CN114878772B - Detection early warning monitoring system applied to sewage treatment - Google Patents
Detection early warning monitoring system applied to sewage treatment Download PDFInfo
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Abstract
The invention provides a detection early warning monitoring system applied to sewage treatment, which comprises: the device comprises an acquisition module, a processing module, a prediction module and an alarm module; the acquisition module is used for acquiring the original data of the sewage in real time; the processing module is used for processing the real-time acquired original data and calculating pollutants; the prediction module is used for predicting the future trend of sewage treatment according to the processed original data and the pollutant calculation result; the alarm module is used for carrying out prevention alarm according to the sewage treatment trend prediction result. The invention monitors the related data of the pollutants in the sewage to be detected in real time, calculates the discharge amount of each pollutant in real time, and carries out intelligent and real-time future trend prediction on the development trend of each data and the sewage treatment trend, thereby really realizing the real-time calculation and intelligent prediction early warning on the development trend of the sewage treatment.
Description
Technical Field
The invention belongs to the technical field of sewage treatment, and particularly relates to a detection, early warning and monitoring system applied to sewage treatment.
Background
The existing sewage treatment system cannot predict the water quality condition, can only judge when the water quality can exceed the standard according to the working experience of an operation and maintenance person, and does some treatment in advance, for example, in a biochemical section of a sewage treatment process, ammonia nitrogen can exceed the standard possibly due to insufficient aeration, and when the operation and maintenance person finds that the ammonia nitrogen index is about to exceed the standard at a certain time according to the working experience of the operation and maintenance person, the explosion value in the sewage is increased by increasing the frequency of an air blower in advance or starting a standby air blower.
Therefore, the current treatment method for exceeding the water quality has the following problems:
1. when the water quality exceeds the standard, a compensation measure is taken, so that a part of sewage which does not reach the standard is discharged, and the environment pollution is caused;
2. the pretreatment is carried out in advance by the working experience of the operator, the requirement on operation and maintenance personnel is high, the operator needs to stare at each operation data in real time, the operation and maintenance cost is high, the empirical judgment is sometimes inaccurate, and the condition of misjudgment can also exist.
Therefore, in order to solve the problems, the invention provides a detection, early warning and monitoring system applied to sewage treatment.
Disclosure of Invention
In order to solve the technical problems, the invention provides a detection, early warning and monitoring system applied to sewage treatment, which monitors relevant data of pollutants in sewage to be detected in real time, calculates the discharge amount of each pollutant in real time, and carries out intelligent and real-time future trend prediction on the development trend of each data and the sewage treatment trend, thereby really realizing real-time calculation and intelligent prediction and early warning on the development trend of sewage treatment.
In order to achieve the above object, the present invention provides a detection, early warning and monitoring system for sewage treatment, comprising: the device comprises an acquisition module, a processing module, a prediction module and an alarm module;
the acquisition module is used for acquiring the original data of the sewage in real time;
the processing module is used for processing the original data acquired in real time and calculating pollutants;
the prediction module is used for predicting the future trend of sewage treatment according to the processed original data and the pollutant calculation result;
and the alarm module is used for performing prevention alarm according to the sewage treatment trend prediction result.
Optionally, the raw data comprises: suspended solid contaminants, colloidal and dissolved organic contaminants, refractory organics, soluble inorganic species that contribute to eutrophication of the water body.
Optionally, the processing module includes: a cleaning unit and a calculating unit;
the cleaning unit is used for cleaning the original data;
and the computing unit is used for computing pollutants according to the cleaned original data.
Optionally, the manner of cleaning the raw data includes: removing repeated items and abnormal values, supplementing missing values by using a linear interpolation method, and uniformly processing the time resolution of data.
Optionally, the calculation unit obtains a pollutant calculation result through three-level processing;
the calculation unit obtains a calculation result of the suspended solid pollutants through primary treatment, wherein the suspended solid pollutants are = (A-B). Times.1000X 1000/V, in the formula, A is the suspended solid + the weight of the filter membrane and the weight of the weighing bottle, B is the weight of the filter membrane and the weight of the weighing bottle, and V is the volume of a water sample;
the calculation unit obtains the calculation result of the colloid and the dissolved organic pollutants through secondary treatment, and the COD volume load of the aeration tank is L vCOD ＝Q·C i V, BOD volume of aeration tankLoaded with
Wherein Q is sewage flow, V is aeration tank volume, and C i Is the chemical dissolved oxygen concentration in the aeration tank, B i The concentration of the biological dissolved oxygen in the aeration tank;
the calculation unit obtains the calculation results of refractory organic matters and soluble inorganic matters causing water eutrophication through three-stage treatment, wherein N = V G/Y, N is the dosage of a nitrogen source, V is the water quantity in the pool, G-needs to supplement the N difference value, and Y-N is converted into N quantity; p = V G/Z, P is the phosphorus source adding amount, V is the water amount in the pool, G is the difference value of P needing to be supplemented, and Z is the P amount converted by P.
Optionally, the prediction module comprises: the system comprises a model building unit, a database and a trend prediction unit;
the model building unit is used for building an LSTM neural network;
the database is used for storing the cleaned original data;
the trend prediction unit is used for training the LSTM neural network according to the cleaned original data and predicting the future trend of sewage treatment based on the trained LSTM neural network.
Optionally, the LSTM neural network comprises: an input layer, a hidden layer and an output layer;
the input layer is used for inputting the cleaned original data and sequencing the original data according to a time sequence;
the hidden layer is used for iteratively learning short-range and long-range semantic features of the time sequence data;
the output layer is used for outputting a prediction result;
the LSTM neural network further comprises LSTM network parameters;
the LSTM network parameters comprise a learning rate, iteration times and stepsize.
Optionally, the trend prediction model comprises: a suspended solid pollutant trend prediction model, an organic pollutant trend prediction model of colloid and dissolved state, a refractory organic matter and soluble inorganic matter trend prediction model causing water eutrophication, and a sewage treatment trend prediction model;
the trend prediction model is used for predicting the future trend and comprises the following steps: and carrying out short-term trend prediction, medium-term trend prediction and long-term trend prediction.
Optionally, the alarm module comprises: the system comprises a suspended solid pollutant alarm module, a colloid and dissolved organic pollutant alarm module, a refractory organic matter and soluble inorganic matter alarm module causing water eutrophication, and a sewage treatment alarm module.
Optionally, after each type of the trend prediction model predicts a future trend, the prediction result is transmitted to each type of corresponding alarm module in the alarm modules in real time, and if the prediction result has a deviation from a preset threshold value, the corresponding alarm module alarms;
after any one of the alarm modules of the suspended solid pollutant alarm module, the colloid and dissolved organic pollutant alarm module, the nondegradable organic matter alarm module and the soluble inorganic matter alarm module causing water eutrophication gives an alarm, the sewage treatment alarm module also gives an alarm at the same time; the more the number of the alarm modules for alarming is, the higher the response stage number of the sewage treatment alarm module for alarming is.
Compared with the prior art, the invention has the following advantages and technical effects:
the invention takes various kinds of raw data collected in real time in a period of time as training data to train a network model in a trend prediction module, and carries out future development trend prediction on various kinds of real-time raw data through the trained network model, thereby predicting the time point when the water quality index exceeds the standard in advance according to the water quality index limit value to alarm so as to take adjustment measures in advance, and a worker can systematically check the running condition of the index in advance and take adjustment in advance, such as adjustment of dosage, adjustment of an aeration system and the like, and does not need to take adjustment after the water quality index exceeds the standard, thereby avoiding the condition that part of over-standard sewage is discharged to cause environmental pollution, and providing data analysis for the subsequent improvement of a sewage treatment flow.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate an embodiment of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic structural diagram of a detection, early warning and monitoring system applied to sewage treatment according to an embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than presented herein.
As shown in fig. 1, the present embodiment provides a detection, early warning and monitoring system for sewage treatment, which includes: the device comprises an acquisition module, a processing module, a prediction module and an alarm module;
the acquisition module is used for acquiring the original data of the sewage in real time;
the processing module is used for processing the real-time acquired original data and calculating pollutants;
the prediction module is used for predicting the future trend of sewage treatment according to the processed original data and the pollutant calculation result;
the alarm module is used for carrying out prevention alarm according to the sewage treatment trend prediction result.
Further, the processing module includes: a cleaning unit and a calculating unit;
the cleaning unit is used for cleaning the original data;
and the calculation unit is used for calculating the pollutants according to the cleaned original data.
Further, the raw data includes: suspended solid contaminants, colloidal and dissolved organic contaminants, refractory organics, soluble inorganic species that contribute to eutrophication of the water body.
Further, the method for cleaning the raw data comprises the following steps: removing repeated items and abnormal values, supplementing missing values by using a linear interpolation method, and uniformly processing the time resolution of data.
Further, the calculation unit obtains a pollutant calculation result through three-stage treatment;
the calculation unit obtains a calculation result of the suspended solid pollutants through primary treatment, wherein the suspended solid pollutants are = (A-B). Times.1000X 1000/V, in the formula, A is the suspended solid + the weight of the filter membrane and the weight of the weighing bottle, B is the weight of the filter membrane and the weight of the weighing bottle, and V is the volume of the water sample;
the calculation unit obtains the calculation result of the colloid and the dissolved organic pollutants through secondary treatment, and the COD volume load of the aeration tank is L vCOD ＝Q·C i V, BOD volume load of aeration tank is
Wherein Q is sewage flow, V is aeration tank volume, and C i Is the chemical dissolved oxygen concentration in the aeration tank, B i The concentration of the biological dissolved oxygen in the aeration tank;
the calculation unit obtains the calculation results of refractory organic matters and soluble inorganic matters causing water eutrophication through three-stage treatment, wherein N = V G/Y, N is the dosage of the nitrogen source, V is the water quantity in the pool, G-needs to be supplemented with the difference of N, and Y-N is converted into N; p = V G/Z, P is the phosphorus source adding amount, V is the water amount in the pool, G is the difference value of P needing to be supplemented, and Z is the P amount converted by P.
The raw data of the contaminated water in this embodiment comprises at least one of: suspended solid pollutants, colloid and dissolved organic pollutants, organic matters difficult to degrade and soluble inorganic matters causing eutrophication of the water body.
Wherein obtaining a calculation of suspended solid contaminants is performed by:
(1) and drying the mixture in a drying oven at 103-105 ℃ for 0.5h by using a weighing bottle.
(2) Washing the filter paper twice with ammonia-free water, placing the filter paper in a weighing bottle, opening a bottle cap, drying the filter paper in an oven at 103-105 ℃ for 0.5h, taking out the filter paper, cooling the filter paper in a drier, covering the filter paper with the bottle cap, weighing the filter paper until the filter paper is constant in weight (the difference between the two times of weighing is not more than 0.0002 g)
(3) Measuring 100m1 of the uniformly mixed water sample, and enabling the water sample to pass through the filter paper weighed to be constant in weight (removing the filtrate to prepare soluble total nitrogen, total phosphorus and nitrate nitrogen); the residue was washed 3 times with 10m1 distilled water.
(4) Carefully taking down the filter paper, putting the filter paper into an original weighing bottle, opening a bottle cap to dry for 1h in a drying oven at 103-105 ℃, transferring the bottle cap into a dryer to cool, covering the bottle cap, and weighing. And repeatedly drying, cooling and weighing until the weighing difference is less than or equal to 0.4 mg.
The index of suspended particles in the effluent of sewage is not more than 20mg/L.
Among colloidal and dissolved organic contaminants, the main indicators of organic contamination are: (1) domestic sewage: COD = 400-500 mg/l, BOD 5 = 200-300 mg/l; (2) industrial waste water: the method mainly comprises the industries of petrochemical industry, light industry, food and the like, such as: beer wastewater: 8 to 20m 3 Waste water/m 3 Wine, COD = 2000-3500 mg/l; alcohol wastewater: 12 to 15m 3 Wastewater/m 3 Wine, COD = 3-6 ten thousand mg/l: monosodium glutamate wastewater: 25 to 35m 3 Waste water/ton of monosodium glutamate, COD = 6-10 ten thousand mg/l: black liquor of paper making: 120 to 600m 3 Waste water/ton pulp, COD = 10-15 ten thousand mg/l
In refractory organic matters and soluble inorganic matters causing water eutrophication, the nitrogen source adding calculation mode is as follows: materials: urea, sodium nitrate, potassium nitrate and the like, wherein the N content of the urea is 46.7 percent, the addition of 1gN requires the addition of Y =1/0.467=2.1g of urea, the N content of the sodium nitrate is 16.5 percent, and the addition of 1gN requires the addition of Y =1/0.165=6.06g of sodium nitrate; the calculation mode of adding phosphate is as follows: materials: potassium dihydrogen phosphate and phosphate fertilizer, wherein the p content of the potassium dihydrogen phosphate is 13.6 percent, 1g of P source is added, and the potassium dihydrogen phosphate Z =1/0.163=7.35g needs to be added.
The cleaned original data is substituted into three-stage treatment, the quality of the sewage to be detected can be checked in real time or near real time, a sewage treatment trend prediction result is generated, and the problems that the current sewage treatment data set has time lag of one year or more and does not have day-level or hour-level sewage treatment data are solved.
Further, LSTM (Long Short-Term Memory network) is a time-cycle neural network, and is specially designed to solve the problems of gradient extinction and gradient explosion existing in RNN. Compared with RNN, LSTM has unique design structure, which adds input gate, output gate and forgetting gate (three gates can pass information selection mode) in hidden layer, and uses memory state unit to store and process long time sequence information, wherein the memory gate is used to select forgetting some information, the input gate is used to memorize some information, the information is merged with the memory through the input gate and the memory gate, and the output gate outputs the information finally. LSTM is therefore well suited to handle and predict significant events of very long intervals and delays in time series.
In this embodiment, the prediction module includes: the system comprises a model building unit, a database and a trend prediction unit;
the model building unit is used for building an LSTM neural network;
the database is used for storing the cleaned original data;
the trend prediction unit is used for training the LSTM neural network according to the cleaned original data and predicting the future trend of sewage treatment based on the trained LSTM neural network.
The trend prediction unit can predict the future trend of the environmental parameters in a short term, a medium term or a long term, wherein the short term refers to 4-48h, the medium term refers to 2-7 days, and the long term refers to 7-15 days. The intermediate-term and long-term future tendency predictions are not limited to the above-described large and small intervals, but may be set to have a larger interval value than the above-described interval, but the longer the predicted future time is, the larger the corresponding error is.
The LSTM neural network comprises an input layer, a hidden layer and an output layer, wherein the input layer inputs training data; the hidden layer iteratively learns short-range and long-range semantic features of the time sequence data; the output layer outputs the prediction result. The LSTM network parameters comprise learning rate, iteration times, stepsize and the like, wherein the key network parameter stepsize takes a value between 1 and 24, and the specific value is determined according to the scale of the environmental parameter training data and the actual conditions and requirements.
The database comprises collected cleaned original data within a period of time, the cleaned original data are used as training data to train a trend prediction model, each type of training data in the database needs to be labeled, and the labeling processing mode meets the following requirements:
when stepsize =1, the labeling processing mode is to use training data at the nth + x moment as a label of the training data at the nth moment; when stepsize =2, the labeling processing mode is to use training data at the nth + x moment as labels of training data at the nth and the n-1 moments; when stepsize =3, the labeling processing mode is to use training data at the n + x th time as labels of environmental training data at the n, n-1 and n-2 th times, and the rest is analogized in turn, where x is a prediction step size parameter and takes any integer greater than or equal to 0.
The value of the prediction step length parameter x is related to short-term and medium-term prediction, if the prediction is performed in a short term, the value of x is smaller, if the prediction is performed in a long term, the value of x is larger, and if the prediction is performed in a medium term, the value of x is between the short-term and medium-term. By adjusting the prediction step size parameter x, the development trend of the prediction sewage treatment parameter data corresponding to the near or far future time can be obtained. For example, when the value of x is 3 (the time span between the nth and the (n + 1) th moments is set to be 4 hours), the trend prediction model can obtain the development trend of the predicted sewage treatment parameter data after 12 hours; when the value of x is 24, the trend prediction model can obtain the development trend of the predicted sewage treatment parameter data after 96 hours (namely after 4 days); when the value of x is 72, the trend prediction model can obtain the development trend of the predicted sewage treatment parameter data after 12 days.
And each class of training data in the training database is arranged according to a time sequence, wherein the training data at the nth time refers to the mean value of the training data in a certain time period, but not the training data value at a time point. Moreover, the time periods corresponding to the training data at all the moments have the same and uniform interval size. In addition, the time span between the nth and the (n + 1) th time (where n is an arbitrary integer of 0 or more) is preferably 4 or 6 or 8 hours, and the maximum time does not exceed 48 hours.
The trend prediction model comprises: a suspended solid pollutant trend prediction model, an organic pollutant trend prediction model of colloid and dissolved state, a refractory organic matter and soluble inorganic matter trend prediction model causing water eutrophication, and a sewage treatment trend prediction model; each type of trend prediction model is obtained by utilizing an LSTM network to perform training learning based on corresponding training data in a database, and the future trend prediction is performed on corresponding real-time original data.
The training process of the LSTM neural network is as follows:
the LSTM neural network carries out iterative training round by round, in each round of training process, training data at the n, n-1, … … and n-stepsize +1 moments are used as input data, predicted sewage treatment parameter data aiming at the n + x moment are output, then, predicted environment parameter data at the n + x moment and actual sewage treatment parameter data at the n + x moment are matched, if matching errors do not meet preset requirements, correction and adjustment are carried out on weight parameters of each neural unit of the neural network according to matching errors, then training data at the n, n-1, … … and n-stepsize +1 moments are continuously used as input data, next round of iterative training is started until the matching errors between the predicted sewage treatment parameter data at the n + x moment and the actual sewage treatment parameter data at the n + x moment are smaller than a specified threshold value, and then the neural network training is finished.
The alarm module includes: the system comprises a suspended solid pollutant alarm module, a colloid and dissolved organic pollutant alarm module, a nondegradable organic matter and soluble inorganic matter alarm module causing eutrophication of a water body, and a sewage treatment alarm module; the alarm modes are different among different sub-modules.
After each type of the trend prediction model predicts the future trend, the prediction result is transmitted to each type of corresponding alarm module in the alarm modules in real time, and if the prediction result has deviation with a preset threshold value, the corresponding alarm module gives an alarm;
after any one of the alarm modules of the alarm module for suspended solid pollutants, the alarm module for colloid and dissolved organic pollutants, the alarm module for refractory organics and the alarm module for soluble inorganics causing water eutrophication gives an alarm, the alarm module for sewage treatment also gives an alarm at the same time; the more the number of the alarm modules for alarming is, the higher the response stage number of the sewage treatment alarm module for alarming is.
The invention trains the network model in the trend prediction module by using various kinds of raw data collected in real time within a period of time as training data, and predicts the future development trend of various kinds of real-time raw data by using the trained network model, so that the time point of exceeding the standard of the water quality index is predicted in advance according to the water quality index limit value, thereby facilitating the adjustment measures to be made in advance, and workers can check the operation condition of the index in advance in a planned way and make adjustments in advance, such as adjustment of dosage, adjustment of an aeration system and the like, and the adjustment is not needed after the standard of the water quality index exceeds the standard, thereby avoiding the occurrence of the condition of environmental pollution caused by partial exceeding-standard sewage discharge, and providing data analysis for the subsequent improvement of a sewage treatment process.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are also included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (4)
1. The utility model provides a be applied to sewage treatment's detection early warning monitoring system which characterized in that includes:
the device comprises an acquisition module, a processing module, a prediction module and an alarm module;
the acquisition module is used for acquiring the original data of the sewage in real time;
the processing module is used for processing the original data acquired in real time and calculating pollutants;
the processing module comprises: a cleaning unit and a calculating unit;
the cleaning unit is used for cleaning the original data;
the computing unit is used for computing pollutants according to the cleaned original data;
the prediction module is used for predicting the future trend of sewage treatment according to the processed original data and the pollutant calculation result;
the alarm module is used for carrying out prevention alarm according to the prediction result of the sewage treatment trend;
the raw data includes: suspended solid pollutants, colloid and dissolved organic pollutants, refractory organic matters and soluble inorganic matters causing water eutrophication;
the calculation unit obtains a pollutant calculation result through three-level treatment;
the calculation unit obtains a calculation result of the suspended solid pollutants through primary treatment, wherein the suspended solid pollutants are = (A-B). Times.1000X 1000/V, in the formula, A is the suspended solid + the weight of the filter membrane and the weight of the weighing bottle, B is the weight of the filter membrane and the weight of the weighing bottle, and V is the volume of a water sample;
the calculation unit obtains the calculation result of colloid and dissolved organic pollutants through secondary treatment, and the COD volume load of the aeration tank is
The BOD volume load of the aeration tank is
In the formula, Q is sewage flow, V is aeration tank volume,
is the chemical dissolved oxygen concentration in the aeration tank,
the concentration of the biological dissolved oxygen in the aeration tank;
the calculation unit obtains the calculation results of refractory organic matters and soluble inorganic matters causing water eutrophication through three-stage treatment, wherein N = V G/Y, N is the dosage of a nitrogen source, V is the water quantity in the pool, G-needs to supplement the difference of N, and Y-is converted into N; p = V G/Z, P is the phosphorus source adding amount, V is the water amount in the pool, G is the difference value of P needing to be supplemented, and Z is the converted P amount;
the prediction module comprises: the system comprises a model construction unit, a database and a trend prediction unit;
the model building unit is used for building an LSTM neural network;
the database is used for storing the cleaned original data;
the database comprises collected cleaned original data in a period of time, and the cleaned original data is used as training data to train the trend prediction unit;
the trend prediction unit is used for training the LSTM neural network according to the cleaned original data and predicting the future trend of sewage treatment based on the trained LSTM neural network;
the LSTM neural network comprises: an input layer, a hidden layer and an output layer;
the input layer is used for inputting the cleaned original data and sequencing the original data according to a time sequence;
the hidden layer is used for iteratively learning short-range and long-range semantic features of the time sequence data;
the output layer is used for outputting a prediction result;
the LSTM neural network further comprises LSTM network parameters;
the LSTM network parameters comprise learning rate, iteration times and stepsize;
the trend prediction unit includes: a suspended solid pollutant trend prediction model, an organic pollutant trend prediction model of colloid and dissolved state, a refractory organic matter and soluble inorganic matter trend prediction model causing water eutrophication, and a sewage treatment trend prediction model;
the trend prediction unit is used for predicting the future trend and comprises the following steps: and carrying out short-term trend prediction, medium-term trend prediction and long-term trend prediction.
2. The detection, early warning and monitoring system applied to sewage treatment according to claim 1, wherein the manner of cleaning the raw data comprises: removing repeated items and abnormal values, supplementing missing values by using a linear interpolation method, and uniformly processing the time resolution of data.
3. The detection, early warning and monitoring system applied to sewage treatment as claimed in claim 1, wherein the alarm module comprises: the system comprises a suspended solid pollutant alarm module, a colloid and dissolved organic pollutant alarm module, a refractory organic matter and soluble inorganic matter alarm module causing water eutrophication, and a sewage treatment alarm module.
4. The detection, early warning and monitoring system applied to sewage treatment of claim 3, wherein after each type of trend prediction model predicts the future trend, the prediction result is transmitted to each corresponding type of alarm module in the alarm modules in real time, and if the prediction result has a deviation from a preset threshold value, the corresponding alarm module gives an alarm;
after any one of the alarm modules of the suspended solid pollutant alarm module, the colloid and dissolved organic pollutant alarm module, the nondegradable organic matter alarm module and the soluble inorganic matter alarm module causing water eutrophication gives an alarm, the sewage treatment alarm module also gives an alarm at the same time; the more the number of the alarm modules for alarming is, the higher the response stage number of the sewage treatment alarm module for alarming is.
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