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A Coke Quality Index Prediction Method Based on Multilayer Neural Network

A multi-layer neural network, coke quality technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve problems such as quality index prediction

Active Publication Date: 2022-04-19
BAOTOU IRON & STEEL GRP +1
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0005] Therefore, the object of the invention is to provide a coke quality index prediction method based on multilayer neural network, to solve the quality index prediction problem in the coke production process of iron and steel enterprises

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  • A Coke Quality Index Prediction Method Based on Multilayer Neural Network
  • A Coke Quality Index Prediction Method Based on Multilayer Neural Network
  • A Coke Quality Index Prediction Method Based on Multilayer Neural Network

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Embodiment Construction

[0028] The specific embodiment of the present invention is described below in conjunction with accompanying drawing:

[0029] The present invention adopts industrial actual production data, first cleans the data, uses gradient enhanced tree to conduct correlation analysis on factors affecting coke quality indicators, and selects the variables most relevant to ash content, sulfur content, M10, M40, CRI and CSR, etc. , and then construct training samples, establish a multi-layer neural network prediction model to predict the coke quality index, and use the intelligent optimization algorithm to optimize the variables in the model, and give the final prediction result of the coke quality index. The invention can predict the quality index of coke with high precision, meet the needs of industrial production, provide data support and guidance for subsequent production, and can also be extended to other coking product industries.

[0030] (1) Correlation analysis affecting coke qualit...

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Abstract

The invention discloses a coke quality index prediction method based on a multilayer neural network, which belongs to the technical field of industrial information. Using the actual industrial production data, the data is firstly cleaned, and the correlation analysis of the factors affecting the coke quality index is carried out by using the gradient enhanced tree, and the factors related to the ash, sulfur, M 10 , M 40 , CRI, CSR and other most relevant variables, and then construct training samples, establish a multi-layer neural network prediction model to predict coke quality indicators, and use intelligent optimization algorithms to optimize the variables in the model to give the final coke quality indicators forecast result. The invention can predict the coke quality index with high precision, meet the needs of industrial production, provide data support and guidance for subsequent production, and can also be extended to other coking product industries.

Description

technical field [0001] The invention specifically relates to a coke quality index prediction method based on a multilayer neural network, which belongs to the field of industrial information technology. Background technique [0002] Coke, as an important solid fuel in the production process of the iron and steel industry, is obtained from coal under high temperature conditions through dry distillation and other processes. At present, the coal blending in coking plants is basically high-quality coking coal such as fat coal and coking coal. .(2018). Research on Coking Coal Analysis and Coke Quality Prediction. (Doctoral dissertation)). From the perspective of the existing production technology and the distribution of coal resources in my country, gas coal, 1 / 3 coking coal and other weakly caking coals and medium caking coals are high-quality coals in the coking process, and their reserves and output are relatively large, but in actual coking The proportion in production is lo...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/06G06Q50/04G06K9/62G06N3/04G06N3/08G06N20/00
CPCG06Q10/06393G06Q10/06395G06Q50/04G06N3/08G06N20/00G06N3/045G06F18/24323Y02P90/30
Inventor 芦建文王宏卢培山江鑫王勇付利俊贾晓宗欧宇星资金凯
Owner BAOTOU IRON & STEEL GRP
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