A water treatment control system and method for the energy center of a manufacturing enterprise

Abstract

The present invention relates to a water treatment control system and method for an energy center of a manufacturing enterprise, which reads instrument data and equipment status data from all reaction tanks. The instrument data includes dissolved oxygen data, and the equipment status data includes the relative aeration volume of the aeration equipment. Calculate the dissolved oxygen difference between adjacent reaction tanks, and respectively construct input vectors corresponding to three aerobic reaction tanks. Among them, the input vector for the i-th reaction tank at least includes: the increase in dissolved oxygen amount of the i-th reaction tank compared to the (i-1)-th reaction tank, the increase in dissolved oxygen amount of the (i-1)-th reaction tank compared to the (i-2)-th reaction tank, and the relative aeration volume of the aeration equipment of the i-th reaction tank. Input the input vector into the corresponding state model, receive the control parameters output by the state model, and control the aeration equipment of the corresponding reaction tank based on the control parameters. Compared with the prior art, the present invention has the advantages of good smoothness and the like.

Description

Technical Field

[0001] The present invention relates to the field of water treatment in an enterprise energy center, and particularly to a water treatment control system and method for an energy center of a manufacturing enterprise. Background Art

[0002] Water treatment is a whole process of adjusting water quality through physical, chemical, and biological means to make the water quality meet the standards to meet the needs of production and life.

[0003] The scope involved in the water treatment field is divided into three categories: production and supply of tap water, sewage treatment and recycling, and other water treatment, utilization and distribution.

[0004] The water treatment process of a manufacturing enterprise is a typical complex system with nonlinearity, multi-variables, and high turbulence. Strong interference increases the control difficulty of the water treatment process of a manufacturing enterprise. In the prior art, the sewage treatment reference model No. 1, namely BSM1, provides a system applicable to the water treatment of a manufacturing enterprise. As Figure 1 shown, it consists of 5 biochemical reaction tanks and 1 secondary sedimentation tank, and this structure simulates the A / O process. The basic biochemical reaction process in the reaction tank.

[0005] Among them, the 5 biochemical reaction tanks are divided into anoxic and aerobic. Dissolved oxygen, as the core control variable in the sewage treatment process, plays an important role in the sewage treatment process. However, in the actual process, manual adjustment is still used to change the dissolved oxygen concentration in the sewage. The adjustment process is prone to overshoot, thus reducing the quality of the biochemical reaction. Summary of the Invention

[0006] The purpose of the present invention is to provide a water treatment control system and method for an energy center of a manufacturing enterprise. By taking the increase in dissolved oxygen amount in the i-th reaction tank compared to the (i - 1)-th reaction tank, the increase in dissolved oxygen amount in the (i - 1)-th reaction tank compared to the (i - 2)-th reaction tank, the dissolved oxygen in the i-th reaction tank, and the relative aeration volume of the aeration equipment in the i-th reaction tank as the input vector of the state model, the smoothness of the switching of the aeration equipment is improved, thereby improving the control effect.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A water treatment control system for an energy center of a manufacturing enterprise, comprising:

[0009] A data service layer, including a data acquisition module and a data storage module. The data acquisition module is configured to collect the instrument data and equipment status data of all reaction tanks, and the data storage module is configured to store the instrument data and equipment status data of each reaction tank. Among them, there are a total of five reaction tanks, the first two are anoxic reaction tanks, and the last three are aerobic reaction tanks;

[0010] The middle processing layer includes a data reading module and a data processing module. The data reading module is configured to read the data in the data storage module and forward it to the data processing module. The data processing module is configured to generate control parameters for the aeration equipment of each aerobic reaction tank according to the instrument data and equipment status data of all reaction tanks.

[0011] The interface display layer is configured to display the instrument data and equipment status data of the reaction tank.

[0012] The data processing module is configured to specifically implement the following steps:

[0013] Read the instrument data and equipment status data from all reaction tanks in the data storage module. Among them, the instrument data includes dissolved oxygen data, and the equipment status data includes the relative aeration volume of the aeration equipment.

[0014] Calculate the dissolved oxygen difference between adjacent reaction tanks, and respectively construct input vectors corresponding to three aerobic reaction tanks. Among them, the input vector for the i-th reaction tank at least includes: the increase in dissolved oxygen in the i-th reaction tank compared to the (i - 1)-th reaction tank, the increase in dissolved oxygen in the (i - 1)-th reaction tank compared to the (i - 2)-th reaction tank, the dissolved oxygen in the i-th reaction tank, and the relative aeration volume of the aeration equipment in the i-th reaction tank.

[0015] Input the input vector into the corresponding state model, receive the control parameters output by the state model, and control the aeration equipment of the corresponding reaction tank based on the control parameters.

[0016] The control parameter is the adjustment value of the relative aeration volume. Controlling the aeration equipment of the corresponding reaction tank based on the control parameter specifically includes:

[0017] Obtain the target relative aeration volume based on the adjustment value of the relative aeration volume and the current relative aeration volume.

[0018] Determine the aeration flow rate according to the target relative aeration volume and in combination with the sewage volume in the reaction tank.

[0019] The interface display layer provides a graphical interaction interface.

[0020] The interface display layer further includes a mobile terminal.

[0021] The state model is a convolutional neural network.

[0022] A water treatment control method for the energy center of a manufacturing enterprise includes:

[0023] Read the instrument data and equipment status data from all reaction tanks. There are a total of five reaction tanks, the first two are anoxic reaction tanks, and the last three are aerobic reaction tanks. The instrument data includes dissolved oxygen data, and the equipment status data includes the relative aeration volume of the aeration equipment.

[0024] Calculate the dissolved oxygen difference between adjacent reaction tanks, and construct input vectors corresponding to the three aerobic reaction tanks respectively. Among them, the input vector for the i-th reaction tank at least includes: the increase in dissolved oxygen in the i-th reaction tank compared to the (i - 1)-th reaction tank, the increase in dissolved oxygen in the (i - 1)-th reaction tank compared to the (i - 2)-th reaction tank, the dissolved oxygen in the i-th reaction tank, and the relative aeration volume of the aeration equipment in the i-th reaction tank.

[0025] Input the input vector into the corresponding state model, receive the control parameters output by the state model, and control the aeration equipment of the corresponding reaction tank based on the control parameters.

[0026] A readable storage medium stores a program, and when the program is executed by a processor, it implements the method as described above.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] 1) By using the increase in dissolved oxygen in the i-th reaction tank compared to the (i - 1)-th reaction tank, the increase in dissolved oxygen in the (i - 1)-th reaction tank compared to the (i - 2)-th reaction tank, the dissolved oxygen in the i-th reaction tank, and the relative aeration volume of the aeration equipment in the i-th reaction tank as the input vector of the state model, the smoothness of the aeration equipment switching is improved, thereby improving the control effect.

[0029] 2) By using the relative aeration volume for conversion, the data structure of the state model can be simplified, and the computing power requirement for the processing chip can be reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a schematic structural diagram of a water treatment system;

[0031] Figure 2 It is a schematic structural diagram of the system of the present invention;

[0032] Figure 3 It is a schematic control flow diagram. DETAILED DESCRIPTION OF THE INVENTION

[0033] The present invention will be described in detail below with reference to the drawings and specific embodiments. This embodiment is implemented on the premise of the technical solution of the present invention, and the detailed implementation manner and specific operation process are given, but the protection scope of the present invention is not limited to the following embodiments.

[0034] An energy center water treatment control system for manufacturing enterprises, which is implemented in the form of a computer system, such as Figure 2 shown, including:

[0035] A data service layer, including a data acquisition module and a data storage module. The data acquisition module is configured to acquire instrument data and equipment status data of all reaction tanks. The data storage module is configured to store the instrument data and equipment status data of each reaction tank. Among them, there are a total of five reaction tanks, the first two are anoxic reaction tanks, and the last three are aerobic reaction tanks. The anoxic reaction tanks do not have aeration equipment, and the aerobic reaction tanks are equipped with aeration equipment;

[0036] An intermediate processing layer, including a data reading module and a data processing module. The data reading module is configured to read the data in the data storage module and forward it to the data processing module. The data processing module is configured to generate control parameters for the aeration equipment of each aerobic reaction tank according to the instrument data and equipment status data of all reaction tanks;

[0037] An interface display layer, which is configured to display the instrument data and equipment status data of the reaction tank;

[0038] By taking the increase in dissolved oxygen amount of the i-th reaction tank compared to the (i - 1)-th reaction tank, the increase in dissolved oxygen amount of the (i - 1)-th reaction tank compared to the (i - 2)-th reaction tank, the dissolved oxygen of the i-th reaction tank, and the relative aeration volume of the aeration equipment of the i-th reaction tank as the input vector of the state model, the smoothness of the aeration equipment switching is improved, thereby improving the control effect.

[0039] Such as Figure 3 shown, the data processing module is configured to specifically implement the following steps:

[0040] Read the instrument data and equipment status data of all reaction tanks from the data storage module. Among them, the instrument data includes dissolved oxygen data, and the equipment status data includes the relative aeration volume of the aeration equipment;

[0041] Calculate the dissolved oxygen difference between adjacent reaction tanks, and respectively construct input vectors corresponding to the three aerobic reaction tanks. Among them, the output input vector for the i-th reaction tank at least includes: the increase in dissolved oxygen amount of the i-th reaction tank compared to the (i - 1)-th reaction tank, the increase in dissolved oxygen amount of the (i - 1)-th reaction tank compared to the (i - 2)-th reaction tank, the dissolved oxygen of the i-th reaction tank, and the relative aeration volume of the aeration equipment of the i-th reaction tank;

[0042] Input the input vector into the corresponding state model, receive the control parameters output by the state model, and control the aeration equipment of the corresponding reaction tank based on the control parameters.

[0043] Specifically, the control parameter is the adjustment value of the relative aeration volume. Controlling the aeration equipment of the corresponding reaction tank based on this control parameter specifically includes: obtaining the target relative aeration volume based on the adjustment value of the relative aeration volume and the current relative aeration volume; determining the aeration flow rate according to the target relative aeration volume in combination with the sewage volume in the reaction tank.

[0044] The interface display layer provides a graphical interaction interface. In some embodiments, the interface display layer further includes a mobile terminal.

[0045] In some embodiments, the state model is a convolutional neural network. Of course, in other embodiments, other models can also be used, such as long short-term memory networks, etc. However, through simulation experiments, the control smoothness of the convolutional neural network is the best. The network includes four input nodes and one output node. The four input nodes respectively correspond to the increase in dissolved oxygen in the i-th reaction tank compared to the (i-1)-th reaction tank, the increase in dissolved oxygen in the (i-1)-th reaction tank compared to the (i-2)-th reaction tank, the dissolved oxygen in the i-th reaction tank, and the relative aeration volume of the aeration equipment in the i-th reaction tank. One output node corresponds to the control parameter. Since there are three aerobic reaction tanks in the water treatment system of this application, there are a total of three state models.

[0046] In this embodiment, the specific simulation process uses MATLAB / Simulink to implement BSM1.

Claims

1. A water treatment control system for the energy center of a manufacturing enterprise, characterized in that, Including: A data service layer, including a data acquisition module and a data storage module. The data acquisition module is configured to acquire instrument data and equipment status data of all reaction tanks. The data storage module is configured to store the instrument data and equipment status data of each reaction tank. Among them, there are a total of five reaction tanks, the first two are anoxic reaction tanks, and the last three are aerobic reaction tanks. An intermediate processing layer, including a data reading module and a data processing module. The data reading module is configured to read the data in the data storage module and forward it to the data processing module. The data processing module is configured to generate control parameters for the aeration equipment of each aerobic reaction tank based on the instrument data and equipment status data of all reaction tanks. An interface display layer, configured to display the instrument data and equipment status data of the reaction tanks. The data processing module is configured to specifically implement the following steps: Read the instrument data and equipment status data from all reaction tanks from the data storage module. Among them, the instrument data includes dissolved oxygen data, and the equipment status data includes the relative aeration volume of the aeration equipment. Calculate the difference in dissolved oxygen between adjacent reaction tanks, and construct input vectors corresponding to the three aerobic reaction tanks respectively. Among them, for the i output input vector of the i reaction tank includes at least: the increase in dissolved oxygen of the i- reaction tank compared to the i- reaction tank, the increase in dissolved oxygen of the i- reaction tank compared to the i reaction tank, the dissolved oxygen of the i reaction tank, and the relative aeration volume of the aeration equipment of the reaction tank; Input the input vector into the corresponding state model, receive the control parameters output by the state model, and control the aeration equipment of the corresponding reaction tank based on the control parameters.

2. The water treatment control system for the energy center of a manufacturing enterprise according to claim 1, wherein The control parameter is the adjustment value of the relative aeration volume. Controlling the aeration equipment of the corresponding reaction tank based on the control parameter specifically includes: Obtaining the target relative aeration volume based on the adjustment value of the relative aeration volume and the current relative aeration volume. Determining the aeration flow rate according to the target relative aeration volume and in combination with the sewage volume in the reaction tank.

3. The water treatment control system of the energy center of a manufacturing enterprise according to claim 1, characterized in that, The interface display layer provides a graphical interaction interface.

4. The water treatment control system of the energy center of a manufacturing enterprise according to claim 1, characterized in that, The interface display layer further includes a mobile terminal.

5. The water treatment control system for the energy center of a manufacturing enterprise according to claim 1, characterized in that, The state model is a convolutional neural network.

6. A water treatment control method for the energy center of a manufacturing enterprise, characterized in that, Including: Read the instrument data and equipment status data from all reaction tanks. Among them, there are a total of five reaction tanks, the first two are anoxic reaction tanks, and the last three are aerobic reaction tanks. The instrument data includes dissolved oxygen data, and the equipment status data includes the relative aeration volume of the aeration equipment. Calculate the difference in dissolved oxygen between adjacent reaction tanks, and construct input vectors corresponding to the three aerobic reaction tanks respectively. Among them, for the i output input vector of the i th reaction tank includes at least: the increase in dissolved oxygen of the i- th reaction tank compared to the i- 1st reaction tank, the increase in dissolved oxygen of the i- 1st reaction tank compared to the i 2nd reaction tank, the dissolved oxygen of the i th reaction tank, and the relative aeration volume of the aeration equipment of the th reaction tank; Input the input vector into the corresponding state model, receive the control parameters output by the state model, and control the aeration equipment of the corresponding reaction tank based on the control parameters.

7. A water treatment control method for an energy center of a manufacturing enterprise according to claim 6, characterized in that, The control parameter is the adjustment value of the relative aeration volume. Controlling the aeration equipment of the corresponding reaction tank based on the control parameter specifically includes: Obtaining the target relative aeration volume based on the adjustment value of the relative aeration volume and the current relative aeration volume. Determining the aeration flow rate according to the target relative aeration volume and in combination with the sewage volume in the reaction tank.

8. The water treatment control method for an energy center of a manufacturing enterprise according to claim 6, wherein The instrument data and equipment status data are displayed through the interface display layer, and the interface display layer provides a graphical interaction interface.

9. A water treatment control method for an energy center of a manufacturing enterprise according to claim 6, characterized in that, The state model is a convolutional neural network.

10. A readable storage medium, on which a program is stored, characterized in that, When the program is executed by a processor, it implements the method described in any one of claims 6-9.

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