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A method and system for automatic burning of hot blast stove based on deep reinforcement learning

A technology of reinforcement learning and hot blast stove, applied in neural learning methods, furnaces, blast furnaces, etc., can solve the bottleneck of knowledge acquisition, difficulty in summarizing and extracting control rules, etc., to achieve high control accuracy, high optimization efficiency, and high flexibility. Effect

Active Publication Date: 2022-05-03
BERIS ENG & RES CORP
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This method has high control precision, good effectiveness, relatively flexible, and high reliability; but this method has the problem of "knowledge acquisition bottleneck", and it is difficult to generalize and extract control rules

Method used

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  • A method and system for automatic burning of hot blast stove based on deep reinforcement learning
  • A method and system for automatic burning of hot blast stove based on deep reinforcement learning
  • A method and system for automatic burning of hot blast stove based on deep reinforcement learning

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Effect test

Embodiment 1

[0035] Such as figure 1 As shown, this embodiment provides a method for automatic firing of hot blast stoves based on deep reinforcement learning, including:

[0036] S1: Obtain the historical furnace firing data of the hot blast stove;

[0037] S2: According to the historical furnace data, train the depth enhancement model of the hot blast stove in different combustion stages;

[0038] The training includes: according to the state of the hot blast stove and the firing action at the previous moment, predict the action range of the firing action in the current hot blast stove state and the feedback state of the hot blast stove at the next moment after the firing action is performed; The reward and punishment value of the firing action, and select the optimal firing action in the current state of the hot blast stove; iteratively calculate the optimal firing action at each moment in the current combustion stage;

[0039] S3: Carry out automatic burning control on the hot blast ...

Embodiment 2

[0069] This embodiment provides a hot stove automatic firing system based on deep reinforcement learning, including:

[0070] The data acquisition module is used to obtain the historical furnace burning data of the hot blast stove;

[0071] The model training module is used to train the depth enhancement model of the hot blast stove in different combustion stages according to the historical furnace data;

[0072] The training includes: according to the state of the hot blast stove and the firing action at the previous moment, predict the action range of the firing action in the current hot blast stove state and the feedback state of the hot blast stove at the next moment after the firing action is performed; The reward and punishment value of the firing action, and select the optimal firing action in the current state of the hot blast stove; iteratively calculate the optimal firing action at each moment in the current combustion stage;

[0073] The automatic control module is...

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Abstract

The invention discloses a method and system for automatic firing of hot blast stoves based on deep reinforcement learning, comprising: obtaining historical firing data of hot blast stoves; training deep reinforcement models of hot blast stoves at different combustion stages according to historical firing data; The training includes: according to the state of the hot blast stove and the firing action at the previous moment, predict the action range of the firing action in the current hot blast stove state and the feedback state of the hot blast stove at the next moment after performing the firing action; obtain the firing rate according to the feedback state of the hot blast stove Action reward and punishment value, select the optimal firing action in the current state of the hot blast stove; iteratively calculate the optimal firing action at each moment in the current combustion stage; automatically burn the hot blast stove according to the depth enhancement model of the hot blast stove in different combustion stages control. The automatic firing of the hot blast stove is realized by using the offline learning method of deep reinforcement learning, which has high control precision, good generalization and strong anti-interference ability.

Description

technical field [0001] The invention relates to the technical field of blast furnace ironmaking in the metallurgical industry, in particular to an automatic firing method and system for a hot blast stove based on deep reinforcement learning. Background technique [0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art. [0003] The main equipment for the production of pig iron in the iron and steel industry is the blast furnace. Its principle is to blow the high-temperature hot air generated by the hot blast stove into the blast furnace to burn coke and reduce iron ore to molten iron. About a quarter of the heat consumed in the ironmaking production process is provided by the hot blast supplied by the hot blast stove to the blast furnace. consumption, to achieve energy saving and consumption reduction. [0004] In order to increase the air supply temperature of the blast furna...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): C21B9/00C21B9/10G06N3/08
CPCC21B9/00C21B9/10G06N3/08
Inventor 陈兆文李小健周春晖
Owner BERIS ENG & RES CORP