Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Aluminum electrolytic process parameter optimization method based on BP neural network and MOBFOA algorithm

A BP neural network and process parameter optimization technology, applied in neural learning methods, biological neural network models, calculations, etc., can solve problems such as difficulty in control optimization, many parameters in the tank, difficult real-time measurement and adjustment of parameters, etc., and achieve nonlinearity The effect of strong mapping ability, reduction of energy consumption per ton of aluminum, and reduction of perfluorinated compound emissions

Active Publication Date: 2016-02-10
CHONGQING UNIVERSITY OF SCIENCE AND TECHNOLOGY
View PDF1 Cites 13 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, complex material chemical changes inside the aluminum electrolytic cell and various external uncertain operating factors lead to many parameters in the cell, and the parameters are characterized by nonlinearity and strong coupling, and parameters such as pole distance and insulation material thickness are difficult Real-time measurement and adjustment bring certain difficulties to the control optimization of aluminum electrolysis production process

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Aluminum electrolytic process parameter optimization method based on BP neural network and MOBFOA algorithm
  • Aluminum electrolytic process parameter optimization method based on BP neural network and MOBFOA algorithm
  • Aluminum electrolytic process parameter optimization method based on BP neural network and MOBFOA algorithm

Examples

Experimental program
Comparison scheme
Effect test

Embodiment

[0052] From figure 1 It can be seen that a method for optimizing aluminum electrolysis process parameters based on BP neural network and MOBFOA algorithm is characterized in that it includes the following steps:

[0053] S1: Select control parameters that affect current efficiency, energy consumption per ton of aluminum, and perfluorinated compound emissions to form a decision variable X=[x 1 ,x 2 ,...,x M ], M is the number of selected parameters;

[0054] In the implementation process, the original variables that have an impact on current efficiency, energy consumption per ton of aluminum, and perfluoride emissions in the production process of aluminum electrolysis are counted, and the impact on current efficiency, energy consumption per ton of aluminum in the production process of aluminum electrolysis is determined from them. Consumption and perfluorinated compounds emissions have the greatest impact as the decision variable X;

[0055] Through the statistics of the me...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The present invention discloses an aluminum electrolytic process parameter optimization method based on a BP neural network and an MOBFOA algorithm, comprising the following steps of: 1, carrying out statistics on a parameter with large influence on current efficiency, ton aluminum energy consumption and perfluorinated compound emission and using the parameter as a decision variable X; 2, by using the BP neural network, establishing an aluminum electrolytic production process model; 3,, by using the MOBFOA algorithm, performing optimization on the decision variable in a value range of the decision variable; and 4, according to the optimal decision variable, performing field control. The aluminum electrolytic process parameter optimization method has advantages that the aluminum electrolytic production process model is established by using the BP neural-network with a strong non-linear mapping ability; and the optimization method directs flora to jump out of local optimization, the optimal production process parameter can be rapidly obtained, and the aims of high efficiency, consumption reduction and emission reduction are fulfilled.

Description

technical field [0001] The invention relates to the field of aluminum electrolysis industrial production, in particular to an aluminum electrolysis process parameter optimization method based on BP neural network and MOBFOA algorithm. Background technique [0002] Aluminum electrolysis is a complex industrial production process. During the production process, a large amount of greenhouse gases will be generated, causing serious environmental pollution. Therefore, on the premise of ensuring the stable production of aluminum electrolytic cells, how to improve current efficiency, reduce energy consumption, and reduce pollutant gas emissions to achieve high efficiency, energy saving, and emission reduction has become the production goal of aluminum electrolysis enterprises. However, complex material chemical changes inside the aluminum electrolytic cell and various external uncertain operating factors lead to many parameters in the cell, and the parameters are characterized by n...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06N3/08
Inventor 易军黄迪李太福何海波周伟张元涛陈实刘兴华
Owner CHONGQING UNIVERSITY OF SCIENCE AND TECHNOLOGY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com