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Deep neural network-based dissolved oxygen concentration control method and system

A deep neural network and dissolved oxygen concentration technology, applied in the field of control, can solve the problems of inability to establish a model, complex relationship between dissolved oxygen operating conditions, and difficulty in controlling the operation of sewage plants, and achieve the effect of precise control.

Pending Publication Date: 2017-11-28
北京易沃特科技有限公司
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  • Abstract
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  • Application Information

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Problems solved by technology

[0004] At present, the control of the aeration rate of the sewage treatment plant mainly depends on the design standards and the experience of the technicians to implement it artificially. However, the influent flow rate and water quality parameters change at any time, and this change has a lot of randomness, which makes the sewage It is very difficult to control the operation of the factory
In addition, the sewage treatment system currently used in sewage treatment plants is a multivariable nonlinear system. The relationship between the concentration control of dissolved oxygen, sewage water quality and operating conditions is complicated, and it is difficult to describe it with a linear relationship. Therefore, it is impossible to use traditional mechanism analysis and The method of mathematical derivation to build the corresponding model

Method used

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  • Deep neural network-based dissolved oxygen concentration control method and system
  • Deep neural network-based dissolved oxygen concentration control method and system
  • Deep neural network-based dissolved oxygen concentration control method and system

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Embodiment 1

[0043] figure 1 A schematic flowchart of the method for controlling dissolved oxygen concentration based on a deep neural network provided in Embodiment 1 of the present invention is shown. Such as figure 1 As shown, the method includes:

[0044] Step S100, detecting and collecting multiple sets of raw data in the wastewater;

[0045] Step S200, establishing a deep neural network model according to the original data;

[0046] Step S300, using the deep neural network model to calculate control parameters;

[0047] Step S400, adjusting the aeration amount according to the control parameters.

[0048] The concrete technical scheme of embodiment one of the present invention is:

[0049] Step S100, detecting and collecting multiple sets of raw data in the wastewater.

[0050] The raw data include but not limited to: wastewater flow rate, hydraulic retention time, influent pollutant concentration, effluent pollutant concentration, activated sludge concentration, aeration param...

Embodiment 2

[0112] Corresponding to the embodiments of the present invention, Figure 5 A schematic structural diagram of a dissolved oxygen concentration control system based on a deep neural network provided by an embodiment of the present invention is shown. Such as Figure 5 As shown, the system includes: a data collection module 101, a model building module 102, a parameter calculation module 103, and a control module 104;

[0113] The data collection module 101, preferably an intelligent gateway device, is used to detect and collect multiple sets of raw data in wastewater. If necessary, the data acquisition module 101 is also used to analyze the validity of the original data, and extract effective data to replace the original data.

[0114] Wherein, the raw data include but not limited to: wastewater flow rate, hydraulic retention time, influent pollutant concentration, effluent pollutant concentration, activated sludge concentration, aeration parameters and corresponding dissolved ...

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Abstract

The invention discloses a deep neural network-based dissolved oxygen concentration control method and system. The method includes the following steps that: a plurality of groups of raw data in waste water are detected and collected; a deep neural network model is established according to the raw data; and the deep neural network model is used to calculate control parameters; and an aeration rate is adjusted according to the control parameters. According to the deep neural network-based dissolved oxygen concentration control method and system of the invention, the dissolved oxygen model of the deep neural network is established so as to accurately control dissolved oxygen.

Description

technical field [0001] The invention relates to the technical field of control, in particular to a method and system for controlling dissolved oxygen concentration. Background technique [0002] Dissolved oxygen is a key factor in the operation of sewage aerobic biological treatment system. The total amount of oxygen supply directly determines the cost of sewage treatment. If the concentration of dissolved oxygen is too low, the activity of activated sludge will be reduced, the degradation of organic matter by organisms will be inhibited, and sludge bulking will occur; Excessively high dissolved oxygen will accelerate the consumption of organic matter in sewage, causing microorganisms to age due to lack of nutrients. Long-term excessively high dissolved oxygen will reduce the flocculation performance and adsorption capacity of activated sludge, increase energy consumption, and cause suspended solids Sedimentability deteriorates. Therefore, the control of dissolved oxygen is...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05D11/13
CPCG05D11/13
Inventor 方芳贾胜勇李守东姜宁梁猛
Owner 北京易沃特科技有限公司
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