CN112794550B - Method and system for solving COD standard exceeding of effluent of sewage treatment plant based on artificial intelligence - Google Patents
Method and system for solving COD standard exceeding of effluent of sewage treatment plant based on artificial intelligence Download PDFInfo
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Abstract
The invention discloses a method and a system for solving the problem that COD (chemical oxygen demand) of effluent of a sewage treatment plant exceeds standard based on artificial intelligence, wherein a training sample for model training is established according to measured values of the COD of the effluent, the COD of the influent, the influent flow, the reflux ratio of mixed liquid, the reflux ratio of sludge and dissolved oxygen in normal standard-reaching operation data; establishing a mathematical model and carrying out model training; obtaining a test sample, selecting test data of the effluent COD in the test sample reaching the standard, calling a trained mathematical model, and judging the fitting property of the mathematical model; selecting test data of the effluent COD in the test sample which does not reach the standard, calling the trained mathematical model, and matching with an artificial intelligent control system to automatically regulate and control the inflow, the reflux ratio of the mixed liquid, the reflux ratio of the sludge and the dissolved oxygen so as to enable the effluent COD of the sludge treatment plant to reach the standard, so as to establish a functional relation among various parameters by monitoring various parameters, establish the model, and rapidly recover the effluent COD to reach the standard by judging and adjusting the method by using the artificial intelligent model when the effluent COD exceeds the standard.
Description
Technical Field
The invention belongs to the technical field of sewage treatment, and particularly relates to a method and a system for solving the problem that COD (chemical oxygen demand) of effluent of a sewage treatment plant exceeds standard based on artificial intelligence.
Background
The effluent standard of a sewage treatment plant is class A of pollutant discharge Standard of urban Sewage treatment plant (GB 18918-2002), and when the COD of the effluent exceeds the standard, parameters influencing the COD of the effluent need to be controlled in time so as to ensure that the COD of the effluent reaches the standard.
However, the sewage treatment process has the characteristics of high nonlinearity, time-varying property, uncertainty, strong coupling property and the like, and in the operation of the traditional sewage treatment plant, when the COD of the effluent of the sewage treatment plant exceeds the standard, the COD of the effluent is difficult to recover to normal within a short time by controlling and adjusting a single parameter.
Disclosure of Invention
In view of the above, in order to solve the above problems in the prior art, the present invention aims to provide a method and a system for solving the problem of the exceeding of the COD of the effluent of the sewage treatment plant based on artificial intelligence, so as to achieve the purpose of rapidly recovering the COD of the effluent to reach the standard by monitoring the parameters of the COD of the effluent of the sewage treatment plant, the COD of the effluent, the inflow water, the dissolved oxygen, the reflux ratio of the mixed liquid, the reflux ratio of the sludge, and the like, establishing a functional relationship among the parameters, thereby establishing a model, and intelligently judging and adjusting the method by using the artificial intelligence model when the COD of the effluent exceeds the standard.
The technical scheme adopted by the invention is as follows: a method for solving the problem that COD (chemical oxygen demand) of effluent of a sewage treatment plant exceeds standard based on artificial intelligence comprises the following steps:
s1: establishing a training sample for model training according to measured values of water outlet COD, water inlet flow, mixed liquid reflux ratio, sludge reflux ratio and dissolved oxygen in normal standard-reaching operation data;
s2: establishing a mathematical model and carrying out model training;
s3: obtaining a test sample, selecting test data of effluent COD (chemical oxygen demand) in the test sample reaching the standard, calling a trained mathematical model, judging the fitting property of the mathematical model, and entering the next step if the fitting property is good; if the fitting performance is not good, returning to S2;
s4: selecting test data of the effluent COD in the test sample which do not reach the standard, calling the trained mathematical model, and calculating the control quantity of the inflow, the reflux ratio of the mixed liquid, the reflux ratio of the sludge and the dissolved oxygen through the mathematical model so as to meet the requirement that the predicted value of the effluent COD reaches the standard.
Further, the method further comprises:
s5: the control quantity of the water inlet flow, the mixed liquid reflux ratio, the sludge reflux ratio and the dissolved oxygen is calculated according to the mathematical model, and the water inlet flow, the mixed liquid reflux ratio, the sludge reflux ratio and the dissolved oxygen are automatically regulated and controlled through the artificial intelligent control system, so that the effluent COD of the sludge treatment plant reaches the standard.
Further, the mathematical model comprises an input layer, a hidden layer and an output layer, wherein the input data of the input layer are measured values of water inflow COD, water inflow, mixed liquid reflux ratio, sludge reflux ratio and dissolved oxygen, and are x respectively 1 、x 2 、x 3 、x 4 、x 5 And the input data are training samples: test sample =6:1; the output data of the output layer is an effluent COD predicted value and is setThe COD predicted value of the effluent is y i And the measured value of COD of the effluent is y 0 。
Further, the method for training the mathematical model in step S1 includes:
s201: assigning a weight ω to input data for each training sample i Aggregating inputs into
Wherein i is a natural number;
s202: introducing a deviation b, a threshold value theta, and defining f (x) =1
f(x)＝0
If f (x) =1, the COD of the effluent reaches the standard; if f (x) =0, the COD of the effluent exceeds the standard;
s203: introducing Sigmoid function
And defining the output function as
S204: introducing least squares functions
Let the initial deviation and the random value be (ω) 0 ，b 0 ) By an iterative method, calculate a gradient ^ E (ω) 0 ，b 0 ) And define
Wherein j is a natural number, and epsilon is a learning rate;
s205: through learning training, the weight omega is adjusted i The deviation b and the threshold θ are optimally chosen such that f (x) =1 and E is less than the set value, then the current mathematical model is the trained model.
Further, in the step S3, half of the data in the test sample is the data with standard COD, and the other half of the data is the data with standard COD.
Further, the mixed liquor reflux ratio is calculated by the ratio of the mixed liquor reflux amount to the water inlet flow, and the sludge reflux ratio is calculated by the ratio of the sludge reflux amount to the water inlet flow.
Further, in step S5, the artificial intelligence control system regulates and controls the water inflow rate through the rotation speed of the water inflow lift pump or the water inflow electric valve; the artificial intelligence control system regulates and controls the reflux ratio of the mixed liquid through the rotating speed of the mixed liquid reflux pump or the electric valve of the mixed liquid reflux; the artificial intelligent control system regulates and controls the sludge reflux ratio through the rotating speed of the sludge reflux pump or the sludge reflux electric valve; the artificial intelligence control system regulates and controls the content of the dissolved oxygen through the rotating speed of the blower or the electric gas transmission valve.
The invention also provides a system for solving the COD standard exceeding of the effluent of the sewage treatment plant based on artificial intelligence, which comprises the following components:
the system comprises a plurality of measuring points, a data acquisition unit and a data processing unit, wherein each measuring point respectively measures the operation data of a sewage treatment plant in real time;
the system comprises a plurality of control points, a plurality of control units and a control unit, wherein each control point respectively regulates and controls the operation data of a sewage treatment plant in real time;
the input data acquisition module is used for acquiring the operation data of each measuring point through the input data module so as to acquire a training sample and a test sample of the sewage treatment plant;
the data analysis module is used for carrying out fitting judgment on the test data of the effluent COD in the test sample which reaches the standard; calculating the test data of the effluent COD in the test sample which does not reach the standard through a data analysis module to obtain the control quantity of each control point;
and the artificial intelligence control module is used for respectively regulating and controlling each control point through the artificial intelligence control module according to each control quantity so as to enable the effluent COD of the sewage treatment plant to reach the standard.
Furthermore, each measuring point is respectively a water inlet pipe, a water outlet pipe, a mixed liquid return pipeline, a sludge return pipeline and an aerobic pool which are positioned in a sludge treatment plant, the water inlet pipeline is provided with a first COD online monitor and a water inlet electromagnetic flowmeter, the water outlet pipeline is provided with a second COD online monitor, the mixed liquid return pipeline is provided with a mixed liquid return electromagnetic flowmeter, the sludge return pipeline is provided with a sludge return electromagnetic flowmeter, and the aerobic pool is provided with an oxygen dissolving instrument;
first COD on-line monitoring appearance, second COD on-line monitoring appearance, water inlet electromagnetic flowmeter, mixed liquid backward flow electromagnetic flowmeter, mud backward flow electromagnetic flowmeter, dissolved oxygen appearance respectively with input data acquisition module communication connection.
Furthermore, each control point is respectively a water inlet electric valve and a water inlet lifting pump which are arranged on the water inlet pipeline, a mixed liquid backflow electric valve and a mixed liquid backflow pump which are arranged on the mixed liquid backflow pipeline, a sludge backflow electric valve and a sludge backflow pump which are arranged on the sludge backflow pipeline, and a gas transmission electric valve and a blower which are arranged on the air pipe of the aerobic tank, and the water inlet electric valve, the water inlet lifting pump, the mixed liquid backflow electric valve, the mixed liquid backflow pump, the sludge backflow electric valve, the sludge backflow pump, the gas transmission electric valve and the blower are respectively in communication connection with the artificial intelligence control module.
The invention has the beneficial effects that:
1. by adopting the method and the system for solving the problem that the COD of the effluent of the sewage treatment plant exceeds the standard based on artificial intelligence, when the COD of the effluent of the sewage treatment plant exceeds the standard, the optimal mathematical model for training is utilized to carry out real-time analysis, the operation data such as the water inflow, the dissolved oxygen, the mixed liquid reflux ratio, the sludge reflux ratio and the like are jointly controlled, the relevance among the data is found, the fastest and stable standard-reaching control quantity of the COD of the effluent is found out by replacing the artificial experience, the operation data is debugged according to the control quantity, the defect that the parameters such as the water inflow, the mixed liquid reflux ratio, the sludge reflux ratio and the dissolved oxygen cannot be dynamically processed is overcome, the optimal processing method for rapidly finding out the COD of the effluent when the COD exceeds the standard in the operation process of the sewage treatment plant is realized, and the multi-parameter comprehensive control is further carried out.
Drawings
FIG. 1 is a schematic flow chart of a method for solving the COD standard exceeding of effluent water of a sewage treatment plant based on artificial intelligence, which is provided by the invention;
FIG. 2 is a neural network diagram of a mathematical model in the method for solving the COD standard exceeding of the effluent of a sewage treatment plant based on artificial intelligence provided by the invention;
FIG. 3 is a comparison graph between the effluent COD of the compliance data and the prediction result in the test sample by the method for solving the effluent COD superscript of the sewage treatment plant based on artificial intelligence provided by the invention.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar modules or modules having the same or similar functionality throughout. The embodiments described below with reference to the accompanying drawings are illustrative and are only for the purpose of explaining the present application and are not to be construed as limiting the present application. On the contrary, the embodiments of the application include all changes, modifications and equivalents coming within the spirit and terms of the claims appended hereto.
Example 1
In this embodiment, a method for solving the problem that the COD of the effluent of the sewage treatment plant exceeds the standard based on artificial intelligence is specifically provided, and the method aims to establish a model through the COD of the effluent, the COD of the influent, the influent flow, the reflux ratio of mixed liquid, the reflux ratio of sludge and dissolved oxygen, and in the operation process of the sewage treatment plant, when the COD of the effluent exceeds the standard, an optimal treatment method is quickly found and multi-parameter comprehensive control is realized, as shown in fig. 1, the method comprises the following steps:
s1: according to measured values of water outlet COD, water inlet flow, mixed liquid reflux ratio, sludge reflux ratio and dissolved oxygen in normal standard-reaching operation data, establishing a training sample for model training, wherein the training sample is mainly used for training a mathematical model;
the system comprises a water inlet pipeline, a water outlet pipeline, a first COD (chemical oxygen demand) online monitor, a second COD online monitor, a first COD monitor and a second COD monitor, wherein the water inlet COD and the water outlet COD are measured and obtained by respectively arranging the first COD online monitor and the second COD online monitor on the water inlet pipeline and the water outlet pipeline; the water inlet flow is obtained by installing a water inlet electromagnetic flowmeter on a water inlet pipeline for measurement; the mixed liquid reflux ratio is calculated according to the ratio of the mixed liquid reflux amount to the water inlet flow, the mixed liquid reflux amount is obtained by installing a mixed liquid reflux electromagnetic flowmeter on a mixed reflux pipeline for measurement, the mixed liquid reflux ratio = the mixed liquid flow/the water inlet flow, and the system automatically calculates the mixed liquid reflux ratio; the sludge reflux ratio is calculated by the ratio of the sludge reflux amount to the water inflow amount, the sludge reflux amount is obtained by measuring a sludge reflux electromagnetic flowmeter arranged on a sludge reflux pipeline, and the system automatically calculates the sludge reflux ratio = the sludge reflux amount/the water inflow amount; the measured value of the dissolved oxygen is obtained by installing an oxygen dissolving instrument in the aerobic tank for real-time measurement.
S2: establishing a mathematical model and performing model training; as shown in fig. 2, the mathematical model includes an input layer, a hidden layer and an output layer, the input data of the input layer are the measured values of the influent COD, the influent flow rate, the reflux ratio of the mixed liquid, the reflux ratio of the sludge and the dissolved oxygen, and are x respectively 1 、x 2 、x 3 、x 4 、x 5 And the input data are training samples: test sample =6:1; the output data of the output layer is an effluent COD predicted value, and the effluent COD predicted value is y i And the measured value of COD of the effluent is y 0 。
According to first COD on-line monitoring appearance, second COD on-line monitoring appearance, dissolved oxygen appearance, mixed liquid backward flow electromagnetic flowmeter, mud backward flow electromagnetic flowmeter, the electromagnetic flowmeter of intaking respectively record normal water COD of going out when reaching standard, the COD of intaking, the flow of intaking, dissolved oxygen, mixed liquid reflux ratio, mud reflux ratio to this training sample is established. In this embodiment, the values of the training samples and the test samples are as follows:
the model training method mainly comprises the following steps:
s201: assigning a weight ω to input data for each training sample i The inputs are aggregated as:
wherein i is a natural number; substituting into the first set of data
The rest groups are analogized;
s202: introducing a deviation b, a threshold value theta, and defining f (x) =1
f(x)＝0
If f (x) =1, the COD of the effluent reaches the standard; if f (x) =0, the COD of the effluent exceeds the standard;
s203: introducing Sigmoid function
And defining the output function as
Substituting into the first set of data y i ＝σ(220ω 1 +1214ω 2 +200％ω 3 +80％ω 4 +2.22ω 5 + b), the rest groups and so on;
s204: introducing least squares functions
Substituting into the first set of data
The rest groups are analogized;
let the initial deviation and the random value be (ω) 0 ，b 0 ) By an iterative method, calculate a gradient ^ E (ω) 0 ，b 0 ) And define
Wherein j is a natural number, and epsilon is a learning speedRate; calculate gradient ^ E (ω) 0 ，b 0 ) When, define
And repeating the process
… … and so on.
S205: through learning training, the weight omega is adjusted i And the deviation b and the threshold value theta are optimally selected, so that f (x) =1 and E is smaller than a set value, the current mathematical model is a trained model, and the predicted value of the effluent COD is shown to approach the measured value of the effluent COD infinitely at the moment.
S3: obtaining a test sample, wherein half of data in the test sample is required to be COD standard exceeding data, and the other half of data is COD standard reaching data;
selecting test data of effluent COD (chemical oxygen demand) in a test sample, calling a trained mathematical model, judging the fitting property of the mathematical model, and entering the next step if the fitting property is good; and if the fitting performance is not good, returning to the step S2, and continuing training the mathematical model. In this step, it is necessary to determine the fitness of the trained mathematical model because the model trained in the previous step does not necessarily satisfy the fitness under the influence of multiple parameters, and thus it is necessary to determine the fitness of the trained mathematical model. In the step, the judgment of the fitness is that after the test data of the effluent COD reaching the standard is selected, a least square function is carried out
Calculating the value E, and if the value E is within a set value, indicating that the fitting performance of the model is good; otherwise, the fitting performance of the model cannot meet the requirement. The prediction results of the test data for effluent COD compliance in the test samples are shown in Table 2 below:
s4: selecting test data that the effluent COD in the test sample does not reach the standard, namely selecting data that the effluent COD is more than 50mg/L, calling the trained mathematical model, and calculating the control quantity of the water inlet flow, the reflux ratio of the mixed liquid, the reflux ratio of the sludge and the dissolved oxygen through the mathematical model, wherein the control quantity can meet the requirement that the effluent COD predicted value of the output data reaches the standard. The adjustment results of the test data for the excess COD of the effluent in the test sample are shown in the following table 3:
s5: the control quantity of the water inlet flow, the reflux ratio of the mixed liquid, the reflux ratio of the sludge and the dissolved oxygen is calculated according to the mathematical model, and the water inlet flow, the reflux ratio of the mixed liquid, the reflux ratio of the sludge and the dissolved oxygen are automatically regulated and controlled by the artificial intelligent control system, so that the COD of the effluent of the sludge treatment plant reaches the standard, namely the COD of the effluent is less than 50mg/L.
In the embodiment, in the automatic regulation and control process, the artificial intelligent control system regulates and controls the water inlet flow through the rotating speed of the water inlet lifting pump or the water inlet electric valve; the artificial intelligence control system regulates and controls the reflux ratio of the mixed liquid through the rotating speed of the mixed liquid reflux pump or the electric valve of the mixed liquid reflux; the artificial intelligent control system regulates and controls the sludge reflux ratio through the rotating speed of the sludge reflux pump or the sludge reflux electric valve; the artificial intelligence control system regulates and controls the content of the dissolved oxygen through the rotating speed of the blower or the gas transmission electric valve.
The parameters of the rotating speed of the water inlet lifting pump or the rotating speed of the water inlet electric valve, the rotating speed of the mixed liquid reflux pump or the mixed liquid reflux electric valve, the rotating speed of the sludge reflux pump or the rotating speed of the sludge reflux electric valve, the rotating speed of the air blower or the rotating speed of the air transmission electric valve are controlled in an optimal range, so that the COD of the effluent can be ensured to reach the standard (the COD of the effluent is less than 50 mg/L) when a sludge treatment plant operates.
Example 2
The invention also provides a system for solving the problem that COD (chemical oxygen demand) of effluent water of a sewage treatment plant exceeds standard based on artificial intelligence, the system is mainly based on a sludge AAO (anaerobic-anoxic-oxic) process treatment system, the sludge AAO process treatment system belongs to common general knowledge in the field, and details are not repeated here, and the system comprises:
(1) measurement data section
The measurement data part comprises a plurality of measurement points, and each measurement point respectively measures the operation data of the sewage treatment plant in real time; each measuring point is respectively a water inlet pipe, a water outlet pipe, a mixed liquid return pipeline and a sludge return pipeline which are positioned in a sludge treatment plant, the water inlet pipeline is provided with a first COD online monitor and a water inlet electromagnetic flowmeter, the water outlet pipeline is provided with a second COD online monitor, the mixed liquid return pipeline is provided with a mixed liquid return electromagnetic flowmeter, the sludge return pipeline is provided with a sludge return electromagnetic flowmeter, and the aerobic tank is provided with an oxygen dissolving instrument; and the first COD online monitor, the second COD online monitor, the water inlet electromagnetic flow meter, the mixed liquid reflux electromagnetic flow meter, the sludge reflux electromagnetic flow meter and the dissolved oxygen meter are respectively in communication connection with the input data acquisition module to realize real-time transmission and feedback of the measured data.
(2) Regulation control part
The regulation and control part comprises a plurality of control points, and each control point respectively regulates and controls the operation data of the sewage treatment plant in real time; the control points are respectively as follows: the system comprises a water inlet electric valve and a water inlet lifting pump which are arranged on a water inlet pipeline, a mixed liquid reflux electric valve and a mixed liquid reflux pump which are arranged on a mixed liquid reflux pipeline, a sludge reflux electric valve and a sludge reflux pump which are arranged on the sludge reflux pipeline, and a gas transmission electric valve and a blower which are arranged on an air pipe of an aerobic pool, wherein the water inlet electric valve, the water inlet lifting pump, the mixed liquid reflux electric valve, the mixed liquid reflux pump, the sludge reflux electric valve, the sludge reflux pump, the gas transmission electric valve and the blower are respectively in communication connection with an artificial intelligent control module; in example 1, the water inlet electric valve, the mixed liquid reflux electric valve, the sludge reflux electric valve and the gas transmission electric valve are controlled to respectively control the water inlet flow rate, the mixed liquid reflux flow rate, the sludge reflux flow rate and the aeration amount to the aerobic tank. Certainly, in practical application, the water inlet flow, the mixed liquid reflux flow, the sludge reflux flow and the aeration quantity to the aerobic tank can be controlled by regulating the rotating speed of the water inlet lift pump, the rotating speed of the mixed liquid reflux pump, the rotating speed of the sludge reflux pump and the rotating speed of the air blower respectively, the regulating effect can be achieved, and the water inlet lift pump, the mixed liquid reflux pump, the sludge reflux pump and the air blower are respectively in communication connection with the artificial intelligent control module.
(3) Input data acquisition module
The method comprises the steps that operation data of each measuring point are obtained through an input data obtaining module, so that training samples and testing samples of a sewage treatment plant are obtained; specifically, the training samples and the test samples are detailed in table 1 of example 1.
(4) Data analysis module
When the data analysis module runs, the mathematical model in embodiment 1 is called, and the training process and method of the mathematical model are known by referring to embodiment 1, and are not described herein again. Fitting and judging the test data of the effluent COD in the test sample reaching the standard through a data analysis module, and if the fitting is good, continuing to execute subsequent logic; if the fitting is not good, retraining the mathematical model;
the control quantity of each control point is obtained by calculating the test data of the effluent COD in the test sample which does not reach the standard through the data analysis module, and the effluent COD predicted value output by the data analysis module can be ensured to reach the standard within the range of the control quantity.
(5) Artificial intelligence control module
Regulating and controlling each control point through an artificial intelligence control module according to each control quantity, issuing a control instruction by an artificial intelligence control system, and regulating and controlling the water inlet flow through a water inlet electric valve; regulating and controlling the reflux ratio of the mixed liquid through the rotating speed of the mixed liquid reflux pump or the electric valve of the mixed liquid reflux; the sludge reflux ratio is regulated and controlled through the rotating speed of the sludge reflux pump or the sludge reflux electric valve; the content of dissolved oxygen is regulated and controlled by the rotating speed of the air blower or the electric valve for gas transmission, and finally, the COD of the effluent of the sewage treatment plant reaches the standard.
It should be noted that, in the description of the present application, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Further, in the description of the present application, the meaning of "a plurality" means at least two unless otherwise specified.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and the scope of the preferred embodiments of the present application includes other implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present application may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc.
In the description of the present specification, reference to the description of "one embodiment," "some embodiments," "an example," "a specific example," or "some examples" or the like means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (7)
1. A method for solving the problem that COD (chemical oxygen demand) of effluent of a sewage treatment plant exceeds standard based on artificial intelligence is characterized by comprising the following steps:
s1: establishing a training sample of model training according to measured values of water outlet COD, water inlet flow, mixed liquid reflux ratio, sludge reflux ratio and dissolved oxygen in normal standard-reaching operation data;
s2: establishing a mathematical model and performing model training;
s3: obtaining a test sample, selecting test data of effluent COD (chemical oxygen demand) in the test sample reaching the standard, calling a trained mathematical model, judging the fitting property of the mathematical model, and entering the next step if the fitting property is good; if the fitting is not good, returning to S2;
s4: selecting test data of the effluent COD in the test sample which do not reach the standard, calling a trained mathematical model, and calculating the control quantity of the inflow, the reflux ratio of the mixed liquid, the reflux ratio of the sludge and the dissolved oxygen through the mathematical model so as to meet the requirement that the predicted value of the effluent COD reaches the standard;
s5: calculating the water inlet flow, the mixed liquid reflux ratio, the sludge reflux ratio and the control quantity of dissolved oxygen according to a mathematical model, and automatically regulating and controlling the water inlet flow, the mixed liquid reflux ratio, the sludge reflux ratio and the dissolved oxygen through an artificial intelligent control system so as to enable the effluent COD of the sludge treatment plant to reach the standard;
the mathematical model comprises an input layer, a hidden layer and an output layer, wherein the input data of the input layer are measured values of water inflow COD, water inflow, mixed liquid reflux ratio, sludge reflux ratio and dissolved oxygen, and are x respectively 1 、x 2 、x 3 、x 4 、x 5 And the input data are training samples: test sample =6:1; the output data of the output layer is an effluent COD predicted value, and the effluent COD predicted value is y i And the measured value of COD of the effluent is y 0 ；
The method for training the mathematical model in the step S1 comprises the following steps:
s201: assigning a weight ω to input data for each training sample i Aggregating inputs into
Wherein i is a natural number;
s202: introducing a deviation b, a threshold value theta, defining
If f (x) =1, the COD of the effluent reaches the standard; if f (x) =0, the COD of the effluent exceeds the standard;
s203: introducing Sigmoid function
And defining the output function as
S204: introducing least squares functions
Let initial deviation and random value be (ω) 0 ，b 0 ) By iterative methods, the gradient is calculated
And define
Wherein j is a natural number, and epsilon is a learning rate;
s205: through learning training, the weight omega is adjusted i The deviation b and the threshold θ are optimally chosen such that f (x) =1 and E is less than the set value, then the current mathematical model is the trained model.
2. The method for solving the COD standard exceeding of the effluent of the sewage treatment plant based on the artificial intelligence as claimed in claim 1, wherein in the step S3, half of the data in the test sample is the COD standard exceeding data, and the other half of the data is the COD standard reaching data.
3. The method for solving the COD standard exceeding of the effluent of the sewage treatment plant based on the artificial intelligence as claimed in claim 2, wherein the mixed liquor reflux ratio is calculated by the ratio of the mixed liquor reflux amount to the influent flow rate, and the sludge reflux ratio is calculated by the ratio of the sludge reflux amount to the influent flow rate.
4. The method for solving the COD exceeding the standard of the effluent water of the sewage treatment plant based on the artificial intelligence according to the claim 3, wherein in the step S5, the artificial intelligence control system regulates and controls the water inlet flow through the rotating speed of a water inlet lifting pump or a water inlet electric valve; the artificial intelligence control system regulates and controls the reflux ratio of the mixed liquid through the rotating speed of the mixed liquid reflux pump or the electric valve of the mixed liquid reflux; the artificial intelligent control system regulates and controls the sludge reflux ratio through the rotating speed of the sludge reflux pump or the sludge reflux electric valve; the artificial intelligence control system regulates and controls the content of the dissolved oxygen through the rotating speed of the blower or the electric gas transmission valve.
5. A system for solving the COD standard exceeding of the effluent of a sewage treatment plant based on artificial intelligence is characterized in that the system comprises the following components based on the method for solving the COD standard exceeding of the effluent of the sewage treatment plant based on the artificial intelligence of claim 1:
the system comprises a plurality of measuring points, a data acquisition unit and a data processing unit, wherein each measuring point respectively measures the operation data of a sewage treatment plant in real time;
the system comprises a plurality of control points, a plurality of control units and a control unit, wherein each control point respectively regulates and controls the operation data of a sewage treatment plant in real time;
the input data acquisition module is used for acquiring the operation data of each measuring point through the input data module so as to acquire a training sample and a test sample of the sewage treatment plant;
the data analysis module is used for carrying out fitting judgment on the test data of the effluent COD in the test sample which reaches the standard; calculating the test data of the effluent COD in the test sample which does not reach the standard through a data analysis module to obtain the control quantity of each control point;
and the artificial intelligence control module is used for respectively regulating and controlling each control point through the artificial intelligence control module according to each control quantity so as to enable the effluent COD of the sewage treatment plant to reach the standard.
6. The system for solving the COD exceeding standard of the effluent of the sewage treatment plant based on the artificial intelligence is characterized in that each measuring point is respectively positioned on an inlet pipe, an outlet pipe, a mixed liquid return pipeline, a sludge return pipeline and an aerobic tank of the sludge treatment plant, the inlet pipe is provided with a first COD online monitor and an inlet electromagnetic flowmeter, the outlet pipe is provided with a second COD online monitor, the mixed liquid return pipeline is provided with a mixed liquid return electromagnetic flowmeter, the sludge return pipeline is provided with a sludge return electromagnetic flowmeter, and the aerobic tank is provided with a dissolved oxygen meter;
first COD on-line monitoring appearance, second COD on-line monitoring appearance, water inlet electromagnetic flowmeter, mixed liquid backward flow electromagnetic flowmeter, mud backward flow electromagnetic flowmeter, dissolved oxygen appearance respectively with input data acquisition module communication connection.
7. The system for solving the COD exceeding the standard of the effluent of the sewage treatment plant based on the artificial intelligence as claimed in claim 6, wherein each control point comprises an electric water inlet valve and an electric water inlet lift pump arranged on the water inlet pipeline, an electric mixed liquid return valve and an electric mixed liquid return pump arranged on the mixed liquid return pipeline, an electric sludge return valve and an electric sludge return pump arranged on the sludge return pipeline, and an electric gas transmission valve and an air blower arranged on the air pipe of the aerobic pool, and the electric water inlet valve, the electric water inlet lift pump, the electric mixed liquid return valve, the electric mixed liquid return pump, the electric sludge return valve, the electric sludge return pump, the electric gas transmission valve and the air blower are respectively in communication connection with the artificial intelligence control module.
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