Air compression station optimization method based on deep learning

A deep learning and optimization method technology, applied in the field of air compressor stations, can solve the problems of air compressor efficiency impact, high efficiency not effectively utilized, and environmental changes not considered, so as to optimize the number of units to be opened, protect the environment, and optimize the overall Effect

Inactive Publication Date: 2018-07-10
浙江中睿低碳科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] 1. The air compressor is opened randomly, and the high efficiency is not effectively utilized
[0006] 2. The impact of environmental changes on the efficiency of the air compressor is not considered

Method used

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  • Air compression station optimization method based on deep learning
  • Air compression station optimization method based on deep learning
  • Air compression station optimization method based on deep learning

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Embodiment

[0046] figure 1 A flow chart of an optimization method for an air compressor station based on deep learning in an embodiment of the present invention is schematically given, as shown in figure 1 As shown, the optimization method of the air compressor station based on deep learning comprises the following steps:

[0047] (A1) Use the monitoring Internet of Things to obtain the operating parameters of each air compressor in the air compressor station, such as running time, inlet pressure, outlet pressure, ambient temperature of the air compressor station, inlet temperature, outlet temperature, flow rate and power;

[0048] (A2) Utilize deep learning algorithm to analyze described operation parameter, extract the operation pattern feature model of each air compressor in the air compressor station; Specifically:

[0049] The deep learning algorithm utilizes a deep autoencoder comprising: an encoder, a decoder, and a hidden layer;

[0050] The encoder uses the following relationa...

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Abstract

The invention provides an air compression station optimization method based on deep learning. The air compression station optimization method based on deep learning comprises the steps that (A1) a monitoring Internet of Things is utilized to obtain operating parameters of all air compressors in an air compression station; (A2) a deep learning algorithm is utilized to analyze the operating parameters, and an operating mode feature model of all the air compressors in the air compression station is extracted; and (A3) a genetic algorithm is utilized to optimize the operating mode feature model, and the number of started air compressors and starting time are acquired. The method has the advantage that power consumption is low.

Description

technical field [0001] The invention relates to an air compressor station, in particular to an optimization method for an air compressor station based on deep learning. Background technique [0002] As a clean and environmentally friendly energy source, compressed air is widely used in various fields of industrial production. But as a secondary energy source, compressed air itself consumes a lot of electricity. According to statistics, 10% of my country's industrial electricity consumption is used for air compressors. However, for a long time, enterprises have not paid enough attention to the energy consumption of the compressed air system, resulting in unreasonable energy consumption of the air compressor and waste of energy in the compressed air system. For enterprises, air supply is generally adopted in the form of an air compressor station, which consists of multiple air compressors. [0003] At present, the air compressor units rely on the original control method of ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F11/30G06N3/04G06N3/08G06N3/12
CPCG06F11/3055G06F11/3058G06N3/084G06N3/086G06N3/088G06N3/126G06N3/045Y02D10/00
Inventor 史云龙唐志军许杨铭
Owner 浙江中睿低碳科技有限公司
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