CN114398821A - Sewage treatment analysis and control method based on data mining and intelligent algorithm - Google Patents
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- 239000010865 sewage Substances 0.000 title claims abstract description 55
- 238000000034 method Methods 0.000 title claims abstract description 22
- 238000007418 data mining Methods 0.000 title claims abstract description 15
- 238000004458 analytical method Methods 0.000 title claims abstract description 9
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 58
- 238000004891 communication Methods 0.000 claims abstract description 11
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims abstract description 6
- 230000033116 oxidation-reduction process Effects 0.000 claims abstract description 6
- 229910052760 oxygen Inorganic materials 0.000 claims abstract description 6
- 239000001301 oxygen Substances 0.000 claims abstract description 6
- 239000000126 substance Substances 0.000 claims abstract description 6
- 238000013528 artificial neural network Methods 0.000 claims description 14
- XKMRRTOUMJRJIA-UHFFFAOYSA-N ammonia nh3 Chemical compound N.N XKMRRTOUMJRJIA-UHFFFAOYSA-N 0.000 claims description 10
- OAICVXFJPJFONN-UHFFFAOYSA-N Phosphorus Chemical compound [P] OAICVXFJPJFONN-UHFFFAOYSA-N 0.000 claims description 6
- 229910052698 phosphorus Inorganic materials 0.000 claims description 6
- 239000011574 phosphorus Substances 0.000 claims description 6
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- 238000007405 data analysis Methods 0.000 claims description 2
- 230000006870 function Effects 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 238000010276 construction Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 239000003344 environmental pollutant Substances 0.000 description 2
- 238000012886 linear function Methods 0.000 description 2
- 238000011022 operating instruction Methods 0.000 description 2
- 231100000719 pollutant Toxicity 0.000 description 2
- 239000010802 sludge Substances 0.000 description 2
- 230000005856 abnormality Effects 0.000 description 1
- 238000005273 aeration Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000005842 biochemical reaction Methods 0.000 description 1
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Abstract
The invention relates to the field of sewage treatment, and provides a sewage treatment analysis and control method based on data mining and intelligent algorithm, wherein a sewage treatment terminal is provided with a meteorological data acquisition system, a water inlet COD (chemical oxygen demand) instrument, a TN (twisted nematic) instrument, a pH meter, a thermometer, a water inlet flow meter, an ORP (oxidation-reduction potential) instrument, an outlet water online monitor, a plurality of pumps, a plurality of dosing devices and a plurality of blowers, a central control system and a CPS (physical control system) micro-control unit are established, an information physical system which is trained based on a large amount of sewage treatment plant operation data establishment algorithm model is pre-stored in the central control system and the field CPS micro-control unit, various instrument parameters are used as the input of the information physical system, the output of the information physical system is a corresponding operation instruction, the pumps, the dosing devices and the blowers are driven by the operation instruction, the target working time and power are achieved, the central control system also sends an upper layer control and decision strategy to the CPS micro-control unit through a communication module, and feeding back various control parameters of the sewage treatment terminal.
Description
Technical Field
The invention relates to the technical field of sewage treatment in villages and towns and rural areas, in particular to a sewage treatment analysis and control method based on data mining and intelligent algorithms.
Background
According to different regional conditions of rural areas, the gathering degree of village population and the scale of sewage generation, a construction mode and a treatment process combining pollution control and resource utilization, engineering measures and ecological measures and concentration and dispersion are adopted according to local conditions.
Based on the characteristics of wide rural distribution, dispersed inhabitants of farmers, large fluctuation of water quantity and water quality and the like in China. Aiming at the problems of insufficient construction of rural infrastructure, imperfect sewage collecting and conveying pipe network, generally low personnel culture level, weak operation and management capability and the like, the novel technology for treating sewage in rural areas in China is developed, and the novel technology has the characteristics of low investment, low energy consumption, simplicity in operation and maintenance, high pollutant removal efficiency and the like, so that the novel technology is not only an urgent need for survival and development of enterprises, but also an urgent need for healthy development of rural sewage treatment industry and realization of beautiful rural areas. CPS micro control unit
Disclosure of Invention
The invention aims to provide a sewage treatment analysis and control method based on data mining and intelligent algorithm, wherein an information physical system which is trained based on an algorithm model established by running data of a large number of sewage treatment plants is prestored in a central control system and a field CPS Microcontroller-Unit (CPSM), various instrument parameters are used as the input of the information physical system, the output of the information physical system is a corresponding operating instruction, and a pump, a dosing device and a blower are driven by the operating instruction, so that the working target time length and power are obtained.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows: a sewage treatment analysis and control method based on data mining and intelligent algorithm, a sewage treatment terminal is provided with a meteorological data acquisition system, a water inlet COD instrument, a TN instrument, a pH meter, a thermometer, a water inlet flow meter, an ORP instrument, a water outlet online monitor, a plurality of pumps, a plurality of dosing devices and a plurality of air blowers, a central control system is established, the instruments are connected to a CPS micro control unit, the meteorological data acquisition system, the water inlet COD instrument, the TN instrument, the pH meter, the thermometer, the water inlet flow meter, the ORP instrument and the water outlet online monitor are connected with the CPS micro control unit, the CPS micro control unit is connected with the pumps, the dosing devices and the air blowers, the CPS micro control unit is connected with the central control system through a communication module and a network,
the CPS micro control unit and the central control system are pre-stored with trained neural network prediction models, parameters of various instruments are used as input of the neural network prediction models, output of the neural network prediction models is corresponding operation instructions, the operation instructions drive target working time length and power of a pump, a dosing machine, a blower and the like, the CPS micro control unit is further connected with a communication module, and data of various instruments of the sewage treatment terminal are sent to the mobile terminal through the communication module.
Preferably, the system comprises a meteorological data acquisition system, a water inlet COD (chemical oxygen demand) instrument, a TN (twisted nematic) instrument, a pH meter, a thermometer, a water inlet flowmeter, an ORP (oxidation-reduction potential) instrument and a water outlet online monitor, wherein the CPS micro control unit respectively acquires nine variable signals with independent adjustment periods and adjustment amplitudes, namely meteorological data, a water inlet COD value, a pH value of a sewage treatment terminal, a sewage treatment terminal temperature, an ORP (oxidation-reduction potential), a water outlet COD value, a water outlet ammonia nitrogen value, a water outlet TN (twisted nematic) value and a water outlet total phosphorus value.
Preferably, the neural network prediction model comprises an input layer, a hidden layer, a connection layer and an output layer, wherein the trained neural network prediction model is obtained by training a transmission function of the connection layer through synchronous historical data.
Preferably, the training content of the connection layer includes the following steps:
step S1: collecting four variable signals and fault alarm state values in a T0 time period, wherein the T0 time period comprises a plurality of unit time T, counting the four variable signals in the unit time according to a time sequence in the fault alarm state, and sending the four variable signals to the step S2;
step S2: performing regression calculation on the four variable signals in the multiple fault alarm states to obtain a linear relation of the four variable signals in unit time;
step S3: the water quality inlet COD value, the sewage treatment terminal pH value, the sewage treatment terminal temperature, the ORP, the outlet COD value, the outlet ammonia nitrogen value, the outlet TN value, the outlet total phosphorus value and the operation instructions of a pump, a dosing machine and a blower are subjected to data mining and analysis to obtain a trained nonlinear function of the connecting layer.
Preferably, the pump, the dosing machine, the blower and other equipment are driven to work under corresponding rotating speed and power through different operation commands, the operation commands are decomposed into different emergency degree grades from low to high, and the higher the emergency degree grade is, the higher the power of the corresponding pump, dosing machine and blower is.
Preferably, the judging standard of the emergency degree grade sent by the CPS micro-control unit comprises a first-level A standard, a first-level B standard and other settable discharge standards, and the target values of COD, ammonia nitrogen, TN and TP control in the first-level A standard are smaller than the target values of COD, ammonia nitrogen, TN and TP control in the first-level B standard.
In conclusion, the beneficial effects of the invention are as follows:
1. the automatic adjustment of the treatment process parameters is carried out according with the technical means of the sewage treatment process, so that the system can always keep the treatment process of pollutants in the sewage in the optimal state according with the sewage treatment process and meet the effluent standard on the premise of not needing manual intervention. The process parameters include, but are not limited to: the biochemical reaction aeration time and intensity, the residual sludge discharge amount, the sludge reflux amount and the dosage;
2. the process trend is predicted based on the Elman neural network. The basic Elman neural network consists of an input layer, a hidden layer, a connection layer and an output layer. There is a connection layer for constituting a local feedback. The transmission function of the connection layer is a linear function, but one more delay unit is added, so that the connection layer can memorize the past state and can be used as the input of the hidden layer together with the input of the network at the next moment, so that the network has a dynamic memory function;
drawings
FIG. 1 is a schematic diagram of a sewage treatment analysis and control method based on data mining and intelligent algorithm according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to fig. 1 of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1:
a sewage treatment analysis and control method based on data mining and intelligent algorithm, a sewage treatment terminal is provided with a meteorological data acquisition system, a water inlet COD instrument, a TN instrument, a pH meter, a thermometer, a water inlet flow meter, an ORP instrument, a water outlet online monitor, a plurality of pumps, a plurality of dosing devices and a plurality of air blowers, a central control system is established, the instruments are connected to a CPS micro control unit, the meteorological data acquisition system, the water inlet COD instrument, the TN instrument, the pH meter, the thermometer, the water inlet flow meter, the ORP instrument and the water outlet online monitor are connected with the CPS micro control unit, the CPS micro control unit is connected with the pumps, the dosing devices and the air blowers, the CPS micro control unit is connected with the central control system through a communication module and a network,
the System comprises a central control System and a field CPS micro control unit, wherein a Cyber-Physical System (CPS) which is trained based on a large amount of sewage treatment plant operation data establishment algorithm model is prestored in the central control System and the field CPS micro control unit, various instrument parameters are used as input of the Cyber-Physical System, output of the Cyber-Physical System is corresponding operation instructions, a plurality of pumps, a plurality of dosing devices and a plurality of blowers are driven through the operation instructions, and the CPS micro control unit is connected with a communication module, and various instrument data, abnormality identification and diagnosis data of a sewage treatment terminal and control and decision strategies obtained through data mining, fuzzy algorithm and artificial intelligence are sent to the central control System and a mobile terminal through the communication module. The central control system also sends the upper control and decision strategy to the CPS micro-control unit through the communication module, and feeds back and controls various control parameters of the sewage treatment terminal.
The system comprises a meteorological data acquisition system, a water inlet COD (chemical oxygen demand) instrument, a TN (twisted nematic) instrument, a pH meter, a thermometer, a water inlet flowmeter, an ORP (oxidation-reduction potential) instrument and a water outlet online monitor, wherein the CPS micro control unit respectively acquires nine variable signals with independent regulation periods and regulation amplitudes, namely meteorological data, a water inlet COD (chemical oxygen demand) value, a sewage treatment terminal pH value, a sewage treatment terminal temperature, an ORP (oxidation-reduction potential), a water outlet COD (chemical oxygen demand) value, a water outlet ammonia nitrogen value, a water outlet TN (total phosphorus value) and a water outlet total phosphorus value.
The neural network prediction model comprises an input layer, a hidden layer, a connection layer and an output layer, wherein the trained neural network prediction model is obtained by training a transmission function of the connection layer through synchronous historical data.
The training content of the connection layer comprises the following steps:
step S1: collecting four variable signals and fault alarm state values in a T0 time period, wherein the T0 time period comprises a plurality of unit time T, counting the four variable signals in the unit time according to a time sequence in the fault alarm state, and sending the four variable signals to the step S2;
step S2: performing regression calculation on the four variable signals in the multiple fault alarm states to obtain a linear relation of the four variable signals in unit time;
it should be noted that the four variable signal values include time (accurate to minute, including weather and temperature of the day), characteristic parameters, corresponding states (including whether failure occurs) under the characteristic parameters, and required interval values of the characteristic parameters (combined with the set interval of the day), the four variable signals are calculated, the positive scores of the satisfied intervals are calculated, the negative scores of the unsatisfied intervals are calculated, and the total scores of the single characteristic parameters of the day are calculated and summarized.
Step S3: and combining the temperature parameter of the water quality and the operation instruction of the blower in the T0 time period, and combining the linear relation in unit time to obtain the trained linear function of the connecting layer. Referring to fig. 1, the present application further adopts data methods such as a fuzzy algorithm, etc. to train the variable signal, obtain an optimal sewage treatment control model, obtain a linear relationship between a single characteristic parameter and the sewage treatment control model, and form an evaluation system for sewage treatment.
The CPS micro-control unit sends out judgment standards of the emergency degree grade, wherein the judgment standards comprise a first-level A standard, a first-level B standard and other settable discharge standards, and the target values of COD, ammonia nitrogen, TN and TP control in the first-level A standard are smaller than the target values of COD, ammonia nitrogen, TN and TP control in the first-level B standard.
It should be noted that, referring to fig. 1, in this embodiment, when a sewage treatment system having a plurality of sewage treatment terminals is used, statistics of total scores of characteristic parameters is performed according to types of different sewage treatment terminals, it is considered that sewage containers of different sizes and shapes affect the characteristic parameters in water quality, and in order to obtain more objective characteristic parameters, correlation is performed with sewage treatment efficiency, so that the types of specific sewage containers are matched, and it is noted that the sewage treatment efficiency in this embodiment is characterized by being set to an emergency degree grade, a total score of characteristic parameters, and a treatment speed of sewage per cubic meter (a ratio of a volume of a sewage container to a difference between water outlet time and water inlet time).
Claims (6)
1. A sewage treatment analysis and control method based on data mining and intelligent algorithm is characterized in that a sewage treatment terminal is provided with a meteorological data acquisition system, a water inlet COD (chemical oxygen demand) instrument, a TN (twisted nematic) instrument, a pH meter, a thermometer, a water inlet flow meter, an ORP (oxidation-reduction potential) instrument, a water outlet online monitor, a plurality of pumps, a plurality of dosing devices and a plurality of air blowers, a central control system is established, the instruments are connected to a CPS (physical control system) micro control unit, the meteorological data acquisition system, the water inlet COD instrument, the TN instrument, the pH meter, the thermometer, the water inlet flow meter, the ORP instrument and the water outlet online monitor are connected with the CPS micro control unit, the CPS micro control unit is connected with the pumps, the dosing devices and the air blowers, and the CPS micro control unit is connected with the central control system through a communication module and a network.
The CPS micro control unit and the central control system are pre-stored with trained neural network prediction models, parameters of various instruments are used as input of the neural network prediction models, output of the neural network prediction models is corresponding operation instructions, the operation instructions drive target working time length and power of a pump, a dosing machine, a blower and the like, the CPS micro control unit is further connected with a communication module, and data of various instruments of the sewage treatment terminal are sent to the mobile terminal through the communication module.
2. The method for analyzing and controlling sewage treatment based on data mining and intelligent algorithm according to claim 1, wherein the meteorological data acquisition system, the water inlet COD meter, the TN meter, the pH meter, the thermometer, the water inlet flow meter, the ORP meter and the water outlet on-line monitor, the CPS micro control unit respectively acquires nine variable signals with independent adjustment period and adjustment amplitude, namely meteorological data, water inlet COD value, sewage treatment terminal pH value, sewage treatment terminal temperature, ORP, water outlet COD value, water outlet ammonia nitrogen value, water outlet TN value and water outlet total phosphorus value.
3. The method of claim 2, wherein the neural network prediction model comprises a service layer, an input layer, an application layer, a hidden layer, a connection layer and an output layer, and wherein the trained neural network prediction model is obtained by training a transmission function of the connection layer through synchronous historical data.
4. The method for analyzing and controlling sewage treatment based on data mining and intelligent algorithm of claim 3, wherein the training content of the connection layer comprises the following steps:
step S1: collecting nine variable signals and fault alarm state values in a T0 time period, wherein the T0 time period comprises a plurality of unit time T, counting the nine variable signals in the unit time according to a time sequence in the fault alarm state, and sending the nine variable signals to the step S2;
step S2: performing regression calculation on the nine variable signals in the multiple fault alarm states to obtain a linear relation of the nine variable signals in unit time;
step S3: and in combination with the water inlet COD value, the sewage treatment terminal pH value, the sewage treatment terminal temperature, the ORP, the water outlet COD value, the water outlet ammonia nitrogen value, the water outlet TN value, the water outlet total phosphorus value and the operation instructions of the pump, the dosing machine and the blower of the water quality in the T0 time period, obtaining the trained nonlinear function of the connecting layer through data mining and analysis.
5. The method for analyzing and controlling sewage treatment based on data mining and intelligent algorithm according to claim 1, wherein different operation commands are used for driving equipment such as pumps, dosing machines and blowers to work at corresponding rotating speeds and powers, the operation commands are decomposed into different urgency levels from low to high, and the higher the urgency level is, the higher the power of the corresponding pumps, dosing machines and blowers is.
6. The method as claimed in claim 5, wherein the criteria for determining the level of urgency sent by the CPS micro-control unit include a primary A standard, a primary B standard and other settable discharge standards, and the target values of COD, ammonia nitrogen, TN and TP in the primary A standard are less than those in the primary B standard.
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| CN117764332A (en) * | 2023-12-19 | 2024-03-26 | 广东精英纺织服饰科技有限公司 | Environment-friendly and efficient textile printing and dyeing wastewater treatment system |
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Application publication date: 20220426 |