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A Long-term Interval Prediction and Structure Learning Method for Iron and Steel Gas System

A technology of gas system and learning method, applied in the field of information

Active Publication Date: 2021-06-08
DALIAN UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The invention mainly solves the problem of long-term interval prediction of production and consumption of by-product gas system in iron and steel industry and learning of model structure

Method used

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  • A Long-term Interval Prediction and Structure Learning Method for Iron and Steel Gas System
  • A Long-term Interval Prediction and Structure Learning Method for Iron and Steel Gas System
  • A Long-term Interval Prediction and Structure Learning Method for Iron and Steel Gas System

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Embodiment Construction

[0024] In order to better understand the technical route and implementation plan of the present invention, the by-product gas system of Shanghai Baoshan Iron and Steel Works, which has a relatively high level of automation in the domestic iron and steel industry, will be further described below. attached by figure 1 It can be seen from the schematic diagram of Baosteel’s gas system that four blast furnaces, six coke ovens and six converters constitute the three main by-product gas generating units, while the consumption units include cold / hot rolling, sintering, etc., among which the low-pressure boiler And power plants are often used as adjustable units. The pipeline network also contains multiple gas cabinets, which serve as temporary storage and buffer. In addition, the gas mixing station and pressurization station are used as the transmission and distribution system, responsible for sending the gas to each consumption unit by pressure. In daily production, maintaining th...

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Abstract

The invention belongs to the field of information technology and provides a long-term interval prediction and structure learning method of a steel gas system. The present invention adopts industrial real data, first constructs multi-level information granularity non-equal-length distribution structure, and establishes a corresponding optimization model; then, considering the importance of model structure to prediction accuracy, the present invention uses the Monte Carlo method to analyze the multi-level model Intensive learning is carried out on the structural parameters; finally, based on the optimal multi-layer granularity calculation structure, the parallel calculation strategy is used to obtain the long-term interval prediction results of gas production and consumption. The accuracy of the results obtained by this method is high, and the calculation efficiency meets the requirements of practical applications. It can also be popularized and applied in other energy medium systems in the iron and steel industry.

Description

technical field [0001] The invention belongs to the field of information technology, relates to technologies such as fuzzy modeling, intensive learning, parallel computing, etc., and is a long-term interval prediction and structure learning method for a steel industry gas system combining granularity calculation and intensive learning. The present invention adopts industrial real data, first constructs multi-level information granularity non-equal-length distribution structure, and establishes a corresponding optimization model; then, considering the importance of model structure to prediction accuracy, the present invention uses the Monte Carlo method to analyze the multi-level model Intensive learning is carried out on the structural parameters; finally, based on the optimal multi-layer granularity calculation structure, the parallel calculation strategy is used to obtain the long-term interval prediction results of gas production and consumption. The accuracy of the results...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06Q50/04G06F16/2458
CPCG06Q10/04G06Q50/04Y02P90/30
Inventor 韩中洋赵珺王伟王霖青
Owner DALIAN UNIV OF TECH