Aluminum electrolytic multi-parameter control method based on BP neural network and MBFO algorithm

A technology of BP neural network and control method, which is applied in the field of multi-parameter control of aluminum electrolysis based on BP neural network and MBFO algorithm, can solve the problems of nonlinearity, low efficiency, and a large amount of greenhouse gases, so as to achieve rapid acquisition and improve production efficiency. Effect

Active Publication Date: 2016-03-16
CHONGQING UNIVERSITY OF SCIENCE AND TECHNOLOGY
View PDF7 Cites 8 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0002] Aluminum electrolysis is a complex industrial production process. The complex chemical changes of materials inside the aluminum electrolytic cell and various external uncertain operating factors lead to many parameters in the cell. The parameters are characterized by nonlinearity and strong coupling, making it difficult to measure in

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 multi-parameter control method based on BP neural network and MBFO algorithm
  • Aluminum electrolytic multi-parameter control method based on BP neural network and MBFO algorithm
  • Aluminum electrolytic multi-parameter control method based on BP neural network and MBFO algorithm

Examples

Experimental program
Comparison scheme
Effect test

Example Embodiment

[0042] The present invention will be further described below in conjunction with the embodiments and drawings.

[0043] Such as figure 1 The key to the multi-parameter control method of aluminum electrolysis based on BP neural network and MBFO algorithm is that it includes the following steps:

[0044] S1: Select control parameters that have an impact on current efficiency, energy consumption per ton of aluminum and perfluoride emissions to form decision variables X = [x 1 ,x 2 ,...X M ], M is the number of selected parameters;

[0045] Through statistics of the original variables that have an impact on current efficiency, energy consumption per ton of aluminum and perfluoride emissions in the aluminum electrolysis production process, and determine the parameters that have the greatest impact on current efficiency, energy consumption per ton of aluminum and perfluoride emissions as decision-making Variable X;

[0046] Through statistics of the measured parameters in the actual indust...

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 invention discloses an aluminum electrolytic multi-parameter control method based on a BP neural network and an MBFO algorithm. Firstly modeling of the aluminum electrolytic process is performed by using the BP neural network, and then aluminum electrolytic production process model parameters are optimized by using an improved multi-target bacterial foraging algorithm so that the optimal solution of a decision variable is obtained, wherein the key of the improved multi-target bacterial foraging algorithm is to use a Pareto entropy external file update strategy to update bacterial floras so that the bacterial floras are enabled to move to the Pareto front at a high speed. The beneficial effects are that the aluminum electrolytic parameters are optimized based on the multi-target bacterial foraging algorithm so that aluminum electrolytic production efficiency can be effectively enhanced; and the Pareto entropy external file update strategy is applied to update the bacterial floras so that the optimal parameters of aluminum electrolytic production can be rapidly acquired.

Description

technical field [0001] The invention relates to the field of optimal control, in particular to a multi-parameter control method for aluminum electrolysis based on BP neural network and MBFO algorithm. Background technique [0002] Aluminum electrolysis is a complex industrial production process. The complex chemical changes of materials inside the aluminum electrolytic cell and various external uncertain operating factors lead to many parameters in the cell. The parameters are characterized by nonlinearity and strong coupling, making it difficult to measure in real time. , Adjustment, brings certain difficulty to the control optimization of aluminum electrolysis production process. The current aluminum electrolysis method consumes a lot of energy and is inefficient, and the production process of aluminum electrolysis will generate a lot of greenhouse gases, causing serious environmental pollution. Therefore, on the premise of ensuring the stable production of aluminum elect...

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
IPC IPC(8): G05B13/02
CPCG05B13/0205
Inventor 易军何海波黄迪李太福陈实周伟张元涛刘兴华
Owner CHONGQING UNIVERSITY OF SCIENCE AND TECHNOLOGY
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap