visualization model that the system integration and hybrid programming are write under VS platform is to writing under Matlab platform
The operation result of forecast analysis module is called, then reaches the control and regulation module write under Step7 platform, by host computer into
Row display；
The automation collection module includes PLC, digital quantity input, output module, analog input, output module, PLC
Programmed environment uses SETP7 development platform, and it includes tissue block OB, functional block FC, FB, system functional block SFC, SFB, memory blocks
DB；
The closed-loop control that module realization is controlled to adjust to dissolved oxygen comprising the setting value of dissolved oxygen, real value, control
Algorithm processed, and PID control theory is used, it is made of ratio control, integration control, differential control；
The forecast analysis module is become by the dissolved oxygen optimal value that intelligent algorithm predicts aeration tank needs as input
Amount, feeds back to further according to predicted value and controls to adjust module output definite value adjustment air blower realization accurate aeration, and intelligent algorithm is logical
The dissolved oxygen hard measurement prediction modeler model for crossing particle swarm algorithm POS optimization support vector regression SVR is realized；
The production visualization model includes that data show, the alarm of data modeling, DATA REASONING, data, data storage, use
Family management, real-time parameter display interface establishes the communication with PLC by Modbus TCP industrial control protocols, with C# language
SendData.AddRange sentence directly reads the DB block of PLC.
Further, the automation collection module is using the S7-300PLC of Siemens Company as core cell.
Further, the module that controls to adjust uses the PID controller FB41 integrated in STEP7 development platform, and
PID control program is stored in timing cycle to interrupt in OB35, the time interval that setting executes OB35 is 100ms, is opened in system
FB41 is called when dynamic by OB35, and is functional module background data block DB20.
Further, particle swarm algorithm POS optimizes the dissolved oxygen hard measurement prediction modeling step of support vector regression SVR
It is rapid as follows:
Step 1: preliminary analogue data is monitored and standardizes, and forms training set and test set；
Step 2: all parameters of initialization SVR model and particle swarm algorithm, initial parameter c1 and c2 including population,
Initial velocity matrix V and each primary personal best particle PiWith global optimization position gi, constructed with training sample set
SVR regression estimates model；
Step 3: Selection of FunctionAs the fitness function of population, particle is calculated
The fitness numerical value of each particle in group；
Step 4: particle personal best particle P is adjusted according to the fitness function value of gained particleiWith global optimum position
gi；
Step 5: the state of particle being updated, the optimal parameter combination of SVR is obtained；
Step 6: the parameter acquired is substituted into dissolved oxygen equationObtain the regression estimates function of SVR
Expression formula predicts the following a certain moment dissolved oxygen concentration.
Further, the production visualization model uses Microsoft VS2012 general-purpose platform software, and database is using micro-
Soft SQL2012 common software, database design is including data display unit, data alarm portion and divides user management part.
Compared with prior art, the beneficial effects of the present invention are:
This Intelligent sewage handles control system, is used to acquire the operation of equipment, instrument by sewage automation collection module
Data and status information, and control equipment operation；Sewage control adjustment module is used for accurate aeration；Sewage forecast analysis module is used
In prediction dissolved oxygen numerical value, and control and regulation module is fed back to, production visualization model can be loaded using hybrid programming, modularization
Mode mutually call, run, the information such as intuitive various parameters, alarm, model shown in sewage disposal process；Pass through again
With true sewage plant Experimental Comparison, obtain in the case that water outlet preferably aeration tank enter water index and dissolved oxygen value at this time
As sample, the supporting vector machine model of particle swarm algorithm optimization is used according to sample training, finally realizes and enters water bar in difference
The accurate prediction of dissolved oxygen under part, then by hybrid programming method, adjust the result of prediction quickly and oxygen amount is given to realize accurately
Aeration, and then optimize effluent quality.
Detailed description of the invention
Fig. 1 is system block diagram of the invention；
Fig. 2 is automation collection modular system figure of the invention；
Fig. 3 is control and regulation module flow diagram of the invention；
Fig. 4 is POS-SVR optimized flow chart of the invention；
Fig. 5 is that POS-SVR of the invention models block diagram；
Fig. 6 is dissolved oxygen concentration predicted value and true value comparison diagram of the invention；
Fig. 7 is production visualization model and hybrid programming block diagram of the invention.
In figure: 1 automation collection module, 2 control to adjust module, 3 forecast analysis modules, 4 production visualization models.
Specific embodiment
Following will be combined with the drawings in the embodiments of the present invention, and technical solution in the embodiment of the present invention carries out clear, complete
Site preparation description, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, it is obtained by those of ordinary skill in the art without making creative efforts every other
Embodiment shall fall within the protection scope of the present invention.
Referring to Fig. 1, in the embodiment of the present invention: a kind of Intelligent sewage processing control system, including automation collection module
1, module 2, forecast analysis module 3 and production visualization model 4, automation collection module 1 and control and regulation module 2 are controlled to adjust
Foundation is bi-directionally connected, and is controlled to adjust module 2 and is bi-directionally connected respectively with forecast analysis module 3, the production foundation of visualization model 4, in advance
It surveys between analysis module 3 and production visualization model 4 to establish and be bi-directionally connected；The system integration and hybrid programming are write under VS platform
Production visualization model 4 operation result for the forecast analysis module 3 write under Matlab platform is called, then reach
The control and regulation module 2 write under Step7 platform, is shown by host computer, is reached intelligent control, is precisely aerated, stablizes
Water water quality, energy-saving target.
Referring to Fig. 2, in the above-described embodiments, automation collection module 1 uses the S7-300PLC conduct of Siemens Company
Core cell, while digital quantity input, output module are extended, analog input, output module are capable of providing the state of equipment
Monitoring and control, instrument signal data acquisition function, programmed environment use SETP7 development platform, include tissue block (OB), function
It can block (FC, FB), system functional block (SFC, SFB), memory block (DB)；
Referring to Fig. 3, in the above-described embodiments, controlling to adjust module 2 mainly to the closed-loop control of dissolved oxygen, with PID
Control theory is made of ratio control, integration control, differential control, and formula is as follows:
Input: control deviation e (t)=r (t)-y (t) formula one；
Output: the ratio P of deviation, the linear combination for integrating I, differential D
kcIt is proportionality coefficient, T1 is integration time constant, and TD is derivative time constant, and PID control of the invention adjusts mould
PID control program is stored in timing cycle and is interrupted by block 2 using the PID controller FB41 integrated in STEP7 development platform
In OB35, the time interval that setting executes OB35 is 100ms, FB41 is called by OB35 in system starting, and be the function
It can module creation block of background data DB20；
First by influent COD, suspended solid SS, total nitrogen content TN, total phosphorus content TP, aeration tank flow of inlet water F, PH
Value, aeration tank temperature P, aerobic tank hydraulic detention time T (being manually entered) by acquisition sensor or are manually entered and are put into PLC's
It is stored in DB block, forecast analysis module 3 measures accurate dissolved oxygen DO value by intelligent algorithm, reaches DB block by communication network
In, storage address DB2.DBD196, the DO value in actually measured current aeration tank is also stored in DB block, and storage address is
DB2.DBD16 allows DB2.DBD196 to be connected with the SP_INT of FB41, and DB2.DB16 is allowed to be connected with the PV_IN of FB41.
Referring to Fig. 4, in the above-described embodiments, forecast analysis module 3 mainly predicts aeration tank by intelligent algorithm and needs
Then the dissolved oxygen optimal value wanted feeds back to according to predicted value and controls to adjust the adjustment air blower realization accurate aeration of module 2, proposes
The dissolved oxygen hard measurement that particle swarm algorithm (POS) optimizes support vector regression (SVR) predicts modeler model；Particle swarm algorithm
Conceptual foundation is exactly particle, and the corresponding solution in solution space is corresponded with particle.Population be in migration dynamic process and
In certain directionality, in order to reach higher search rate, feedback principle and parallel computation are combined in search process；
It determines influent COD by analyzing sewage treatment mechanism characteristic according to process mechanism and Heuristics, hang
Buoyant solid SS, total nitrogen content TN, total phosphorus content TP, aeration tank flow of inlet water F, pH value, aeration tank temperature P, aerobic tank waterpower
7 input variables of residence time T (being manually entered), model output is dissolved oxygen concentration DO, takes 100 groups of data samples altogether,
60 groups of data are chosen immediately as training data, and remaining 40 groups of data are as extensive data.
Referring to Fig. 5, the dissolved oxygen hard measurement prediction of particle swarm algorithm (POS) optimization support vector regression (SVR) is built
Steps are as follows for mould:
Step 1: preliminary analogue data is monitored and standardizes, and forms training set and test set；
Step 2: initialization SVR model and particle swarm algorithm so parameter, initial parameter c1 including population and
C2, initial velocity matrix V and each primary personal best particle PiWith global optimization position gi, with training sample set structure
Build SVR regression estimates model；
Step 3: Selection of FunctionAs the fitness function of population, grain is calculated
The fitness numerical value of each particle in subgroup；
Step 4: particle personal best particle P is adjusted according to the fitness function value of gained particleiWith global optimum position
gi；
Step 5: being updated the state of particle, to obtain the optimal parameter combination of SVR；
Step 6: the parameter acquired is substituted into dissolved oxygen equationObtain the regression estimates letter of SVR
Number expression formula predicts the following a certain moment dissolved oxygen concentration.
Referring to Fig. 6, being based on above-mentioned modeler model, prediction model simulation result and true value are compared, and initialize each grain
The range of son initializes population number m=100 all between [- 1,1], and it is 10 that minimum training, which is arranged, and stops error-4, maximum
Number of iterations 1000, simulation result is as shown in Figure 6, it can be seen that, prediction effect is very good, and error is also smaller, meets application demand.
Referring to Fig. 7, in the above-described embodiments, production visualization model 4 uses Microsoft VS2012 general-purpose platform software, number
Microsoft SQL2012 common software is used according to library, database design is divided into three parts, and first is data display unit, this part
Main storage is the real-time display data being periodically inserted into；Second is alert data, the relevant information including various alarms；Third
Certain customers part mainly stores user information, adds redundant field between each table, realizes the Connection inquiring of each table；
Wherein, real-time parameter display interface establishes the communication with PLC by Modbus TCP industrial control protocols, with C#
Language sendData.AddRange sentence directly reads the DB block of PLC, and analog data directly reads that be shown in host computer real-time
On parameter interface, switching value needs a conversion, such as the run signal of elevator pump, 0 it is corresponding stop, 1 it is corresponding open, when display
It is converted into chinese disply.
In summary: a kind of Intelligent sewage processing control system provided by the invention, including sewage automation collection module 1
For acquiring the operation data and status information of equipment, instrument, and control equipment operation；Sewage control adjustment module 2 is for essence
Really aeration；Sewage forecast analysis module 3 feeds back to for predicting dissolved oxygen numerical value and controls to adjust module 2, reaches intelligent control
System, cost efficiency, the target for improving effluent quality produce visualization model 4 in such a way that hybrid programming, modularization can load
It mutually calls, operation, the information such as intuitive various parameters, alarm, model shown in sewage disposal process.
The foregoing is only a preferred embodiment of the present invention, but scope of protection of the present invention is not limited thereto,
Anyone skilled in the art within the technical scope of the present disclosure, according to the technique and scheme of the present invention and its
Inventive concept is subject to equivalent substitution or change, should be covered by the protection scope of the present invention.
Claims (5)
1. a kind of Intelligent sewage handles control system, which is characterized in that including automation collection module (1), control to adjust module
(2), forecast analysis module (3) and production visualization model (4), automation collection module (1) and control and regulation module (2) are established
It is bi-directionally connected, controls to adjust module (2) and establish and be bi-directionally connected with forecast analysis module (3), production visualization model (4) respectively,
It establishes and is bi-directionally connected between forecast analysis module (3) and production visualization model (4)；The system integration and hybrid programming are in VS platform
Under the production visualization model (4) write the operation result for the forecast analysis module (3) write under Matlab platform is adjusted
With, then the control and regulation module (2) write under Step7 platform is reached, it is shown by host computer；
The automation collection module (1) includes PLC, digital quantity input, output module, analog input, output module, PLC
Programmed environment uses SETP7 development platform, and it includes tissue block OB, functional block FC, FB, system functional block SFC, SFB, memory blocks
DB；
The closed-loop control that module (2) realization is controlled to adjust to dissolved oxygen comprising the setting value of dissolved oxygen, real value, control
Algorithm processed, and PID control theory is used, it is made of ratio control, integration control, differential control；
The forecast analysis module (3) is become by the dissolved oxygen optimal value that intelligent algorithm predicts aeration tank needs as input
Amount feeds back to further according to predicted value and controls to adjust module (2) output definite value adjustment air blower realization accurate aeration, intelligent algorithm
Predict that modeler model is realized by the dissolved oxygen hard measurement that particle swarm algorithm POS optimizes support vector regression SVR；
The production visualization model (4) includes that data show, the alarm of data modeling, DATA REASONING, data, data storage, use
Family management, real-time parameter display interface establishes the communication with PLC by Modbus TCP industrial control protocols, with C# language
SendData.AddRange sentence directly reads the DB block of PLC.
2. a kind of Intelligent sewage as described in claim 1 handles control system, it is characterised in that: the automation collection module
(1) using the S7-300PLC of Siemens Company as core cell.
3. a kind of Intelligent sewage as described in claim 1 handles control system, it is characterised in that: the control and regulation module
(2) using the PID controller FB41 integrated in STEP7 development platform, and PID control program is stored in timing cycle and is interrupted
In OB35, the time interval that setting executes OB35 is 100ms, FB41 is called by OB35 in system starting, and be the function
It can module creation block of background data DB20.
4. a kind of Intelligent sewage as described in claim 1 handles control system, it is characterised in that: particle swarm algorithm POS optimization
The dissolved oxygen hard measurement prediction modeling procedure of support vector regression SVR is as follows:
Step 1: preliminary analogue data is monitored and standardizes, and forms training set and test set；
Step 2: all parameters of initialization SVR model and particle swarm algorithm, initial parameter c1 and c2 including population, initially
Rate matrices V and each primary personal best particle PiWith global optimization position gi, returned with training sample set building SVR
Return estimation model；
Step 3: Selection of FunctionAs the fitness function of population, calculate in population
The fitness numerical value of each particle；
Step 4: particle personal best particle P is adjusted according to the fitness function value of gained particleiWith global optimum position gi；
Step 5: the state of particle being updated, the optimal parameter combination of SVR is obtained；
Step 6: the parameter acquired is substituted into dissolved oxygen equationObtain the regression estimates function representation of SVR
Formula predicts the following a certain moment dissolved oxygen concentration.
5. a kind of Intelligent sewage as described in claim 1 handles control system, which is characterized in that the production visualization model
(4) Microsoft VS2012 general-purpose platform software is used, database uses Microsoft SQL2012 common software, and database design includes number
According to display portion, data alarm portion and divide user management part.
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