BP neural network and MBFO algorithm-based aluminum electrolysis energy conservation and emission reduction control method

A BP neural network, energy saving and emission reduction technology, applied in the direction of neural learning method, biological neural network model, etc., can solve the problems of high energy consumption, environmental pollution, low efficiency, etc., to achieve the effect of improving current efficiency and reducing emissions

Active Publication Date: 2016-03-23
CHONGQING UNIVERSITY OF SCIENCE AND TECHNOLOGY
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Problems solved by technology

[0003] This application provides a control method for energy saving and emission reduction of aluminum electrolysis based on BP neural network and

Method used

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  • BP neural network and MBFO algorithm-based aluminum electrolysis energy conservation and emission reduction control method
  • BP neural network and MBFO algorithm-based aluminum electrolysis energy conservation and emission reduction control method
  • BP neural network and MBFO algorithm-based aluminum electrolysis energy conservation and emission reduction control method

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Embodiment

[0047] Such as figure 1 As shown, a control method for energy saving and emission reduction of aluminum electrolysis based on BP neural network and MBFO algorithm includes the following steps:

[0048] S1: Select control parameters that have an impact on current efficiency and greenhouse gas emissions to form a decision variable X=[x 1 ,x 2 ,...,x M ], M is the number of selected parameters;

[0049] The implementation is to count the original variables that have an impact on the current efficiency and greenhouse gas emissions in the aluminum electrolysis production process, and determine the parameters that have a large impact on the current efficiency and greenhouse gas emissions as the decision variable X;

[0050] Through the statistics of the measured parameters in the actual industrial production process, the variables that have the greatest impact on current efficiency and greenhouse gas emissions are: series current x 1 , times of feeding x 2 , Molecular ratio x ...

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Abstract

The invention provides a BP neural network and MBFO algorithm-based aluminum electrolysis energy conservation and emission reduction control method. The method comprises the following steps: carrying out modeling on the aluminum electrolysis production process by utilizing a BP neural network; and optimizing the production process model by utilizing a crowding distance-based multi-target bacterial foraging optimization algorithm so as to obtain a group of optimum solutions of each decision variable as well as current efficiencies and greenhouse gas emissions corresponding to the optimum solutions, wherein the crowding distances of the non-inferior solutions need to be calculated during the optimization of the production process model and the external files are updated according to the crowding distances so that the floras rapidly move toward the target to ensure quick convergence under the premise of ensuring the population diversity. According to the method, the optimum values of the process parameters in the aluminum electrolysis production process are determined, the current efficiency is effectively improved and the greenhouse gas emission is decreased, so that the aim of conserving energy and reducing emission are really achieved.

Description

technical field [0001] The invention relates to an automatic control technology in the production process of aluminum electrolysis, in particular to a control method for energy saving and emission reduction of aluminum electrolysis based on BP neural network and MBFO algorithm. Background technique [0002] Aluminum electrolysis is a complicated industrial production process, which is usually smelted by the Bayer process. However, this method consumes a lot of energy and has low efficiency. At the same time, a large amount of greenhouse gases will be generated in the process of aluminum electrolysis, 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, comple...

Claims

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Application Information

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IPC IPC(8): G06N3/08C25C3/20
CPCC25C3/20G06N3/084G06N3/086
Inventor 黄迪易军陈实李太福何海波周伟张元涛刘兴华
Owner CHONGQING UNIVERSITY OF SCIENCE AND TECHNOLOGY
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