CN201749343U - Sewage treatment control system - Google Patents

Sewage treatment control system Download PDF

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Publication number
CN201749343U
CN201749343U CN2010201453932U CN201020145393U CN201749343U CN 201749343 U CN201749343 U CN 201749343U CN 2010201453932 U CN2010201453932 U CN 2010201453932U CN 201020145393 U CN201020145393 U CN 201020145393U CN 201749343 U CN201749343 U CN 201749343U
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station
computing machine
sewage treatment
control system
data
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程朋胜
杨永军
钟宇彤
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Shenzhen Das Intellitech Co Ltd
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Shenzhen Das Intellitech Co Ltd
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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
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02W—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO WASTEWATER TREATMENT OR WASTE MANAGEMENT
    • Y02W10/00—Technologies for wastewater treatment
    • Y02W10/10—Biological treatment of water, waste water, or sewage

Abstract

The utility model relates to a sewage treatment control system. The sewage treatment control system comprises an operating station computer, a data analysis station computer, a programmable controller (PLC) master station and a remote PLC station which are respectively connected through an industrial Ethernet, wherein the operating station computer is used for gathering the data of water quality index of water in a sewage regulating pond and transmitting the data to the data analysis station computer; the data analysis station computer performs measurement to dissolved oxygen (DO) for the index data according to the genetic neural network (GABP) and outputs the measured DO to the programmable controller (PLC) master station and the remote PLC station; and the programmable controller (PLC) master station and the remote PLC station are respectively used for optimizing the PID parameter according to the genetic algorithm and controlling the measured DO according to the optimized PID parameter. The provided sewage treatment control system has the advantages that the proper control parameter corresponding to the variable parameter model can be rapidly found, the sewage treatment control system can be rapidly and stably controlled with high accuracy, and the energy consumption can be reduced.

Description

Sewage treatment control system
Technical field
The utility model relates to sewage treatment area, relates in particular to sewage treatment control system.
Background technology
Along with the growth of national economy and the enhancing of public's environmental consciousness, the wastewater treatment automatic technology has welcome unprecedented opportunity to develop.Propose to study and to promote wastewater treatment new technology efficient, low energy consumption in the medium-term and long-term science and technology development planning of country.Therefore, achievement in research of the present utility model has broad application prospects.Dissolved oxygen DO (DO) concentration is the running controlled variable that is most widely used in the present wastewater treatment, when dissolved oxygen DO is not enough or excessive, all can cause the mud living environment to worsen: when oxygen is not enough, on the one hand because der Pilz breeding in a large number in the Aerobic Pond, final sludge bulking, the generation unusual service condition of producing; On the other hand because thereby the growth rate of aerobic bacteria reduces the decline that causes effluent quality.Oxygen excessive (being excessive aeration) then can cause suspended solid settling property variation, influences the normal operation of sewage disposal system.The Control of Dissolved Oxygen relates to the energy consumption of microbial growth environment and processing procedure, and therefore, dissolved oxygen DO control is the emphasis of research always.
Good control can be saved the wastewater treatment operating cost, also is simultaneously the key that reduces and tackle unusual service condition generation, the normal operation of guarantee sewage disposal process.In addition,, can also reduce operational management and operating personnel effectively, reduce operating cost by improving the sewage disposal process automatization level.Present effluent control system uses PID control more, and the subject matter of PID control is exactly the parameter tuning problem.At present adjusting of pid control parameter mostly is to rely on staff's engineering experience, empirical method such as adopt that examination is gathered, and it is long that such parameter tuning method exists spended time, causes the accident easily, and it is of low quality to adjust, and influences problems such as production during adjusting.And for the sewage disposal system model, owing to itself be exactly a variable-parameter system, along with the difference of the influent quality water yield, the concrete model of system changes.For such system, the problem of tuning of pid control parameter is just more outstanding, makes can't control accurately and can't cut down the consumption of energy.
The utility model content
The purpose of this utility model is to provide a kind of sewage treatment control system, and solve in the prior art and can't find the suitable controlled variable corresponding apace with variable parameter model, and problem that can't the high-precision control of fast and stable.
The utility model provides a kind of sewage treatment control system, comprise the active station computing machine, data analysis station computing machine, Programmable Logic Controller PLC main website and the remote PLC station that connect by Industrial Ethernet, active station computer acquisition sewage regulating reservoir influent quality achievement data wherein, and send to the data analysis station computing machine; The data analysis station computing machine carries out dissolved oxygen DO DO according to genetic neural network GABP to achievement data to be measured, and the DO after the output measurement is to Programmable Logic Controller PLC main website and remote PLC station; Programmable Logic Controller PLC main website and remote PLC station are carried out optimizing according to genetic algorithm to pid parameter, then according to the DO after the pid parameter control survey after the optimizing.
Preferably, above-mentioned data analysis station computing machine is connected with engineer station's computing machine and active station computing machine by first hub, and data are carried out pre-service, determine the structure of auxiliary variable and genetic neural network GABP, based on genetic neural network GABP achievement data is carried out dissolved oxygen DO DO and measure.
Preferably, above-mentioned data analysis station computing machine is connected with engineer station's computing machine and active station computing machine by first hub, and the rejecting abnormalities data, and the data of remainder are carried out data normalization.
Preferably, above-mentioned data analysis station computing machine is connected with engineer station's computing machine and active station computing machine by first hub, and the sample set data are carried out normalized with the zero-mean standardized method; Neural network weight and threshold value are unified, and neural network weight and threshold value are encoded; Given input and output sample set is divided into training sample and test sample book, many group networks weights is trained respectively, try to achieve each network output with genetic neural network GABP algorithm; Setting fitness function is the square error performance function; Setting fitness function is that the square error performance function is operated the individuality in the mating pond according to intersection, mutation genetic operator, obtains population of new generation.
Preferably, above-mentioned Programmable Logic Controller PLC main website and remote PLC station interconnect by second hub, and definite encoding scheme, determine fitness function, and the parameter of definite genetic algorithm.
The utility model sewage treatment control system, with genetic algorithm pid parameter is carried out optimizing, can find the suitable controlled variable corresponding apace, make sewage treatment control system can access the high-precision control of fast and stable with variable parameter model, and capable of reducing energy consumption.
Description of drawings
Fig. 1 is the structural representation of the utility model sewage treatment control system;
Fig. 2 is the process flow diagram of the utility model sewage treatment control method;
Fig. 3 is auxiliary variable and genetic neural network;
Fig. 4 is a GABP neural network weight encoding scheme;
Fig. 5 is the structural drawing of PID control system;
Fig. 6 is the adaptive value curve of GABP algorithm genetic iteration;
Fig. 7 is the training error performance curve of 0.001 o'clock adaptive learning speed momentum GABP algorithm for Target Setting;
Fig. 8 is the DO prediction case of GABP neural network algorithm.
Embodiment
Below in conjunction with the drawings and specific embodiments technical solutions of the utility model are described in further detail, so that those skilled in the art can better understand the utility model and being implemented, but illustrated embodiment is not as to qualification of the present utility model.
Fig. 1 is the structural representation of the utility model sewage treatment control system.
Unmanned is carried out in the operational management of the utility model sewage treatment control system, wherein this sewage treatment control system comprises engineer station's computing machine 101, active station computing machine 102, data analysis station computing machine 103, Programmable Logic Controller PLC main website 104, PLC main website 105, PLC main website 106, remote PLC station 107, remote PLC station 108, remote PLC station 109 are formed.Engineer station's computing machine 101, active station computing machine 102, data analysis station computing machine 103 also can be described as host computer.Certainly in another embodiment, the utility model sewage treatment control system also is provided with field instrument, and engineer station's computing machine 101, active station computing machine 102 and data analysis station computing machine 103 can be integrated into same computing machine.
In this example, connect by Industrial Ethernet between engineer station's computing machine 101, active station computing machine 102, data analysis station computing machine 103, Programmable Logic Controller PLC main website 104, PLC main website 105, PLC main website 106, remote PLC station 107, remote PLC station 108 and the remote PLC station 109.
Host computer adopts WinCC 6.2 softwares of SIEMENS, WinCC 6.2 softwares provide open, extendible man-machine interface, developing application flexibly, finishing analyzing and processing, storage, demonstration, warning, and the data of entire sewage process control system are carried out Long-distance Control to field data.
SIMATIC S7300 family computer is adopted at Programmable Logic Controller PLC main website and remote PLC station, the characteristics of this family computer are to be easy to configuration and maintenance, and provide flexible selection to structure and module, has integrated electronics control (Integrated Electronic Control, IEC) programming and open network structure, finish various on-site signals by main website and remote I/O station, as liquid level, device start, stop, data acquisitions such as fault alarm, the reception of conversion and host computer control command is transmitted, thereby realizes the logic of process equipment is controlled automatically.Field instrument is realized the collection of technological process signal and is uploaded.
After engineer station's computing machine 101 and active station computing machine 102 couples of Programmable Logic Controller PLC main website and remote PLC station data are gathered, send to data analysis station computing machine 103 by Industrial Ethernet.Be specially, active station computing machine 102 is used to gather sewage regulating reservoir influent quality achievement data, and sends to data analysis station computing machine 103.Data analysis station computing machine 103 is used for based on genetic neural network GABP achievement data being carried out dissolved oxygen DO DO to be measured, and the DO after the output measurement is to Programmable Logic Controller PLC main website and remote PLC station.Programmable Logic Controller PLC main website and remote PLC station are carried out optimizing based on genetic algorithm to pid parameter, then according to the DO after the pid parameter control survey after the optimizing.
Further, above-mentioned data analysis station computing machine 103 is connected with engineer station's computing machine 101 and active station computing machine 102 by first hub 110, and data are carried out pre-service, determine the structure of auxiliary variable and genetic neural network GABP, based on genetic neural network GABP achievement data is carried out dissolved oxygen DO DO and measure.
Further, above-mentioned data analysis station computing machine 103 is connected with engineer station's computing machine 101 and active station computing machine 102 by first hub 110, and the rejecting abnormalities data, and the data of remainder are carried out data normalization.
Further, above-mentioned data analysis station computing machine 103 is connected with engineer station's computing machine 101 and active station computing machine 102 by first hub 110, and the sample set data are carried out normalized with the zero-mean standardized method; Neural network weight and threshold value are unified, and neural network weight and threshold value are encoded; Given input and output sample set is divided into training sample and test sample book, many group networks weights is trained respectively, try to achieve each network output with genetic neural network GABP algorithm; Setting fitness function is the square error performance function; Setting fitness function is that the square error performance function is operated the individuality in the mating pond according to intersection, mutation genetic operator, obtains population of new generation.
Further, above-mentioned PLC main website 104, PLC main website 105, PLC main website 106 interconnect by second hub 111 with remote PLC station 107, remote PLC station 108 and remote PLC station 109, and definite encoding scheme, determine fitness function, and the parameter of definite genetic algorithm.
Fig. 2 is the process flow diagram of the utility model sewage treatment control method.
Step S201, active station computing machine 102 gather sewage regulating reservoir influent quality achievement data, and send to data analysis station computing machine 103.
Step S202, data analysis station computing machine 103 carry out dissolved oxygen DO DO based on genetic neural network GABP to achievement data and measure, and the DO after the output measurement is to Programmable Logic Controller PLC main website and remote PLC station.
Step S203, Programmable Logic Controller PLC main website and remote PLC station are carried out optimizing based on genetic algorithm to pid parameter, then according to the DO after the pid parameter control survey after the optimizing.
Further, above-mentioned steps S202 comprises:
Step S2021,103 pairs of data of data analysis station computing machine are carried out pre-service, are specially:
At first, data analysis station computing machine 103 rejecting abnormalities data, what adopt for the rejecting of abnormal data is La Yida criterion (3 σ criterion) in the assumed statistical inspection method, if sample data is x1, x2...xn, mean value are x, deviation is vi=xi-x (i=1,2 ... n), calculate standard deviation according to the Bessel formula:
S = σ = [ Σ v i 2 / ( n - 1 ) ] 1 / 2
If the deviation vi of sample data xi≤i≤n satisfies: | v i|＞3 σ
Think that then xi is an abnormal data, should reject.
Secondly, the data of 103 pairs of remainders of data analysis station computing machine are carried out data normalization.Because measured data have different engineering units in the sewage disposal process, the size of each variable is numerically widely different, directly use raw measurement data to calculate possibility drop-out and the instability that causes numerical evaluation, therefore need do standardization each parameter.Normalized is carried out in standardized method to the sample data zero-mean.So-called normalized to data is that data are carried out centralization and compression processing simultaneously, and its mathematic(al) representation is:
x ij * = x ij - x ‾ j S j , i＝1，2，…，n；j＝1，2，…，p
Through after the normalized of data, can make that the average of each variable is 0, standard deviation is 1, and then eliminates because the different different influences that bring with the order of magnitude of different characteristic factor dimension.
Step S2022, data analysis station computing machine 103 is determined the structure of auxiliary variable and genetic neural network GABP.
Determined auxiliary variable and genetic neural network GABP are as shown in Figure 3 in the present embodiment.Owing to be respectively COD, SS, DO and PH with several auxiliary variables of DO coupling and incidence relation maximum, so the neuron of the input layer of determined genetic neural network GABP is 4 in the present embodiment, is respectively COD, SS, DO and PH; The neuron of hidden layer is 8, and the neuron of output layer is one, and promptly DO sets up a MISO system model to realize the soft measurement of leading variable DO.Wherein the excitation function of hidden layer and output layer is respectively tanh Sigmoid function and linear function.
Step S2023, data analysis station computing machine 103 carry out dissolved oxygen DO DO based on genetic neural network GABP to achievement data and measure.
Because the standard BP algorithm speed of convergence is slow, is absorbed in local minimum easily, numerical stability is poor.Present embodiment is employed to be the GABP algorithm, and Fig. 4 is a GABP neural network weight encoding scheme.GABP algorithm in Fig. 4 adopts the genetic algorithm optimization neural network weight, and adopt adaptive learning speed momentum BP algorithm that neural network is trained, calculate fitness function, use initial weight and the threshold value of the weights of the optimization corresponding at last, calculate neural network output as neural network with minimum fitness function.It is as follows that data analysis station computing machine 103 carries out GABP algorithm detailed process:
Step 1,103 pairs of sample set data of data analysis station computing machine are carried out normalized with the zero-mean standardized method:
x ij * = x ij - x ‾ j S jj , i＝1，2，…，n；j＝1，2，…，p
Wherein, i is a sample number; J is the sample component; Xij is a j component of i sample; Xj is the average of j component sample.Sij is the standard deviation of variable xj.
Step 2, data analysis station computing machine 103 is unified neural network weight and threshold value, and neural network weight and threshold value are encoded, and wherein threshold value is to be input as-1 connection weight.What adopt in the present embodiment is real coding, and 40 weights and 9 threshold values are cascaded, and obtains a very long character string, and promptly a gene strand (chromosome) (Fig. 4) is represented a kind of weights combination of network, i.e. body one by one.The setting population scale is N, produces an initial population at random.
Step 3, data analysis station computing machine 103 is divided into training sample and test sample book with given input and output sample set, with genetic neural network GABP algorithm many group networks weights are trained respectively, try to achieve each network output, satisfy accuracy requirement if having one group at least through the group of the N after training weights, then algorithm finishes; Otherwise change program (4) over to.
Step 4, it is the square error performance function that data analysis station computing machine 103 is set fitness function:
perf＝mse(E，net，PP)
Wherein, E is the network output error; Net is employed network (can therefrom obtain weights and threshold vector X); PP is the error performance regulation.
It is that the square error performance function is operated the individuality in the mating pond according to intersection, mutation genetic operator that step 5, data analysis station computing machine 103 are set fitness function, obtains population of new generation.
The end condition that above-mentioned steps need reach the genetic algorithm setting just stops.
Further, above-mentioned steps S203 comprises:
Step S2031, encoding scheme is determined at Programmable Logic Controller PLC main website and remote PLC station.
Owing to adopt binary coding can inevitably relate to the balance of quantization error and code length, and can cause the distortion of objective function so the pid control parameter of dissolved oxygen concentration coding employing real coding mode during optimization spatial discretization that will be continuous.This paper determines all optimizing in [02] scope of Kp, Ki, Kd.
Step S2032, fitness function is determined at Programmable Logic Controller PLC main website and remote PLC station.
Fig. 5 is the structural drawing of PID control system, is through feedback of status quelling system model in the frame of broken lines wherein.
To the evaluation of the performance quality of various control system, generally adopt the overshoot σ % of control system and regulate time T S and estimate.In order to obtain satisfied transient process dynamic perfromance, the minimum target function that adopts absolute value time integral performance index to select as parameter; Excessive in order to prevent control energy, in objective function, add the quadratic term that control is exported.The optimum index of selecting for use following formula to select as parameter:
J = ∫ 0 ∞ ( ω 1 | e ( t ) | + ω 2 u 2 ( t ) ) dt + ω 3 t u
In following formula: e (t) is a systematic error; U (t) is controller output; Tu is the rise time; ω 1, and ω 2, and ω 3 is the weights of corresponding parameter.
For fear of overshoot, in evaluation function, added punitive function, promptly in case produce overshoot, just with overshoot as optimum index, optimum index is at this moment:
J = ∫ 0 ∞ ( ω 1 | e ( t ) | + ω 2 u 2 ( t ) + ω 4 | e ( t ) | ) dt + ω 3 t u
In the following formula: ω 4 is the weights of overshoot, and ω 4＞＞ω 1; This paper sets ω 1=ω 2=ω 3=1, ω 4=100.
Step S2033, the parameter of genetic algorithm is determined at Programmable Logic Controller PLC main website and remote PLC station.
Rule of thumb reach this paper actual conditions, set each parameter and be respectively: population scale Population size is 30, and crossover probability Crossover ratio is 0.9, and variation probability Mutation ratio is 0.1, and evolutionary generation T is 100.
Fig. 6 is the adaptive value curve of GABP algorithm genetic iteration.
Present embodiment adopts MATLAB that the GABP neural network soft sensor model is carried out emulation experiment.For the performance of genetic algorithm in the neural network weight optimal design clearly is described, 66 groups of remaining after data pre-service rejecting abnormalities data data samples are divided into two parts: wherein 46 groups of data are used as training sample, and all the other 20 groups of data are as the verification sample.As can be seen from Figure 6, the GABP algorithm promptly finds optimum weights threshold value combination through 11 genetic manipulations, and 50 iteration adaptive values afterwards all do not have to improve, and the end condition that reaches the genetic algorithm setting just stops.
Fig. 7 is the training error performance curve of 0.001 o'clock adaptive learning speed momentum GABP algorithm for Target Setting.As seen from Figure 7, the GABP algorithm only need just can arrive 0.001 level through 26633 iteration error performance computation.
Fig. 8 is the DO prediction case of GABP neural network algorithm.From the experimental result of Fig. 7 to 8 as can be seen, at crucial effluent quality in the sewage disposal process can't on-line measurement problem, the utilization soft-measuring technique comes DO is carried out simulation and forecast, the GABP Algorithm Convergence is good.The superiority that the soft-sensing model that the GABP algorithm is set up can embody global convergence has the local search ability that shows the BP algorithm again, and constringency performance will be considerably beyond simple BP algorithm.GABP convergence of algorithm speed is fast as can be seen from Figure 7 simultaneously.Genetic algorithm is when running into local extremum, and search does not stop, but constantly obtains new optimum individual (neural network weight), has overcome simple BP algorithm and has been absorbed in local minimum, speed of convergence is slow, precision is low defective easily, has realized the soft measurement to DO.
Below for using the utility model sewage treatment control system and sewage water treatment method carry out wastewater treatment in certain sewage treatment plant overview.
The engineering design scale of this sewage treatment plant is the amount of disposing of sewage 80,000 m3/d, the actual motion scale is 100,000 m3/d, presses 100% combined system in the peaking variation factor Kz=1.3, design period in the recent period, interception ratio is got N=1, and pre-service flow (flow in rainy season) is 160,000 m3/d.Influent quality is shown in table 4-1, and the design effluent quality is as shown in table 2:
Table 1
Figure GDA0000020285800000081
Table 2
Figure GDA0000020285800000082
The main production structures of this sewage treatment plant mainly comprise: coarse rack, lift pump room, fine fack, rotational flow grit chamber, modified form A2/O biochemistry pool, change power distribution station, distribution well and mud pump house, second pond, disinfection by ultraviolet light pond, air blast machine room and sludge concentration and dewatering machine room etc.Each operation main units and electromechanical equipment can be shown in Table 3:
Table 3
Figure GDA0000020285800000083
Figure GDA0000020285800000091
Can find out that according to the table statistics the main energy consumption equipment of this sewage treatment plant is: 4 promote submersible sewage pump, 4 mixed-liquor return pumps and 3 fan blowers.Continuous 1 all power consumption situations add up as shown in table 4 below when this sewage treatment plant is normally moved:
Table 4
Sequence number Day output (ten thousand m3/d) Total installation of generating capacity (kW) Power consumption (kW.h/m3) Plant factor
1 10 (reality) 1654.85 0.213 53.63％
2 10 (reality) 1654.85 0.196 49.35％
3 10 (reality) 1654.85 0.216 54.39％
4 10 (reality) 1654.85 0.221 55.64％
5 10 (reality) 1654.85 0.192 48.34％
6 10 (reality) 1654.85 0.223 56.15％
7 10 (reality) 1654.85 0.205 51.62％
8 On average 1654.85 0.209429 52.73％
Find out that from last table this sewage treatment plant is after utilizing the utility model sewage treatment control system and sewage treatment control method, its ton water power consumes about 0.2kW.h/m3, and plant factor is about 53%.At present, domestic most of sewage treatment plants ton water power consumption is between 0.204～0.364kW.h/m, and plant factor is between 50%～88%.As seen, capable of reducing energy consumption behind the utility model sewage treatment control system and the sewage treatment control method.Therefore, illustrate simultaneously that also the utility model sewage treatment control system and sewage treatment control method also can solve the problem that can't cut down the consumption of energy of the prior art simultaneously.
In a word, the utility model sewage treatment control system and sewage treatment control method, with genetic algorithm pid parameter is carried out optimizing, can find the suitable controlled variable corresponding apace with variable parameter model, make sewage treatment control system can access the high-precision control of fast and stable, and capable of reducing energy consumption.
It below only is preferred embodiment of the present utility model; be not so limit claim of the present utility model; every equivalent structure or equivalent flow process conversion that utilizes the utility model instructions and accompanying drawing content to be done; or directly or indirectly be used in other relevant technical fields, all in like manner be included in the scope of patent protection of the present utility model.

Claims (3)

1. sewage treatment control system, it is characterized in that, comprise the active station computing machine, data analysis station computing machine, Programmable Logic Controller PLC main website and the remote PLC station that connect by Industrial Ethernet, active station computer acquisition sewage regulating reservoir influent quality achievement data wherein, and send to the data analysis station computing machine; The data analysis station computing machine carries out dissolved oxygen DO DO according to genetic neural network GABP to achievement data to be measured, and the DO after the output measurement is to Programmable Logic Controller PLC main website and remote PLC station; Programmable Logic Controller PLC main website and remote PLC station are carried out optimizing according to genetic algorithm to pid parameter, then according to the DO after the pid parameter control survey after the optimizing.
2. sewage treatment control system as claimed in claim 1 is characterized in that, described data analysis station computing machine is connected with engineer station's computing machine and active station computing machine by first hub.
3. sewage treatment control system as claimed in claim 1 or 2 is characterized in that, described Programmable Logic Controller PLC main website and remote PLC station interconnect by second hub.
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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101833314A (en) * 2010-03-30 2010-09-15 深圳达实智能股份有限公司 Sewage treatment control system and sewage treatment control method
CN106886204A (en) * 2017-04-28 2017-06-23 山东国舜建设集团有限公司 Industrial 4.0 coal-fired flue-gas pollution reducing facility remote comprehensive control systems
CN108921364A (en) * 2018-09-30 2018-11-30 中冶华天工程技术有限公司 Sewage treatment plant's blast engine energy saving consumption-reducing method based on artificial intelligence
CN117092908A (en) * 2023-10-19 2023-11-21 中国标准化研究院 Intelligent control method and system for wastewater treatment equipment based on genetic algorithm

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101833314A (en) * 2010-03-30 2010-09-15 深圳达实智能股份有限公司 Sewage treatment control system and sewage treatment control method
CN101833314B (en) * 2010-03-30 2012-07-25 深圳达实智能股份有限公司 Sewage treatment control system and sewage treatment control method
CN106886204A (en) * 2017-04-28 2017-06-23 山东国舜建设集团有限公司 Industrial 4.0 coal-fired flue-gas pollution reducing facility remote comprehensive control systems
CN106886204B (en) * 2017-04-28 2019-05-17 山东国舜建设集团有限公司 Coal-fired flue-gas pollution reducing facility remote comprehensive control system
CN108921364A (en) * 2018-09-30 2018-11-30 中冶华天工程技术有限公司 Sewage treatment plant's blast engine energy saving consumption-reducing method based on artificial intelligence
CN117092908A (en) * 2023-10-19 2023-11-21 中国标准化研究院 Intelligent control method and system for wastewater treatment equipment based on genetic algorithm
CN117092908B (en) * 2023-10-19 2024-01-02 中国标准化研究院 Intelligent control method and system for wastewater treatment equipment based on genetic algorithm

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