CN117238389B - Fluorine-containing wastewater treatment system and method based on intelligent dosing - Google Patents
Fluorine-containing wastewater treatment system and method based on intelligent dosing Download PDFInfo
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- 229910052731 fluorine Inorganic materials 0.000 title claims abstract description 182
- 239000011737 fluorine Substances 0.000 title claims abstract description 182
- YCKRFDGAMUMZLT-UHFFFAOYSA-N Fluorine atom Chemical compound [F] YCKRFDGAMUMZLT-UHFFFAOYSA-N 0.000 title claims abstract description 181
- 238000000034 method Methods 0.000 title claims abstract description 67
- 238000004065 wastewater treatment Methods 0.000 title claims abstract description 52
- 239000002351 wastewater Substances 0.000 claims abstract description 226
- 239000003814 drug Substances 0.000 claims abstract description 191
- 238000006243 chemical reaction Methods 0.000 claims abstract description 82
- 238000012549 training Methods 0.000 claims abstract description 61
- 239000003795 chemical substances by application Substances 0.000 claims abstract description 13
- 238000004062 sedimentation Methods 0.000 claims description 183
- 239000003153 chemical reaction reagent Substances 0.000 claims description 136
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 80
- 238000001556 precipitation Methods 0.000 claims description 29
- 238000012545 processing Methods 0.000 claims description 22
- 238000003062 neural network model Methods 0.000 claims description 17
- 238000004364 calculation method Methods 0.000 claims description 12
- 238000004590 computer program Methods 0.000 claims description 10
- 239000013049 sediment Substances 0.000 claims description 4
- 125000004122 cyclic group Chemical group 0.000 claims description 2
- 238000012512 characterization method Methods 0.000 abstract 1
- KRHYYFGTRYWZRS-UHFFFAOYSA-M Fluoride anion Chemical compound [F-] KRHYYFGTRYWZRS-UHFFFAOYSA-M 0.000 description 15
- 238000004458 analytical method Methods 0.000 description 6
- 238000003756 stirring Methods 0.000 description 6
- 238000010586 diagram Methods 0.000 description 5
- 230000000694 effects Effects 0.000 description 5
- 238000005259 measurement Methods 0.000 description 4
- 239000013043 chemical agent Substances 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000005189 flocculation Methods 0.000 description 3
- 230000016615 flocculation Effects 0.000 description 3
- 238000011160 research Methods 0.000 description 3
- 239000000126 substance Substances 0.000 description 3
- 230000007547 defect Effects 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 229940079593 drug Drugs 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000000306 recurrent effect Effects 0.000 description 2
- 238000013480 data collection Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 239000003344 environmental pollutant Substances 0.000 description 1
- 231100000719 pollutant Toxicity 0.000 description 1
- 239000010865 sewage Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
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Abstract
The invention belongs to the technical field of fluorine-containing wastewater treatment, and discloses a fluorine-containing wastewater treatment system and method based on intelligent dosing; acquisition ofGroup historical wastewater status data; the historical wastewater status data includes wastewater characteristic data and a medicament flow rate corresponding to the wastewater characteristic data; training a medicament selection model for predicting medicament flow based on historical wastewater state data; real-time wastewater characterization to be collectedInputting data into a medicament selection model to obtain predicted medicament flow, and taking the predicted medicament flow as a control parameter of the medicament delivery equipment in the reaction tank; the agent delivery device can be controlled in real time to deliver the agent, corresponding agent flow adjustment is made according to the change of the fluorine concentration of the wastewater, the agent flow is increased in time, and the fluorine concentration in the wastewater discharged by the fluorine-containing wastewater treatment device is ensured to reach the standard required level.
Description
Technical Field
The invention relates to the technical field of fluorine-containing wastewater treatment, in particular to a fluorine-containing wastewater treatment system and method based on intelligent dosing.
Background
The research mainly aiming at the high-fluorine wastewater treatment system is solved at home, and a part of the research also aims at the low-concentration fluorine wastewater treatment system, for example, a system and a method for deeply removing fluorine in fluorine wastewater are provided in Chinese patent with the application publication number of CN 116395814A; the intelligent chemical adding device comprises a mixing stirring area, a flocculation stirring area and a sedimentation area which are sequentially connected, wherein one side of the mixing stirring area is provided with a water inlet end, and the water inlet end, the mixing stirring area and the flocculation stirring area are respectively provided with an automatic chemical adding point which is connected with an intelligent chemical adding system; a water outlet end is arranged at one side of the sedimentation zone, a fluoride on-line monitor is arranged at the water outlet end, an image recognition system is arranged at the flocculation stirring zone, and the fluoride on-line monitor and the image recognition system are respectively connected with an intelligent dosing system; the problems of frequent manual adjustment of a dosing metering pump, more and less time and unstable pollutant removal effect during actual dosing and the like when the water quality and the water quantity of sewage fluctuate can be effectively solved; although the above technology can realize automatic drug addition, research and practical application of the inventor on the above technology and the prior art find that the above technology and the prior art have at least the following partial defects:
(1) The dosage of the medicament cannot be adjusted in real time according to the fluorine concentration of the wastewater, and the medicament adding equipment can be controlled to add medicament only by data collection or manual operation after a period of time;
(2) The reagent dispensing point cannot cover the fluorine-containing wastewater treatment equipment, and the reagent and wastewater react insufficiently;
(3) Whether the dosage of the medicament is increased can only be judged according to the fluorine concentration of the wastewater at the water outlet end, the fluorine concentration of the wastewater can not be effectively reduced, and the fluoride removal effect in the wastewater is unstable.
In view of the above, the present invention provides a fluorine-containing wastewater treatment system and method based on intelligent dosing to solve the above problems.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention provides the following technical scheme for achieving the purposes: the fluorine-containing wastewater treatment method based on intelligent dosing comprises the following steps:
acquisition ofGroup historical wastewater status data; the historical wastewater status data includes wastewater characteristic data and a medicament flow rate corresponding to the wastewater characteristic data;
training a medicament selection model for predicting medicament flow based on historical wastewater state data;
dividing fluorine-containing wastewater treatment equipment intoA plurality of regions;
inputting the collected real-time wastewater characteristic data into a reagent selection model to obtain predicted reagent flow, and taking the predicted reagent flow as a control parameter of a reagent delivery device in the reaction tank;
Collecting wastewater characteristic data of a water inlet end of a sedimentation tank, and marking the fluorine-containing concentration of wastewater in the wastewater characteristic data of the water inlet end of the sedimentation tank as a first treatment concentration;
analyzing the first treatment concentration, and judging whether a secondary treatment instruction or a medicament adding instruction is generated;
if a secondary treatment instruction is generated, inputting the collected real-time wastewater characteristic data of the water inlet end of the sedimentation tank into a reagent selection model to obtain predicted reagent flow, and taking the predicted reagent flow as a control parameter of the reagent feeding equipment in the sedimentation tank; if a reagent increasing instruction is generated, calculating reagent flow increasing amount of the reaction tank, and taking the reagent flow in the reaction tank plus the reagent flow increasing amount of the reaction tank as new reagent flow of the reaction tank;
collecting the fluorine concentration of the wastewater at the water outlet end of the sedimentation tank, and marking the fluorine concentration of the wastewater at the water outlet end of the sedimentation tank as second treatment concentration;
analyzing the second treatment concentration to determine whether to generate a reagent increase instruction;
if the secondary medicament increasing instruction is generated, calculating the medicament flow increasing amount of the sedimentation tank, and adding the medicament flow in the sedimentation tank to the medicament flow increasing amount of the sedimentation tank to serve as new medicament flow of the sedimentation tank.
Further, the wastewater characteristic data includes wastewater flow and wastewater fluorine concentration.
Further, the agent selection model specific training process includes:
taking the wastewater characteristic data as input of a medicament selection model, wherein the medicament selection model takes predicted medicament flow of each group of wastewater characteristic data as output, takes actual medicament flow corresponding to the group of wastewater characteristic data as a prediction target, and takes the sum of prediction errors of all the wastewater characteristic data as a training target; wherein, the calculation formula of the prediction error is as followsWherein->For prediction error +.>Numbering of the characteristic data of the wastewater, +.>Is->Predicted reagent flow corresponding to group wastewater characteristic data, < + >>Is->The actual medicament flow corresponding to the group wastewater characteristic data; training the medicament selection model until the sum of the prediction errors reaches convergence, and stopping training; the agent selection model is either a deep neural network model or a deep belief network model.
Further, the fluorine-containing wastewater treatment equipment comprises a reaction tank, a sedimentation tank, a dosing hole, a dosing pipe and a medicament delivery equipment, and divides the reaction tank intoThe sedimentation tank is divided into a plurality of areas>Area(s)>The method comprises the steps of carrying out a first treatment on the surface of the The medicament delivery device delivers medicaments to the reaction tank and the sedimentation tank through n medicament adding holes, the n medicament adding holes are in one-to-one correspondence with n areas, and the reaction tank is provided with The medicine adding holes and the sedimentation tank are provided with +.>And the dosing holes are arranged on the dosing pipe, and the dosing pipe is connected with the medicament delivery equipment and the fluorine-containing wastewater treatment equipment.
Further, the method for judging whether to generate the secondary processing instruction or the medicament adding instruction comprises the following steps:
comparing and analyzing the first treatment concentration with a preset fluorine-containing concentration threshold value;
if the first treatment concentration is smaller than the fluorine-containing concentration threshold value, a secondary treatment instruction and a medicament increasing instruction are not generated;
if the first treatment concentration is greater than or equal to the fluorine concentration threshold value and the first treatment concentration divided by the fluorine concentration threshold value is less than or equal to R, generating a secondary treatment instruction, wherein R is a numerical value greater than 1;
if the first treatment concentration divided by the fluorine-containing concentration threshold is greater than R, generating a secondary treatment instruction and a reagent increase instruction;
the fluorine concentration threshold is determined by personnel according to fluoride emission standards established by various government authorities.
Further, the amount of increase in the reagent flow rate in the reaction tank was calculated as follows:
；
in the middle ofFor increasing the flow of the reagent in the reaction tank, +.>For the fluorine concentration of the wastewater at the water inlet end of the sedimentation tank,is a threshold value of fluorine concentration,>is the wastewater flow of the water inlet end of the sedimentation tank.
Further, the method of determining whether to generate the secondary medicament addition instruction includes:
Comparing and analyzing the second treatment concentration with a preset fluorine-containing concentration threshold value;
if the second treatment concentration is less than or equal to the fluorine-containing concentration threshold, a secondary medicament increasing instruction is not generated;
and if the second treatment concentration is greater than the fluorine-containing concentration threshold, generating a secondary medicament increasing instruction.
Further, the calculation of the increase in the reagent flow rate in the sedimentation tank is as follows:
；
in the middle ofFor increasing the flow of the medicament in the sedimentation tank, +.>For the fluorine concentration of the wastewater at the water outlet end of the sedimentation tank,is the wastewater flow of the water outlet end of the sedimentation tank.
Further, inputting the Q second treatment concentrations collected continuously into a concentration prediction model to predict the second treatment concentration at the future time; the training method of the concentration prediction model comprises the following steps:
constructing a second treatment concentration set by using the Q continuously collected second treatment concentrations, and training a prediction model for predicting the second treatment concentration at the future moment based on the second treatment concentration set;
presetting a sliding step length L and a sliding window length; converting the second processing concentration in the second processing concentration set into a plurality of training samples by using a sliding window method, taking the training samples as input of a circulating neural network model, taking the second processing concentration after predicting the sliding step length L as output, taking the subsequent second processing concentration of each training sample as a prediction target, taking the prediction accuracy rate as a training target, and training the circulating neural network model; generating a concentration prediction of the second treatment concentration at a future time based on the second treatment concentration; the recurrent neural network model may be an RNN neural network model.
Further, collecting an image of the sedimentation tank through a CCD camera, carrying out graying treatment on the image, and collecting gray values of Y pixel points;
comparing and analyzing the gray values of the Y pixel points with a gray value threshold, if the gray value of the pixel point is larger than or equal to the gray value threshold, marking the pixel point as a precipitation point, and if the gray value of the pixel point is smaller than a difference value threshold, not marking the pixel point as the precipitation point; the gray value threshold is that staff collects a plurality of sedimentation tank images and carries out graying treatment in a historical fluorine-containing wastewater treatment stage, gray values of pixel points corresponding to a sedimentation area in each sedimentation tank are used as an analysis set, and the average value of the lowest gray values in the plurality of analysis sets is used as a gray value threshold;
counting the number of precipitation points and calculating the precipitation area;
the sedimentation area was calculated as follows:
:
in the method, in the process of the invention,for precipitation area->For the number of sediment spots, +.>Is the area of a precipitation point, +.>Is a proportionality coefficient;
the area of one sedimentation point is obtained by the resolution ratio of an image, the actual area of the sedimentation tank is divided by the image area of the sedimentation tank to be used as a proportionality coefficient, and the actual area of the sedimentation tank and the image area of the sedimentation tank are obtained by the measurement of staff;
Calculating a second treatment concentration according to the precipitation area, and sending the second treatment concentration to a second concentration judging module;
the second treatment concentration was calculated as follows:
:
in the method, in the process of the invention,is a weight coefficient.
The fluorine-containing wastewater treatment system based on intelligent dosing realizes the fluorine-containing wastewater treatment method based on intelligent dosing, and comprises the following steps:
a first data acquisition module for acquiringThe historical wastewater state data is assembled and sent to a model training module; the historical wastewater status data includes wastewater characteristic data and a medicament flow rate corresponding to the wastewater characteristic data;
the model training module is used for training a medicament selection model for predicting medicament flow based on the historical wastewater state data;
the area dividing module divides the fluorine-containing wastewater treatment equipment intoA plurality of regions;
the first reagent delivery module inputs the collected real-time wastewater characteristic data into a reagent selection model to obtain predicted reagent flow, and the predicted reagent flow is used as a control parameter of the reagent delivery equipment in the reaction tank;
the second data acquisition module acquires the wastewater characteristic data of the water inlet end of the sedimentation tank, and marks the fluorine concentration of wastewater in the wastewater characteristic data of the water inlet end of the sedimentation tank as first treatment concentration;
The first concentration judging module is used for analyzing the first treatment concentration and judging whether a secondary treatment instruction or a medicament adding instruction is generated or not;
the second reagent delivery module inputs the collected real-time wastewater characteristic data of the water inlet end of the sedimentation tank into a reagent selection model if a secondary treatment instruction is generated, so as to obtain predicted reagent flow, and the predicted reagent flow is used as a control parameter of the reagent delivery equipment in the sedimentation tank; if a reagent increasing instruction is generated, calculating reagent flow increasing amount of the reaction tank, and taking the reagent flow in the reaction tank plus the reagent flow increasing amount of the reaction tank as new reagent flow of the reaction tank;
the third data acquisition module is used for acquiring the fluorine concentration of the wastewater at the water outlet end of the sedimentation tank and marking the fluorine concentration of the wastewater at the water outlet end of the sedimentation tank as a second treatment concentration;
the second concentration judging module is used for analyzing the second treatment concentration and judging whether a medicament increasing instruction is generated or not;
and the medicament adding module is used for calculating the medicament flow increasing amount of the sedimentation tank if a secondary medicament adding instruction is generated, and adding the medicament flow in the sedimentation tank to the medicament flow increasing amount of the sedimentation tank to serve as new medicament flow of the sedimentation tank.
An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the intelligent dosing-based fluorine-containing wastewater treatment method when executing the computer program.
A computer readable storage medium having stored thereon a computer program which when executed implements the intelligent dosing-based fluorine-containing wastewater treatment method.
The fluorine-containing wastewater treatment system and the method based on intelligent dosing have the technical effects and advantages that:
1. the wastewater flow and the fluorine concentration of the wastewater at the water inlet end of the reaction tank, the water inlet end of the sedimentation tank and the water outlet end of the sedimentation tank are detected in real time, so that the medicament delivery device can be controlled in real time to deliver medicaments, corresponding medicament flow adjustment is made according to the change of the fluorine concentration of the wastewater, the medicament flow is increased in time, and the fluorine concentration in the wastewater discharged by the fluorine-containing wastewater treatment device is ensured to reach the standard required level; a plurality of dosing holes are formed in the reaction tank and the sedimentation tank, so that the full contact reaction of the medicament and the wastewater is ensured, and the problem that the medicament in the dosing pipe is blocked easily due to single-point dosing in the center is prevented.
2. The fluorine concentration of the wastewater at the water outlet end of the sedimentation tank is predicted at the future moment, the dosage of the traditional Chinese medicine agent in the sedimentation tank is adjusted in advance, the flow rate of the traditional Chinese medicine agent is increased, and the condition that the fluorine concentration of the wastewater discharged by the short-time fluorine-contained wastewater treatment equipment exceeds the standard due to untimely dosing is avoided.
3. The CCD camera is used for collecting images of the sedimentation tank, and calculating the sedimentation area generated after the wastewater reacts with the medicament, so that the fluorine concentration of the wastewater at the water outlet end of the sedimentation tank is calculated, the fluorine concentration of the wastewater at the water outlet end of the sedimentation tank can be obtained in real time, the medicament delivery equipment is controlled in real time to increase the medicament flow, and the fluoride concentration in the discharged wastewater is ensured to be reduced to the standard required level.
Drawings
FIG. 1 is a schematic diagram of a fluorine-containing wastewater treatment system based on intelligent dosing according to example 1 of the present invention;
FIG. 2 is a schematic view of a fluorine-containing wastewater treatment facility according to example 1 of the present invention;
FIG. 3 is a schematic diagram of a fluorine-containing wastewater treatment system based on intelligent dosing according to example 2 of the present invention;
FIG. 4 is a schematic diagram of a fluorine-containing wastewater treatment system based on intelligent dosing in accordance with example 3 of the present invention;
FIG. 5 is a schematic diagram of a method for treating fluorine-containing wastewater based on intelligent dosing in example 4 of the present invention;
fig. 6 is a schematic diagram of an electronic device according to embodiment 5 of the present invention.
Reference numerals: 1. a reaction tank; 2. a dosing hole; 3. a dosing tube; 4. a sedimentation tank; 5. a medicament delivery device.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, the fluorine-containing wastewater treatment system based on intelligent dosing according to the embodiment includes a first data acquisition module, a model training module, a region dividing module, a first medicament delivery module, a second data acquisition module, a first concentration judgment module, a second medicament delivery module, a third data acquisition module, a second concentration judgment module and a medicament adding module, wherein the modules are connected in a wired and/or wireless manner;
a first data acquisition module for acquiringGroup history wastewater status data,/->Transmitting historical wastewater state data to a model training module for integers greater than 1; the historical wastewater status data includes wastewater characteristic data and a medicament flow rate corresponding to the wastewater characteristic data;
the wastewater characteristic data comprise wastewater flow and wastewater fluorine concentration; the wastewater flow is obtained through an electromagnetic flowmeter arranged at the water inlet end of the reaction tank, the wastewater flow is increased, and the corresponding medicament flow is also increased, so that enough medicaments are ensured to neutralize the wastewater, and fluoride in the wastewater can be effectively removed; the fluorine concentration of the wastewater is obtained through a fluoride on-line detector arranged at the water inlet end of the reaction tank, the fluorine concentration of the wastewater is increased, and the flow of the medicament is increased; when the flow rate of the reagent is increased, the concentration of the reagent in the reaction tank is increased, and the high-concentration reagent has better treatment capacity for the wastewater with high fluorine concentration, so that fluoride in the wastewater can be effectively removed; the medicament flow is obtained through an electromagnetic flowmeter arranged at the inlet of the medicament adding pipe;
Under an experimental environment, a worker sequentially adopts different reagent flows to treat the same group of wastewater characteristic data, sequentially collects the fluorine concentration of the treated wastewater, and takes the reagent flow with the lowest fluorine concentration in the wastewater as the reagent flow corresponding to the wastewater characteristic data; acquiring the medicament flow corresponding to different medicament flows according to the method sequentially;
the model training module is used for training a medicament selection model for predicting medicament flow based on the historical wastewater state data; the specific training process of the medicament selection model comprises the following steps:
taking the wastewater characteristic data as input of a medicament selection model, wherein the medicament selection model takes predicted medicament flow rate of each group of wastewater characteristic data as output, and the medicament selection model corresponds to the group of wastewater characteristic dataThe actual medicament flow is taken as a prediction target, and the sum of prediction errors of all the wastewater characteristic data is minimized to be taken as a training target; wherein, the calculation formula of the prediction error is as followsWherein->For prediction error +.>Numbering of the characteristic data of the wastewater, +.>Is->Predicted reagent flow corresponding to group wastewater characteristic data, < + >>Is->The actual medicament flow corresponding to the group wastewater characteristic data; training the medicament selection model until the sum of the prediction errors reaches convergence, and stopping training;
The agent selection model is either a deep neural network model or a deep belief network model;
the area dividing module divides the fluorine-containing wastewater treatment equipment intoArea(s)>Is an integer greater than 1;
the first reagent delivery module inputs the collected real-time wastewater characteristic data into a reagent selection model to obtain predicted reagent flow, and the predicted reagent flow is used as a control parameter of the reagent delivery equipment in the reaction tank;
referring to FIG. 2, the fluorine-containing wasteThe water treatment equipment comprises a reaction tank 1, a dosing hole 2, a dosing pipe 3, a sedimentation tank 4 and a medicament delivery equipment 5, and divides the reaction tank intoThe sedimentation tank is divided into a plurality of areas>Area(s)>The method comprises the steps of carrying out a first treatment on the surface of the The medicament delivery device 5 delivers medicaments to the reaction tank 1 and the sedimentation tank 4 through n medicament adding holes 2, wherein the n medicament adding holes are in one-to-one correspondence with n areas, namely the reaction tank is provided with +>The medicine adding holes and the sedimentation tank are provided with +.>The dosing holes 2 are positioned on the dosing pipe 3, and the dosing pipe 3 is connected with the medicament delivery device 5; the reagent and the wastewater can be fully mixed and reacted, fluoride in the wastewater is effectively removed, and the problem that the reagent in the reagent adding pipe is easy to be blocked due to single-point reagent adding in the center is prevented;
the wastewater flows into the reaction tank from the water inlet end of the reaction tank, reacts with the medicament in the reaction tank, and flows into the sedimentation tank through the water outlet end of the reaction tank, so that the wastewater in the reaction tank is controlled The medicine adding holes are used for adding medicines into the reaction tank;
the second data acquisition module acquires the wastewater characteristic data of the water inlet end of the sedimentation tank, and marks the fluorine concentration of wastewater in the wastewater characteristic data of the water inlet end of the sedimentation tank as first treatment concentration; the wastewater flow is obtained through an electromagnetic flowmeter arranged at the water inlet end of the sedimentation tank, and the fluorine concentration of the wastewater is obtained through an online fluorine electrode arranged at the water inlet end of the sedimentation tank;
the reason for re-collecting the characteristic data of the wastewater in the sedimentation tank is that other conditions possibly exist in the treatment process of the fluorine-containing wastewater, so that the fluorine concentration in the wastewater cannot be reduced to the standard required level, for example, the mixing time of the wastewater and the medicament in the reaction tank is insufficient, and the medicament cannot fully react with the wastewater; the reagent in the reagent adding pipe of the reaction tank is blocked, so that the reagent cannot enter the reaction tank to treat wastewater; error exists in the data acquisition equipment at the water inlet end of the reaction tank, so that the fluorine concentration of the wastewater is misreported, and the situation that the medicine put in by the medicine putting equipment is insufficient and the like is caused; the secondary treatment is carried out in the sedimentation tank or the flow rate of the chemical agent put in the reaction tank is increased, so that the concentration of fluorine in the wastewater discharged from the water outlet end of the sedimentation tank is ensured to reach the standard required level;
The first concentration judging module is used for analyzing the first treatment concentration and judging whether a secondary treatment instruction or a medicament adding instruction is generated or not;
the method for judging whether to generate the secondary processing instruction or the medicament adding instruction comprises the following steps:
comparing and analyzing the first treatment concentration with a preset fluorine-containing concentration threshold value;
if the first treatment concentration is smaller than the fluorine concentration threshold value, a secondary treatment instruction and a reagent adding instruction are not generated, which indicates that the fluorine-containing wastewater fully reacts with the reagent in the reaction tank at the moment, the fluorine concentration in the wastewater reaches the standard required level, and other operations are not needed;
if the first treatment concentration is greater than or equal to the fluorine-containing concentration threshold and the first treatment concentration divided by the fluorine-containing concentration threshold is less than or equal to R, generating a secondary treatment instruction, but not generating a reagent increase instruction; the method is characterized in that the fluorine-containing wastewater is not fully reacted with the reagent in the reaction tank, the fluorine-containing concentration in the wastewater does not reach the standard required level, the secondary treatment is needed in the sedimentation tank, but the fluorine-containing concentration in the wastewater is effectively reduced in the reaction tank, and the reagent does not need to be added in the reaction tank; r is set by a worker according to a fluorine concentration threshold, R is a numerical value larger than 1, and R is preferably 2 in the embodiment;
If the first treatment concentration divided by the fluorine concentration threshold is greater than R, generating a secondary treatment instruction and a reagent increasing instruction, which indicate that the reagent in the reaction tank is insufficient to effectively reduce the fluorine concentration in the wastewater at the moment, and the reagent in the reaction tank needs to be increased, namely the reagent flow is increased; the fluorine concentration in the wastewater still far from reaching the standard required level, and secondary treatment is needed in the sedimentation tank to ensure that the fluorine concentration in the wastewater flowing out of the water outlet end of the sedimentation tank reaches the standard required level;
the fluorine concentration threshold value is determined by staff according to fluoride emission standards established by related departments of government at all levels;
the second reagent delivery module inputs the collected real-time wastewater characteristic data of the water inlet end of the sedimentation tank into a reagent selection model to obtain predicted reagent flow, takes the predicted reagent flow as a control parameter of the reagent delivery equipment in the sedimentation tank, and controls the sedimentation tankAdding the medicament into the sedimentation tank through the medicament adding holes; if a reagent increasing instruction is generated, calculating reagent flow increasing amount of the reaction tank, and taking the reagent flow in the reaction tank plus the reagent flow increasing amount of the reaction tank as new reagent flow of the reaction tank;
The calculation of the reagent flow increase in the reaction tank is as follows:
；
in the middle ofFor increasing the flow of the reagent in the reaction tank, +.>For the first treatment concentration, ++>Is a threshold value of fluorine concentration,>the wastewater flow is the wastewater flow at the water inlet end of the sedimentation tank;
the third data acquisition module is used for acquiring the fluorine concentration of the wastewater at the water outlet end of the sedimentation tank and marking the fluorine concentration of the wastewater at the water outlet end of the sedimentation tank as a second treatment concentration; the fluorine concentration of the wastewater is obtained through an online fluorine electrode arranged at the water outlet end of the sedimentation tank;
the second concentration judging module is used for analyzing the second treatment concentration and judging whether a secondary medicament increasing instruction is generated or not;
the method for judging whether to generate the secondary medicament increasing instruction comprises the following steps:
comparing and analyzing the second treatment concentration with a preset fluorine-containing concentration threshold value;
if the second treatment concentration is smaller than or equal to the fluorine concentration threshold value, a secondary reagent increasing instruction is not generated, which indicates that the fluorine concentration in the wastewater discharged from the water outlet end of the sedimentation tank reaches the standard required level, the fluoride removal effect in the wastewater is good, and the reagent flow put in the sedimentation tank does not need to be increased;
if the second treatment concentration is greater than the fluorine concentration threshold value, a secondary reagent increasing instruction is generated, which indicates that the fluorine concentration of the wastewater still does not reach the standard required level after the wastewater is subjected to secondary treatment in the sedimentation tank, and the reagent is required to be increased again, namely the reagent in the sedimentation tank is increased, the reagent flow is increased, so that the fluorine concentration in the wastewater flowing out of the water outlet end of the sedimentation tank is ensured to reach the standard required level, and the fluoride removing effect in the wastewater is improved;
The medicament adding module is used for calculating the medicament flow increasing amount of the sedimentation tank if a secondary medicament adding instruction is generated, and adding the medicament flow in the sedimentation tank to the medicament flow increasing amount of the sedimentation tank to serve as new medicament flow of the sedimentation tank;
the calculation of the increase of the reagent flow in the sedimentation tank is as follows:
；
in the middle ofFor increasing the flow of the medicament in the sedimentation tank, +.>For the second treatment concentration,/->The wastewater flow is the wastewater flow at the water outlet end of the sedimentation tank;
according to the embodiment, the wastewater flow and the fluorine concentration of the wastewater at the water inlet end of the reaction tank, the water inlet end of the sedimentation tank and the water outlet end of the sedimentation tank are detected in real time, so that the medicament delivery device can be controlled to deliver medicaments in real time, corresponding medicament flow adjustment is made according to the change of the fluorine concentration of the wastewater, the medicament flow is increased in time, and the fluorine concentration in the wastewater discharged by the fluorine-containing wastewater treatment device is ensured to reach the standard required level; a plurality of dosing holes are formed in the reaction tank and the sedimentation tank, so that the full contact reaction of the medicament and the wastewater is ensured, and the problem that the medicament in the dosing pipe is blocked easily due to single-point dosing in the center is prevented.
Example 2
Referring to fig. 3, the design of this embodiment is further improved based on embodiment 1, in the sedimentation tank, whether to increase the flow rate of the chemical agent to the sedimentation tank needs to be determined according to the fluorine concentration of the wastewater at the water outlet end of the sedimentation tank, and a certain hysteresis exists, so that the concentration of the fluoride in the wastewater cannot be reduced in time, and the wastewater with the fluorine concentration not reaching the standard requirement level is discharged out of the sedimentation tank; therefore, the embodiment provides a fluorine-containing wastewater treatment method based on intelligent dosing, which further comprises a concentration prediction module for predicting the fluorine-containing concentration of wastewater at the water outlet end of the sedimentation tank at a future moment, and increasing the flow of the medicament to the sedimentation tank in advance to ensure that the fluorine-containing concentration in wastewater discharged from the water outlet end of the sedimentation tank reaches the standard required level;
The concentration prediction module inputs the Q second processing concentrations continuously acquired into the concentration prediction model to predict the second processing concentration at the future moment, and sends the second processing concentration at the future moment to the second concentration judgment module; according to the second treatment concentration at the future moment, the flow rate of the medicament is increased to the sedimentation tank in advance, so that the fluorine-containing concentration in the wastewater discharged from the water outlet end of the sedimentation tank is ensured to reach the standard required level; the training method of the concentration prediction model comprises the following steps:
constructing a second treatment concentration set by using the Q continuously collected second treatment concentrations, and training a prediction model for predicting the second treatment concentration at the future moment based on the second treatment concentration set;
presetting a sliding step length L and a sliding window length according to the actual experience of a worker; converting the second processing concentration in the second processing concentration set into a plurality of training samples by using a sliding window method, taking the training samples as input of a circulating neural network model, taking the second processing concentration after predicting the sliding step length L as output, taking the subsequent second processing concentration of each training sample as a prediction target, taking the prediction accuracy rate as a training target, and training the circulating neural network model; generating a concentration prediction of the second treatment concentration at a future time based on the second treatment concentration; the cyclic neural network model may be an RNN neural network model;
Exemplary, assume a second set of treatment concentrationsComprises 10 groups of second treatment concentrations, +.>={/>, /> , ... , />},/>Is->A plurality of training samples are constructed by using sliding windows, the length of the sliding windows is defined to be 3, the sliding step length L is 1, each training sample comprises 3 continuous second treatment concentrations, and the next second treatment concentration of the 3 continuous second treatment concentrations is taken as a prediction target; for example:
{, />, />used as training data, { }>, />, />The prediction target corresponding to the ∈>；
{, />, />Used as training data, { }>, />, />The prediction target corresponding to the ∈>The method comprises the steps of carrying out a first treatment on the surface of the And so on for a concentration prediction model;
according to the embodiment, the fluorine concentration of the wastewater at the water outlet end of the sedimentation tank is predicted at the future moment, the dosage of the traditional Chinese medicine in the sedimentation tank is adjusted in advance, the medicine flow is increased, and the situation that the fluorine concentration of the wastewater discharged by the short-time fluorine-containing wastewater treatment equipment exceeds the standard due to untimely medicine adding is avoided.
Example 3
Referring to fig. 4, the present embodiment is further improved based on embodiments 1 and 2, and besides the advanced treatment of predicting the fluorine concentration of the wastewater at the outlet end of the sedimentation tank at a future time, the fluorine concentration of the wastewater can be calculated by collecting the sedimentation area generated after the wastewater in the sedimentation tank reacts with the chemical agent through a CCD camera; therefore, the embodiment provides a fluorine-containing wastewater treatment method based on intelligent dosing, which also comprises an image detection module;
The image detection module is used for acquiring an image of the sedimentation tank through the CCD camera, carrying out graying treatment on the image and collecting gray values of Y pixel points;
comparing and analyzing the gray values of the Y pixel points with a gray value threshold, if the gray value of the pixel point is larger than or equal to the gray value threshold, marking the pixel point as a precipitation point, and if the gray value of the pixel point is smaller than a difference value threshold, not marking the pixel point as the precipitation point; the gray value threshold is that staff collects a plurality of sedimentation tank images and carries out graying treatment in a historical fluorine-containing wastewater treatment stage, gray values of pixel points corresponding to a sedimentation area in each sedimentation tank are used as an analysis set, and the average value of the lowest gray values in the plurality of analysis sets is used as a gray value threshold;
counting the number of precipitation points and calculating the precipitation area;
the sedimentation area was calculated as follows:
:
in the method, in the process of the invention,for precipitation area->For the number of sediment spots, +.>Is the area of a precipitation point, +.>Is a proportionality coefficient;
the area of one sedimentation point is obtained by the resolution ratio of an image, the actual area of the sedimentation tank is divided by the image area of the sedimentation tank to be used as a proportionality coefficient, and the actual area of the sedimentation tank and the image area of the sedimentation tank are obtained by the measurement of staff;
Calculating a second treatment concentration according to the precipitation area, and sending the second treatment concentration to a second concentration judging module;
the second treatment concentration was calculated as follows:
:
in the method, in the process of the invention,is a weight coefficient;
when the weight coefficient is the determined gray value threshold, a worker obtains a plurality of precipitation areas through measurement, the collected second treatment concentration is subtracted by the first treatment concentration and divided by the precipitation area to obtain a plurality of values, and the average value of the plurality of values is used as the weight coefficient;
according to the embodiment, the CCD camera is used for collecting images of the sedimentation tank, the sedimentation area generated after the wastewater reacts with the medicament is calculated, so that the fluorine concentration of the wastewater at the water outlet end of the sedimentation tank is calculated, the fluorine concentration of the wastewater at the water outlet end of the sedimentation tank can be obtained in real time, the medicament delivery equipment is controlled in real time to increase the medicament flow, and the fluoride concentration in the discharged wastewater is guaranteed to be reduced to the standard required level.
Example 4
Referring to fig. 5, this embodiment is not described in detail in examples 1, 2 and 3, but the method for treating fluorine-containing wastewater based on intelligent dosing comprises:
acquisition ofGroup calendarHistory of wastewater status data; the historical wastewater status data includes wastewater characteristic data and a medicament flow rate corresponding to the wastewater characteristic data;
Training a medicament selection model for predicting medicament flow based on historical wastewater state data;
dividing fluorine-containing wastewater treatment equipment intoA plurality of regions;
inputting the collected real-time wastewater characteristic data into a reagent selection model to obtain predicted reagent flow, and taking the predicted reagent flow as a control parameter of a reagent delivery device in the reaction tank;
collecting wastewater characteristic data of a water inlet end of a sedimentation tank, and marking the fluorine-containing concentration of wastewater in the wastewater characteristic data of the water inlet end of the sedimentation tank as a first treatment concentration;
analyzing the first treatment concentration, and judging whether a secondary treatment instruction or a medicament adding instruction is generated;
if a secondary treatment instruction is generated, inputting the collected real-time wastewater characteristic data of the water inlet end of the sedimentation tank into a reagent selection model to obtain predicted reagent flow, and taking the predicted reagent flow as a control parameter of the reagent feeding equipment in the sedimentation tank; if a reagent increasing instruction is generated, calculating reagent flow increasing amount of the reaction tank, and taking the reagent flow in the reaction tank plus the reagent flow increasing amount of the reaction tank as new reagent flow of the reaction tank;
collecting the fluorine concentration of the wastewater at the water outlet end of the sedimentation tank, and marking the fluorine concentration of the wastewater at the water outlet end of the sedimentation tank as second treatment concentration;
Analyzing the second treatment concentration to determine whether to generate a reagent increase instruction;
if the secondary medicament increasing instruction is generated, calculating the medicament flow increasing amount of the sedimentation tank, and adding the medicament flow in the sedimentation tank to the medicament flow increasing amount of the sedimentation tank to serve as new medicament flow of the sedimentation tank.
Further, the wastewater characteristic data includes wastewater flow and wastewater fluorine concentration.
Further, the specific training process includes:
taking the wastewater characteristic data as input of a medicament selection model, wherein the medicament selection model takes predicted medicament flow of each group of wastewater characteristic data as output, takes actual medicament flow corresponding to the group of wastewater characteristic data as a prediction target, and takes the sum of prediction errors of all the wastewater characteristic data as a training target; wherein, the calculation formula of the prediction error is as followsWherein->For prediction error +.>Numbering of the characteristic data of the wastewater, +.>Is->Predicted reagent flow corresponding to group wastewater characteristic data, < + >>Is->The actual medicament flow corresponding to the group wastewater characteristic data; training the medicament selection model until the sum of the prediction errors reaches convergence, and stopping training;
the agent selection model is either a deep neural network model or a deep belief network model.
Further, the fluorine-containing wastewater treatment equipment comprises a reaction tank, a sedimentation tank, a dosing hole, a dosing pipe and a medicament delivery equipment, and divides the reaction tank intoThe sedimentation tank is divided into a plurality of areas>Area(s)>The method comprises the steps of carrying out a first treatment on the surface of the The medicament delivery device delivers medicaments to the reaction tank and the sedimentation tank through n medicament adding holes, the n medicament adding holes are in one-to-one correspondence with n areas, and the reaction tank is provided withThe medicine adding holes and the sedimentation tank are provided with +.>And the dosing holes are arranged on the dosing pipe, and the dosing pipe is connected with the medicament delivery equipment and the fluorine-containing wastewater treatment equipment.
Further, the method for judging whether to generate the secondary processing instruction or the medicament adding instruction comprises the following steps:
comparing and analyzing the first treatment concentration with a preset fluorine-containing concentration threshold value;
if the first treatment concentration is smaller than the fluorine-containing concentration threshold value, a secondary treatment instruction and a medicament increasing instruction are not generated;
if the first treatment concentration is greater than or equal to the fluorine concentration threshold value and the first treatment concentration divided by the fluorine concentration threshold value is less than or equal to R, generating a secondary treatment instruction, wherein R is a numerical value greater than 1;
if the first treatment concentration divided by the fluorine-containing concentration threshold is greater than R, generating a secondary treatment instruction and a reagent increase instruction;
the fluorine concentration threshold is determined by personnel according to fluoride emission standards established by various government authorities.
Further, the amount of increase in the reagent flow rate in the reaction tank was calculated as follows:
；
in the middle ofFor increasing the flow of the reagent in the reaction tank, +.>For the fluorine concentration of the wastewater at the water inlet end of the sedimentation tank,is a threshold value of fluorine concentration,>is the wastewater flow of the water inlet end of the sedimentation tank.
Further, the method of determining whether to generate the secondary medicament addition instruction includes:
comparing and analyzing the second treatment concentration with a preset fluorine-containing concentration threshold value;
if the second treatment concentration is less than or equal to the fluorine-containing concentration threshold, a secondary medicament increasing instruction is not generated;
and if the second treatment concentration is greater than the fluorine-containing concentration threshold, generating a secondary medicament increasing instruction.
Further, the calculation of the increase in the reagent flow rate in the sedimentation tank is as follows:
；
in the middle ofFor increasing the flow of the medicament in the sedimentation tank, +.>For the fluorine concentration of the wastewater at the water outlet end of the sedimentation tank,is the wastewater flow of the water outlet end of the sedimentation tank. />
Further, inputting the Q second treatment concentrations collected continuously into a concentration prediction model to predict the second treatment concentration at the future time; the training method of the concentration prediction model comprises the following steps:
constructing a second treatment concentration set by using the Q continuously collected second treatment concentrations, and training a prediction model for predicting the second treatment concentration at the future moment based on the second treatment concentration set;
Presetting a predicted time step L, a first sliding step length and a sliding window length according to actual experience of a worker; converting the second processing concentration in the second processing concentration set into a plurality of training samples by using a sliding window method, taking the training samples as input of a circulating neural network model, taking the second processing concentration after the prediction time step L as output, taking the subsequent second processing concentration of each training sample as a prediction target, taking the prediction accuracy rate as a training target, and training the circulating neural network model; generating a concentration prediction of the second treatment concentration at a future time based on the second treatment concentration; the recurrent neural network model may be an RNN neural network model.
Further, collecting an image of the sedimentation tank through a CCD camera, carrying out graying treatment on the image, and collecting gray values of Y pixel points;
comparing and analyzing the gray values of the Y pixel points with a gray value threshold, if the gray value of the pixel point is larger than or equal to the gray value threshold, marking the pixel point as a precipitation point, and if the gray value of the pixel point is smaller than a difference value threshold, not marking the pixel point as the precipitation point; the gray value threshold is that staff collects a plurality of sedimentation tank images and carries out graying treatment in a historical fluorine-containing wastewater treatment stage, gray values of pixel points corresponding to a sedimentation area in each sedimentation tank are used as an analysis set, and the average value of the lowest gray values in the plurality of analysis sets is used as a gray value threshold;
Counting the number of precipitation points and calculating the precipitation area;
the sedimentation area was calculated as follows:
:
in the method, in the process of the invention,for precipitation area->For the number of sediment spots, +.>Is the area of a precipitation point, +.>Is a proportionality coefficient;
the area of one sedimentation point is obtained by the resolution ratio of an image, the actual area of the sedimentation tank is divided by the image area of the sedimentation tank to be used as a proportionality coefficient, and the actual area of the sedimentation tank and the image area of the sedimentation tank are obtained by the measurement of staff;
calculating a second treatment concentration according to the precipitation area, and sending the second treatment concentration to a second concentration judging module;
the second treatment concentration was calculated as follows:
:
in the method, in the process of the invention,is a weight coefficient.
Example 5
Referring to fig. 6, the disclosure provides an electronic device, which includes a memory, a processor, and a computer program stored on the memory and capable of running on the processor, wherein the processor implements any one of the methods for treating fluorine-containing wastewater based on intelligent dosing provided by the methods when executing the computer program.
Since the electronic device described in this embodiment is an electronic device used for implementing the method for treating fluorine-containing wastewater based on intelligent dosing in the embodiment of the present application, based on the method for treating fluorine-containing wastewater based on intelligent dosing described in the embodiment of the present application, those skilled in the art can understand the specific implementation manner of the electronic device and various modifications thereof, so how to implement the method in the embodiment of the present application for this electronic device will not be described in detail herein. As long as the electronic equipment adopted by the person skilled in the art for implementing the fluorine-containing wastewater treatment method based on intelligent dosing in the embodiment of the application belongs to the scope of protection intended by the application.
Example 6
The embodiment discloses a computer readable storage medium, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes any one of the fluorine-containing wastewater treatment methods based on intelligent dosing provided by the methods when executing the computer program.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Finally: the foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (12)
1. The fluorine-containing wastewater treatment method based on intelligent dosing is characterized by comprising the following steps of:
acquisition ofGroup historical wastewater status data; the historical wastewater status data includes wastewater characteristic data and a medicament flow rate corresponding to the wastewater characteristic data;
Training a medicament selection model for predicting medicament flow based on historical wastewater state data;
dividing fluorine-containing wastewater treatment equipment intoA plurality of regions; the fluorine-containing wastewater treatment equipment comprises a reaction tank, a sedimentation tank, a dosing hole, a dosing pipe and a medicament delivery device, wherein the reaction tank is divided into +.>The sedimentation tank is divided into a plurality of areas>Area(s)>The method comprises the steps of carrying out a first treatment on the surface of the The medicament delivery device delivers medicaments to the reaction tank and the sedimentation tank through n medicament adding holes, the n medicament adding holes are in one-to-one correspondence with n areas, and the reaction tank is provided with +.>The medicine adding holes and the sedimentation tank are provided with +.>The dosing holes are arranged on the dosing pipe, and the dosing pipe is connected with the medicament delivery equipment and the fluorine-containing wastewater treatment equipment;
inputting the collected real-time wastewater characteristic data into a reagent selection model to obtain predicted reagent flow, and taking the predicted reagent flow as a control parameter of a reagent delivery device in the reaction tank;
collecting wastewater characteristic data of a water inlet end of a sedimentation tank, and marking the fluorine-containing concentration of wastewater in the wastewater characteristic data of the water inlet end of the sedimentation tank as a first treatment concentration;
analyzing the first treatment concentration, and judging whether a secondary treatment instruction or a medicament adding instruction is generated;
if a secondary treatment instruction is generated, inputting the collected real-time wastewater characteristic data of the water inlet end of the sedimentation tank into a reagent selection model to obtain predicted reagent flow, and taking the predicted reagent flow as a control parameter of the reagent feeding equipment in the sedimentation tank; if a reagent increasing instruction is generated, calculating reagent flow increasing amount of the reaction tank, and taking the reagent flow in the reaction tank plus the reagent flow increasing amount of the reaction tank as new reagent flow of the reaction tank;
Collecting the fluorine concentration of the wastewater at the water outlet end of the sedimentation tank, and marking the fluorine concentration of the wastewater at the water outlet end of the sedimentation tank as second treatment concentration;
analyzing the second treatment concentration to judge whether a secondary medicament increasing instruction is generated;
if the secondary medicament increasing instruction is generated, calculating the medicament flow increasing amount of the sedimentation tank, and adding the medicament flow in the sedimentation tank to the medicament flow increasing amount of the sedimentation tank to serve as new medicament flow of the sedimentation tank.
2. The intelligent dosing-based fluorine-containing wastewater treatment process of claim 1, wherein the wastewater characteristic data comprises wastewater flow and wastewater fluorine concentration.
3. The intelligent dosing-based fluorine-containing wastewater treatment process of claim 2, wherein the agent selection model training process comprises:
taking the wastewater characteristic data as input of a medicament selection model, wherein the medicament selection model takes predicted medicament flow of each group of wastewater characteristic data as output, takes actual medicament flow corresponding to the group of wastewater characteristic data as a prediction target, and takes the sum of prediction errors of all the wastewater characteristic data as a training target; wherein, the calculation formula of the prediction error is as followsWherein- >For prediction error +.>Numbering of the characteristic data of the wastewater, +.>Is->Predicted reagent flow corresponding to group wastewater characteristic data, < + >>Is->The actual medicament flow corresponding to the group wastewater characteristic data; training the medicament selection model until the sum of the prediction errors reaches convergence, and stopping training; the agent selection model is either a deep neural network model or a deep belief network model.
4. The method for treating fluorine-containing wastewater based on intelligent dosing according to claim 3, wherein the method for judging whether to generate the secondary treatment instruction or the reagent addition instruction comprises:
comparing and analyzing the first treatment concentration with a preset fluorine-containing concentration threshold value;
if the first treatment concentration is smaller than the fluorine-containing concentration threshold value, a secondary treatment instruction and a medicament increasing instruction are not generated;
if the first treatment concentration is greater than or equal to the fluorine concentration threshold value and the first treatment concentration divided by the fluorine concentration threshold value is less than or equal to R, generating a secondary treatment instruction, wherein R is a numerical value greater than 1;
if the first treatment concentration divided by the fluorine-containing concentration threshold is greater than R, a secondary treatment instruction and a reagent increase instruction are generated.
5. The method for treating fluorine-containing wastewater based on intelligent dosing according to claim 4, wherein the calculation formula of the reagent flow increment of the reaction tank is as follows:
；
In the middle ofFor increasing the flow of the reagent in the reaction tank, +.>For the water inlet end of the sedimentation tankFluorine concentration of wastewater of->Is a threshold value of fluorine concentration,>is the wastewater flow of the water inlet end of the sedimentation tank.
6. The method for treating fluorine-containing wastewater based on intelligent dosing according to claim 5, wherein the method for judging whether to generate the secondary agent increasing instruction comprises:
comparing and analyzing the second treatment concentration with a preset fluorine-containing concentration threshold value;
if the second treatment concentration is less than or equal to the fluorine-containing concentration threshold, a secondary medicament increasing instruction is not generated;
and if the second treatment concentration is greater than the fluorine-containing concentration threshold, generating a secondary medicament increasing instruction.
7. The method for treating fluorine-containing wastewater based on intelligent dosing according to claim 6, wherein the calculation formula of the reagent flow increment of the sedimentation tank is as follows:
；
in the middle ofFor increasing the flow of the medicament in the sedimentation tank, +.>The concentration of fluorine in the wastewater at the water outlet end of the sedimentation tank is +.>Is the wastewater flow of the water outlet end of the sedimentation tank.
8. The method for treating fluorine-containing wastewater based on intelligent dosing according to claim 7, wherein the continuously collected Q second treatment concentrations are input into a concentration prediction model for predicting the second treatment concentration at the future time; the training method of the concentration prediction model comprises the following steps:
Constructing a second treatment concentration set by using the Q continuously collected second treatment concentrations, and training a prediction model for predicting the second treatment concentration at the future moment based on the second treatment concentration set;
presetting a sliding step length L and a sliding window length; converting the second processing concentration in the second processing concentration set into a plurality of training samples by using a sliding window method, taking the training samples as input of a concentration prediction model, taking the second processing concentration after predicting the sliding step length L as output, taking the subsequent second processing concentration of each training sample as a prediction target, taking the prediction accuracy rate as a training target, and training the concentration prediction model; generating a concentration prediction of the second treatment concentration at a future time based on the second treatment concentration; the concentration prediction model is a cyclic neural network model.
9. The method for treating fluorine-containing wastewater based on intelligent dosing according to claim 8, wherein the image of the sedimentation tank is collected by a CCD camera, the image is subjected to gray scale treatment, and gray scale values of Y pixels are collected;
comparing and analyzing the gray values of the Y pixel points with a gray value threshold, if the gray value of the pixel point is larger than or equal to the gray value threshold, marking the pixel point as a precipitation point, and if the gray value of the pixel point is smaller than a difference value threshold, not marking the pixel point as the precipitation point;
Counting the number of precipitation points and calculating the precipitation area;
the sedimentation area calculation formula is as follows:
:
in the method, in the process of the invention,to precipitateArea (S)>For the number of sediment spots, +.>Is the area of a precipitation point, +.>Is a proportionality coefficient;
the area of one sedimentation point is obtained by the resolution ratio of an image, and the actual area of the sedimentation tank is divided by the image area of the sedimentation tank to be used as a proportionality coefficient;
calculating a second treatment concentration according to the precipitation area, and sending the second treatment concentration to a second concentration judging module;
the second treatment concentration calculation formula is as follows:
:
in the method, in the process of the invention,is a weight coefficient.
10. An intelligent dosing-based fluorine-containing wastewater treatment system realized based on the intelligent dosing-based fluorine-containing wastewater treatment method as claimed in any one of claims 1 to 9, characterized by comprising:
a first data acquisition module for acquiringGroup historical wastewater status data; the historical wastewater status data includes wastewater characteristic data and a medicament flow rate corresponding to the wastewater characteristic data;
the model training module is used for training a medicament selection model for predicting medicament flow based on the historical wastewater state data;
the area dividing module divides the fluorine-containing wastewater treatment equipment intoA plurality of regions;
the first reagent delivery module inputs the collected real-time wastewater characteristic data into a reagent selection model to obtain predicted reagent flow, and the predicted reagent flow is used as a control parameter of the reagent delivery equipment in the reaction tank;
The second data acquisition module acquires the wastewater characteristic data of the water inlet end of the sedimentation tank, and marks the fluorine concentration of wastewater in the wastewater characteristic data of the water inlet end of the sedimentation tank as first treatment concentration;
the first concentration judging module is used for analyzing the first treatment concentration and judging whether a secondary treatment instruction or a medicament adding instruction is generated or not;
the second reagent delivery module inputs the collected real-time wastewater characteristic data of the water inlet end of the sedimentation tank into a reagent selection model if a secondary treatment instruction is generated, so as to obtain predicted reagent flow, and the predicted reagent flow is used as a control parameter of the reagent delivery equipment in the sedimentation tank; if a reagent increasing instruction is generated, calculating reagent flow increasing amount of the reaction tank, and taking the reagent flow in the reaction tank plus the reagent flow increasing amount of the reaction tank as new reagent flow of the reaction tank;
the third data acquisition module is used for acquiring the fluorine concentration of the wastewater at the water outlet end of the sedimentation tank and marking the fluorine concentration of the wastewater at the water outlet end of the sedimentation tank as a second treatment concentration;
the second concentration judging module is used for analyzing the second treatment concentration and judging whether a medicament increasing instruction is generated or not;
and the medicament adding module is used for calculating the medicament flow increasing amount of the sedimentation tank if a secondary medicament adding instruction is generated, and adding the medicament flow in the sedimentation tank to the medicament flow increasing amount of the sedimentation tank to serve as new medicament flow of the sedimentation tank.
11. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the intelligent dosing-based fluorine-containing wastewater treatment method according to any of claims 1-9 when executing the computer program.
12. A computer readable storage medium, characterized in that the computer readable storage medium has stored thereon a computer program which, when executed, implements the intelligent dosing-based fluorine-containing wastewater treatment method according to any of claims 1-9.
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