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Metallurgy production process dynamic cost control method based on neural network

A cost control and neural network technology, applied in biological neural network models, special data processing applications, instruments, etc., can solve the problem of not being able to grasp the trend of cost changes in time, analyze the reasons for cost fluctuations, and not be able to discover the cost and cost target setting value in real time Various differences and the causes of the differences, the inability to carry out pre-event and in-event control in time, etc., to achieve the effect of reducing costs and improving the competitiveness of enterprises

Inactive Publication Date: 2004-11-03
NORTHEASTERN UNIV
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  • Abstract
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  • Application Information

AI Technical Summary

Problems solved by technology

[0002] The globalized market competition of the process industry is becoming increasingly fierce, mainly manifested in cost competition, product quality competition, and sales service competition. Among them, cost competition is the most basic and most critical. In the fierce market competition, cost reduction has become an important aspect of metallurgical enterprises. An important means and approach of competitiveness. The cost index is a comprehensive index to measure the production and operation activities of the enterprise. In most industrial enterprises, cost control is basically completed by manual quotations and other methods. Without real-time collection of complete cost data, it is impossible to discover various Various differences between the actual cost of the process and link and the cost target setting value and the reasons for the difference, so it is impossible to carry out pre-event and in-event control in time
[0003] After domestic patent search, no relevant patent application was found. After world patent search, relevant patent documents were detected, one of which is: about the physical properties, operation and The parameters of the equipment used are used to control the cost and the manufacturing process; the disadvantages: the trend of cost changes cannot be grasped in time, the reasons for cost fluctuations can not be analyzed, and the dynamic online control of costs cannot be realized

Method used

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  • Metallurgy production process dynamic cost control method based on neural network
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  • Metallurgy production process dynamic cost control method based on neural network

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specific Embodiment approach

[0019] The production index factors affecting the beneficiation process of a concentrator mainly include lump ore rate, waste rock rate, weak magnetic tailings grade, strong magnetic tailings grade, raw ore grade, etc. These factors directly affect the cost of mineral processing and refining. This implementation For example, based on the statistical data of these factors, a three-layer neural network BP model is constructed as the basic model for analyzing the impact of 5 factors such as the lump ore rate on the cost of comprehensive refinement. .

[0020] Table 1 Raw data of comprehensive cost factors

[0021] Table 2 Sample set after preprocessing

[0022] Table 3 Analysis results of factors affecting comprehensive precision cost

[0023] Factor Lump ore rate Waste rock yield Weak magnetic tailings grade Strong magnetic tailings grade Raw ore grade Comprehensive refining cost

Sample X 1 %X 2 %X 4 %X 5 %X 6 %Y$

1 65.00 11.38 17.07 ...

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Abstract

The present invention is dynamic cost controlling method for metallurgical production process under network environment and by means of concentrated and distributed control technology, dynamic cost management technology, intelligent nerve network technology, etc. The system structure consists of hardware system and software system, the hardware system includes exchanger, server, user terminals, dynamic data acquisition system, control network and enterprise network, and the software system includes application platform software, data base, information collection and interface software. The key algorithm in dynamic cost control software package utilizes nerve network in dynamic cost prediction and analysis. The present invention can lower production cost of production enterprise.

Description

Technical field [0001] The invention relates to a method for realizing dynamic cost control in the metallurgical production process by adopting intelligent control technologies such as distributed control technology, dynamic cost management technology and neural network under the network environment. Background technique [0002] The globalized market competition of the process industry is becoming increasingly fierce, mainly manifested in cost competition, product quality competition, and sales service competition. Among them, cost competition is the most basic and most critical. In the fierce market competition, cost reduction has become an important aspect of metallurgical enterprises. An important means and approach of competitiveness. The cost index is a comprehensive index to measure the production and operation activities of the enterprise. In most industrial enterprises, cost control is basically completed by manual quotations and other methods. Without real-time coll...

Claims

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Application Information

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IPC IPC(8): G06N3/02
Inventor 柴天佑史大为刘威李小平
Owner NORTHEASTERN UNIV
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