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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
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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 real time. , adjustment, brings certain difficulties 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

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  • 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

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Embodiment Construction

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

[0043] Such as figure 1 A multi-parameter control method for aluminum electrolysis based on BP neural network and MBFO algorithm is shown, the key of which is to include the following steps:

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

[0045] By counting 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 a decision variable X;

[0046] Through the statistics of the measured paramete...

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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

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Application Information

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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
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