CN115097886A - Method, system, equipment and medium for controlling concentration of dissolved oxygen in sewage treatment - Google Patents
Method, system, equipment and medium for controlling concentration of dissolved oxygen in sewage treatment Download PDFInfo
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- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 title claims abstract description 127
- 239000001301 oxygen Substances 0.000 title claims abstract description 127
- 229910052760 oxygen Inorganic materials 0.000 title claims abstract description 127
- 239000010865 sewage Substances 0.000 title claims abstract description 57
- 238000000034 method Methods 0.000 title claims abstract description 52
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 98
- XKMRRTOUMJRJIA-UHFFFAOYSA-N ammonia nh3 Chemical compound N.N XKMRRTOUMJRJIA-UHFFFAOYSA-N 0.000 claims abstract description 41
- 238000005273 aeration Methods 0.000 claims abstract description 23
- 230000008569 process Effects 0.000 claims abstract description 20
- 238000012549 training Methods 0.000 claims description 18
- 238000004590 computer program Methods 0.000 claims description 8
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- 238000004065 wastewater treatment Methods 0.000 claims description 5
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- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 description 2
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- 238000013528 artificial neural network Methods 0.000 description 2
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- GPRLSGONYQIRFK-UHFFFAOYSA-N hydron Chemical compound [H+] GPRLSGONYQIRFK-UHFFFAOYSA-N 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
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- 238000003062 neural network model Methods 0.000 description 1
- 210000002569 neuron Anatomy 0.000 description 1
- 229910052757 nitrogen Inorganic materials 0.000 description 1
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D27/00—Simultaneous control of variables covered by two or more of main groups G05D1/00 - G05D25/00
- G05D27/02—Simultaneous control of variables covered by two or more of main groups G05D1/00 - G05D25/00 characterised by the use of electric means
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Abstract
The invention discloses a method, a system, equipment and a medium for controlling the concentration of dissolved oxygen in sewage treatment. The control method of the dissolved oxygen concentration in the sewage treatment comprises the following steps: acquiring the ammonia nitrogen concentration of inlet water of sewage; determining a set dissolved oxygen concentration according to the ammonia nitrogen concentration of the inlet water; controlling the aeration air quantity of the blower according to the difference value between the set dissolved oxygen concentration and the predicted dissolved oxygen concentration; the predicted dissolved oxygen concentration is obtained by prediction according to the water inflow data of the sewage. The invention dynamically adjusts and sets the dissolved oxygen concentration according to the real-time ammonia nitrogen concentration of the inlet water, and controls the aeration air volume of the blower according to the difference between the set dissolved oxygen concentration and the predicted dissolved oxygen concentration, thereby realizing the control of the dissolved oxygen concentration in the sewage, enabling the dissolved oxygen concentration to track and set the dissolved oxygen concentration, improving the control effect of the dissolved oxygen concentration and reducing the energy consumption generated in the aeration process of the blower.
Description
Technical Field
The invention relates to the field of sewage treatment, in particular to a method, a system, equipment and a medium for controlling the concentration of dissolved oxygen in sewage treatment.
Background
The ammonia nitrogen concentration and the dissolved oxygen concentration in the sewage treatment process are important water quality indexes and are important bases for evaluating whether the discharge reaches the standard and the sewage treatment efficiency. In the actual production process, the dissolved oxygen concentration is difficult to obtain in real time through a detection instrument, great delay often exists, the control fluctuation of the dissolved oxygen concentration is easy to cause, and accurate real-time control of the dissolved oxygen concentration is difficult to realize. The modeling prediction is a feasible method for solving the difficulty in online monitoring of the water quality, wherein intelligent algorithms such as a neural network and machine learning do not depend on a mechanism model, active learning is performed through existing data, the fault tolerance is high, and the method is widely applied to modeling prediction of a sewage treatment system.
The control of the concentration of dissolved oxygen in the aeration process is an important link in sewage treatment, and the value of the dissolved oxygen directly influences the sewage treatment efficiency and the treatment quality. From the last century to the present, researchers at home and abroad have conducted aeration process design and dissolved oxygen concentration control research. The conventional PID control technology is widely used by sewage treatment plants, and although the control technology can achieve certain effect, under the complex working condition environment, the control effect is general, and the aeration process generates large energy consumption, so that the energy waste is caused. Therefore, applying a more advanced intelligent control method to the control of the dissolved oxygen concentration in the aeration process can play a crucial role in energy conservation and consumption reduction.
Disclosure of Invention
The invention aims to overcome the defects of non-ideal control effect and large energy consumption of a control method of the dissolved oxygen concentration in the aeration process in the prior art, and provides a control method, a control system, control equipment and a control medium of the dissolved oxygen concentration in sewage treatment.
The invention solves the technical problems through the following technical scheme:
the first aspect of the present invention provides a method for controlling the concentration of dissolved oxygen in sewage treatment, comprising the steps of:
acquiring the ammonia nitrogen concentration of inlet water of sewage;
determining a set dissolved oxygen concentration according to the ammonia nitrogen concentration of the inlet water;
controlling the aeration air quantity of the blower according to the difference value between the set dissolved oxygen concentration and the predicted dissolved oxygen concentration;
and predicting the predicted dissolved oxygen concentration according to the water inflow data of the sewage.
Optionally, the step of determining the set dissolved oxygen concentration according to the ammonia nitrogen concentration of the inlet water specifically includes:
and carrying out fuzzy control on the ammonia nitrogen concentration of the inlet water to obtain the set dissolved oxygen concentration.
Optionally, the predicting the dissolved oxygen concentration is predicted according to influent water data of the sewage, and specifically includes:
collecting water inlet data of sewage;
inputting the water inlet data into a water quality prediction model; wherein the water quality prediction model is obtained based on sample data training;
outputting the predicted dissolved oxygen concentration.
Optionally, the control method further comprises the steps of:
and in the training process of the water quality prediction model, optimizing the parameters of the water quality prediction model by using a differential evolution algorithm.
A second aspect of the present invention provides a system for controlling a concentration of dissolved oxygen in sewage treatment, comprising:
the acquisition module is used for acquiring the ammonia nitrogen concentration of inlet water of sewage;
the determining module is used for determining the set dissolved oxygen concentration according to the ammonia nitrogen concentration of the inlet water;
the control module is used for controlling the aeration air volume of the blower according to the difference value between the set dissolved oxygen concentration and the predicted dissolved oxygen concentration;
and predicting the predicted dissolved oxygen concentration according to the water inflow data of the sewage.
Optionally, the determining module is specifically configured to perform fuzzy control on the intake ammonia nitrogen concentration to obtain a set dissolved oxygen concentration.
Optionally, the control system further comprises:
the acquisition module is used for acquiring the water inlet data of the sewage;
the input module is used for inputting the water inlet data into a water quality prediction model; wherein the water quality prediction model is obtained based on sample data training;
and the output module is used for outputting the predicted dissolved oxygen concentration.
Optionally, the control system further comprises: and the training module is used for optimizing the parameters of the water quality prediction model by using a differential evolution algorithm in the training process of the water quality prediction model.
A third aspect of the present invention provides 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 method for controlling the concentration of dissolved oxygen in wastewater treatment according to the first aspect when executing the computer program.
A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method for controlling the concentration of dissolved oxygen in sewage treatment as described in the first aspect.
The positive progress effects of the invention are as follows: the dissolved oxygen concentration is dynamically adjusted and set according to the real-time ammonia nitrogen concentration of the inlet water, and the aeration air volume of the air blower is controlled according to the difference value between the set dissolved oxygen concentration and the predicted dissolved oxygen concentration, so that the dissolved oxygen concentration in the sewage is controlled, the set dissolved oxygen concentration is tracked, the control effect of the dissolved oxygen concentration is improved, and the energy consumption generated in the aeration process of the air blower is reduced.
Drawings
Fig. 1 is a flowchart of a method for controlling a dissolved oxygen concentration in sewage treatment according to embodiment 1 of the present invention.
Fig. 2 is a block diagram illustrating control of the concentration of dissolved oxygen in sewage treatment according to embodiment 1 of the present invention.
Fig. 3 is a flowchart of a method for predicting a dissolved oxygen concentration according to embodiment 1 of the present invention.
Fig. 4 is a block diagram of a system for controlling a concentration of dissolved oxygen in sewage treatment according to embodiment 1 of the present invention.
Fig. 5 is a schematic structural diagram of an electronic device according to embodiment 2 of the present invention.
Detailed Description
The invention is further illustrated by the following examples, which are not intended to limit the invention thereto.
Example 1
Fig. 1 is a flowchart of a method for controlling a dissolved oxygen concentration in sewage treatment according to this embodiment, where the method may be performed by a control system for controlling a dissolved oxygen concentration in sewage treatment, the control system may be implemented by software and/or hardware, and the control system may be part or all of an electronic device. The electronic device in this embodiment may be a desktop computer, a notebook computer, a tablet computer, a PDA (Personal Digital Assistant), and other electronic devices.
The following describes the method for controlling the concentration of dissolved oxygen in wastewater treatment provided by this embodiment with electronic equipment as the main implementation. As shown in fig. 1, the method for controlling the concentration of dissolved oxygen in sewage treatment provided by this embodiment may include the following steps S101 to S103:
and S101, acquiring the ammonia nitrogen concentration of inlet water of sewage. In specific implementation, detection equipment or instruments can be used for collecting the ammonia nitrogen concentration of the inlet water of the sewage in real time.
And S102, determining the set dissolved oxygen concentration according to the ammonia nitrogen concentration of the inlet water.
In specific implementation, the removal rate of the ammonia nitrogen concentration of the effluent is exponentially increased along with the concentration of the dissolved oxygen, and the energy can be further saved on the premise of ensuring that the effluent quality reaches the standard. When the ammonia nitrogen concentration of the inlet water is higher, the ammonia nitrogen concentration of the outlet water is reduced by improving the dissolved oxygen concentration; when the concentration of the ammonia nitrogen in the inlet water is lower, the concentration of dissolved oxygen is reduced so as to reduce the energy consumption generated in the aeration process.
In order to avoid the generation of step change and large overshoot of the dissolved oxygen concentration in the adjustment process, a fuzzy controller can be selected as a dynamic adjustment controller for ammonia nitrogen concentration feedforward. In an optional implementation manner of step S102, the ammonia nitrogen concentration of the intake water is subjected to fuzzy control to obtain a set dissolved oxygen concentration. In the embodiment, on the premise of ensuring that the quality of the effluent water reaches the standard, in order to further reduce the energy consumption generated in the aeration process, the set value of the dissolved oxygen concentration is dynamically adjusted by establishing a fuzzy controller between the ammonia nitrogen concentration and the set value of the dissolved oxygen concentration, namely the dissolved oxygen concentration is dynamically adjusted and set.
Wherein, the setting range of the dissolved oxygen concentration can be 1.2mg/L to 1.8mg/L, and the setting range of the corresponding ammonia nitrogen concentration of the inlet water can be 10mg/L to 22 mg/L. Based on the above, the fuzzy domain of the ammonia nitrogen concentration of the inlet water is N ═ {10, 12, 14, 16, 18, 20 and 22}, and the corresponding language values are AN, BN, CN, DN, EN, FN and GN; the fuzzy domain of the dissolved oxygen concentration is set to DO ═ 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, and 1.8, and the corresponding linguistic values are VS, LS, S, M, B, CB, and VB, which respectively represent small, medium, large, and large. Wherein, a triangular function can be selected as a membership function of the ammonia nitrogen concentration of the inlet water and the concentration of the set dissolved oxygen.
The fuzzy control rule table for setting the dissolved oxygen concentration based on ammonia nitrogen concentration feedforward is as follows:
| concentration of ammonia nitrogen in inlet water | AN | BN | CN | DN | EN | FN | GN |
| Setting the dissolved oxygen concentration | VS | LS | S | M | B | CB | VB |
In addition, in the specific process of establishing the fuzzy controller, the fuzzy controller can be defuzzified by adopting a weighted average method.
It should be noted that, in the specific implementation of step S102, the set dissolved oxygen concentration may also be determined according to the ammonia nitrogen concentration of the inlet water by other manners. For example, a fuzzy PID (proportional integral derivative) controller is adopted to determine the set dissolved oxygen concentration, or the set dissolved oxygen concentration is determined according to the pre-established ammonia nitrogen concentration-dissolved oxygen concentration corresponding relation, etc.
And step S103, controlling the aeration air volume of the blower according to the difference value between the set dissolved oxygen concentration and the predicted dissolved oxygen concentration. Wherein, the aeration by the blower means adding dissolved oxygen to the sewage. In this embodiment, the dissolved oxygen concentration in the sewage can be controlled by controlling the aeration air volume of the blower, and the dissolved oxygen concentration can be set by tracking the aeration air volume. Therefore, the controller that controls the blower in step S103 may be referred to as a DO (dissolved oxygen) controller, and in a specific implementation, the DO controller may use a PID controller.
In the present embodiment, the estimated dissolved oxygen concentration in step S103 is estimated from the influent water data of the wastewater. The water inlet data of the sewage may include data having a large correlation with the quality of the outlet water, such as inlet water SS (Suspended solid), inlet water COD (Chemical Oxygen Demand), inlet water PH (hydrogen ion concentration index), inlet water flow rate, and inlet water TN (total nitrogen).
FIG. 2 is a block diagram showing the control of the concentration of dissolved oxygen in wastewater treatment. As shown in fig. 2, the ammonia nitrogen concentration of the inlet water is input into the fuzzy controller to obtain the set dissolved oxygen concentration, and the difference value between the set dissolved oxygen concentration and the predicted dissolved oxygen concentration output by the water quality prediction model is input into the DO controller to control the aeration air volume of the blower. The water quality prediction model is used for predicting the dissolved oxygen concentration according to water inlet data, outputting the predicted dissolved oxygen concentration and forming closed-loop control on the dissolved oxygen concentration.
As shown in fig. 3, the present embodiment further provides a method for predicting the dissolved oxygen concentration, which specifically includes the following steps S201 to S203:
and step S201, collecting water inlet data of sewage. In specific implementation, the water inlet data of the sewage can be collected through a sensor, a PLC (programmable logic controller) device, an instrument and meter and the like.
And S202, inputting the water inlet data into a water quality prediction model. And the water quality prediction model is obtained based on sample data training.
And step S203, outputting the predicted dissolved oxygen concentration.
In specific implementation, original sample data can be obtained by collecting historical data and/or experimental data of a sewage treatment plant, abnormal data in the original sample data are removed, and then normalization pretreatment is carried out to obtain final sample data. And training the water quality prediction model by using the final sample data.
The final sample data is: (x) i ,t i )∈R n ×R m (ii) a Wherein x is i ＝[x i1 ,x i2 ,...,x in ] T ∈R n ，t i ＝[t i1 ,t i2 ,...,t im ] T ∈R m Representing the input sample and the desired output sample, respectively.
The expression of the single hidden layer neural network containing L hidden layer nodes is as follows:
wherein g (x) is an activation function, w i ＝[w i1 ,w i2 ,...,w in ] T For weight vectors connecting the input node with the i-th hidden node, beta i ＝[β i1 ,β i2 ,...,β im ] T For the output weight connecting the ith hidden node and the output node, b i Is the offset of the ith hidden node, o j Is the actual output value of the jth output neuron.
Wherein n is the number of the water inlet data input into the water quality prediction model, and m is the number of the output data of the water quality prediction model. In the present embodiment, since the water quality prediction model is used to predict the dissolved oxygen concentration, m is 1.
In a specific example, the water quality prediction model employs an extreme learning machine network. Specifically, in the process of constructing the water quality prediction model, the number of hidden layer nodes is initialized, then the number of hidden layer nodes is continuously increased, but the number of hidden layer nodes is smaller than the number of training data, an ELM (Extreme Learning Machine) network under different hidden layer nodes is trained and tested, training and testing errors are output, the training errors and the testing errors are added, and the number L value of the hidden nodes when the sum of the errors is the minimum value is determined as the number of hidden layer neurons of the Extreme Learning Machine network.
In one example of the implementation, a sigmoid function is selected as an activation function, an input weight ω and an offset b are randomly generated, the range of the input weight ω and the offset b is selected as [ -1,1], and an implicit layer output matrix H is calculated:
the single hidden layer neural network model can approximate a sample with zero error, namely, the following conditions are met:
then
I.e., H β ═ T. From this, the output weight β ═ H is calculated + T; wherein H + As a matrix HThe generalized inverse.
In an extreme learning network, the output weight β is based on H + T is calculated, and the input weight omega and the bias b are randomly generated and do not change, so that the problems of low response speed of the water quality prediction model to data, poor generalization performance and the like caused by the generation of non-optimal input values are inevitable. Therefore, in an alternative embodiment, the input weights ω and the bias b of the extreme learning machine model are optimized by using a differential evolution algorithm.
The differential evolution algorithm is an intelligent optimization learning algorithm with heuristic global search capability. And generating a new generation of population through a group of initial populations randomly generated through a feasible solution space through mutation, intersection and selection operations, and continuously iterating until a termination condition is met to obtain an optimal value. By using
Each individual in the group is subjected to mutation operation as a target variable to obtain a new variant individual
Typical mutation operations are:
wherein r0, r1, r2 epsilon (1,2, …, NP) and the current target individual r i Are different from each other; f is a variation factor; and g is the current iteration number.
The crossover operation is a process of partially exchanging parent individuals with variant individuals according to crossover probability to obtain new test individuals, wherein each parent individual
The j-th dimension component and the variant individual of (2)
The j-th dimension components are subjected to cross operation in a one-to-one correspondence manner to obtain a new test individual
Component of j-th dimension
Wherein, CR is the cross probability, and the value range is [0, 1]]；j rand A fixed value randomly generated in the dimension D; rand (j) epsilon [0, 1]]。
Selection operation is about to
And test subjects
Selecting next generation individuals in a one-to-one comparison
Selecting a later generation of population
Wherein f is a fitness function.
And taking the sum of the squares of the errors of the output value of the dissolved oxygen concentration predicted by the extreme learning machine network and the actual value as a target function, and judging whether the error magnitude of the predicted value output by the differential evolution algorithm optimization meets the target function. If yes, outputting a predicted value, otherwise, repeating the steps until the maximum iteration times, and outputting the optimal parameter input weight omega and the bias b.
In the embodiment, the dissolved oxygen concentration is predicted in real time according to the water quality prediction model after the parameters are optimized, the accuracy of the prediction result can be improved, and further, the blower is controlled according to the predicted dissolved oxygen concentration and the set ammonia nitrogen concentration, so that the control effect of the dissolved oxygen concentration is further improved, and the energy consumption generated in the aeration process is reduced.
As shown in fig. 4, the present embodiment further provides a system 40 for controlling the concentration of dissolved oxygen in sewage treatment, comprising: an acquisition module 41, a determination module 42, and a control module 43.
The obtaining module 41 is used for obtaining the ammonia nitrogen concentration of the inlet water of the sewage.
The determining module 42 is used for determining the set dissolved oxygen concentration according to the ammonia nitrogen concentration of the inlet water.
The control module 43 is used for controlling the aeration air volume of the blower according to the difference value between the set dissolved oxygen concentration and the predicted dissolved oxygen concentration.
And predicting the predicted dissolved oxygen concentration according to the water inflow data of the sewage.
In an alternative embodiment, the determining module 42 is specifically configured to perform fuzzy control on the ammonia nitrogen concentration of the inlet water, so as to obtain the set dissolved oxygen concentration.
In an alternative embodiment, as shown in fig. 4, the control system 40 further includes an acquisition module 44, an input module 45, and an output module 46.
The acquisition module 44 is used for acquiring the water inflow data of the sewage.
The input module 45 is used for inputting the water inlet data into a water quality prediction model; and the water quality prediction model is obtained based on sample data training.
The output module 46 is used for outputting the predicted dissolved oxygen concentration.
In an optional implementation manner, the control system further includes a training module, configured to optimize parameters of the water quality prediction model by using a differential evolution algorithm in a training process of the water quality prediction model.
Example 2
Fig. 5 is a schematic structural diagram of an electronic device provided in this embodiment. The electronic device includes at least one processor and a memory communicatively coupled to the at least one processor. Wherein the memory stores instructions executable by the at least one processor, the instructions being executable by the at least one processor to enable the at least one processor to perform the method of controlling dissolved oxygen concentration in wastewater treatment of embodiment 1. The electronic device 3 shown in fig. 5 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiment of the present invention.
The components of the electronic device 3 may include, but are not limited to: the at least one processor 4, the at least one memory 5, and a bus 6 connecting the various system components (including the memory 5 and the processor 4).
The bus 6 includes a data bus, an address bus, and a control bus.
The memory 5 may include volatile memory, such as Random Access Memory (RAM)51 and/or cache memory 52, and may further include Read Only Memory (ROM) 53.
The processor 4 executes various functional applications and data processing, such as the above-described control method of the dissolved oxygen concentration in sewage treatment, by running a computer program stored in the memory 5.
The electronic device 3 may also communicate with one or more external devices 7, such as a keyboard, pointing device, etc. Such communication may be via an input/output (I/O) interface 8. Also, the electronic device 3 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 9. As shown in fig. 5, the network adapter 9 communicates with other modules of the electronic device 3 via the bus 6. It should be appreciated that although not shown in FIG. 5, other hardware and/or software modules may be used in conjunction with the electronic device 3, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, and data backup storage systems, etc.
It should be noted that although in the above detailed description several units/modules or sub-units/modules of the electronic device are mentioned, such a division is merely exemplary and not mandatory. Indeed, the features and functions of two or more of the units/modules described above may be embodied in one unit/module according to embodiments of the invention. Conversely, the features and functions of one unit/module described above may be further divided into embodiments by a plurality of units/modules.
Example 3
The present embodiment provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to execute the method for controlling the dissolved oxygen concentration in sewage treatment in embodiment 1.
More specific examples that may be employed by the readable storage medium include, but are not limited to: a portable disk, hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.
In a possible embodiment, the present invention may also be realized in the form of a program product including program code for causing an electronic device to execute a method of controlling a dissolved oxygen concentration in sewage treatment in the implementation example 1, when the program product is run on the electronic device.
Where program code for carrying out the invention is written in any combination of one or more programming languages, the program code may be executed entirely on the electronic device, partly on the electronic device, as a stand-alone software package, partly on the electronic device, partly on a remote device or entirely on the remote device.
While specific embodiments of the invention have been described above, it will be understood by those skilled in the art that this is by way of example only, and that the scope of the invention is defined by the appended claims. Various changes or modifications to these embodiments may be made by those skilled in the art without departing from the principle and spirit of this invention, and these changes and modifications are within the scope of this invention.
Claims (10)
1. A method for controlling the concentration of dissolved oxygen in sewage treatment is characterized by comprising the following steps:
acquiring the concentration of ammonia nitrogen in inlet water of sewage;
determining a set dissolved oxygen concentration according to the ammonia nitrogen concentration of the inlet water;
controlling the aeration air quantity of the blower according to the difference value between the set dissolved oxygen concentration and the predicted dissolved oxygen concentration;
and predicting the predicted dissolved oxygen concentration according to the water inflow data of the sewage.
2. The control method according to claim 1, wherein the step of determining the set dissolved oxygen concentration according to the ammonia nitrogen concentration of the inlet water specifically comprises:
and carrying out fuzzy control on the ammonia nitrogen concentration of the inlet water to obtain the set dissolved oxygen concentration.
3. The control method according to claim 1, wherein the predicting the dissolved oxygen concentration is predicted according to influent water data of the wastewater, and specifically comprises:
collecting water inlet data of sewage;
inputting the water inlet data into a water quality prediction model; wherein the water quality prediction model is obtained based on sample data training;
outputting the predicted dissolved oxygen concentration.
4. The control method according to claim 3, characterized by further comprising the step of:
and in the training process of the water quality prediction model, optimizing the parameters of the water quality prediction model by using a differential evolution algorithm.
5. A control system for dissolved oxygen concentration in sewage treatment, comprising:
the acquisition module is used for acquiring the ammonia nitrogen concentration of inlet water of sewage;
the determining module is used for determining the set dissolved oxygen concentration according to the ammonia nitrogen concentration of the inlet water;
the control module is used for controlling the aeration air volume of the blower according to the difference value between the set dissolved oxygen concentration and the predicted dissolved oxygen concentration;
and predicting the predicted dissolved oxygen concentration according to the water inflow data of the sewage.
6. The control system of claim 5, wherein the determination module is specifically configured to perform fuzzy control on the intake ammonia nitrogen concentration to obtain a set dissolved oxygen concentration.
7. The control system of claim 5, further comprising:
the acquisition module is used for acquiring the water inlet data of the sewage;
the input module is used for inputting the water inlet data into a water quality prediction model; the water quality prediction model is obtained based on sample data training;
and the output module is used for outputting the predicted dissolved oxygen concentration.
8. The control system of claim 7, further comprising: and the training module is used for optimizing the parameters of the water quality prediction model by using a differential evolution algorithm in the training process of the water quality prediction model.
9. 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 method for controlling the concentration of dissolved oxygen in wastewater treatment according to any one of claims 1 to 4 when executing the computer program.
10. A computer-readable storage medium on which a computer program is stored, the computer program being executed by a processor to implement the method for controlling a dissolved oxygen concentration in sewage treatment according to any one of claims 1 to 4.
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| CN116859830A (en) * | 2023-03-27 | 2023-10-10 | 福建天甫电子材料有限公司 | Production management control system for electronic grade ammonium fluoride production |
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| CN116859830B (en) * | 2023-03-27 | 2024-01-26 | 福建天甫电子材料有限公司 | Production management control system for electronic grade ammonium fluoride production |
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