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Aluminum electrolysis production process multi-objective optimization method based on adaptive-step bacterial foraging algorithm

A bacterial foraging algorithm and self-adaptive step size technology, applied in the multi-objective optimization field of aluminum electrolysis production process, can solve problems such as low efficiency, difficult real-time measurement and adjustment, high energy consumption, etc., and achieve the effect of improving production efficiency

Active Publication Date: 2016-03-23
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

Method used

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  • Aluminum electrolysis production process multi-objective optimization method based on adaptive-step bacterial foraging algorithm
  • Aluminum electrolysis production process multi-objective optimization method based on adaptive-step bacterial foraging algorithm
  • Aluminum electrolysis production process multi-objective optimization method based on adaptive-step bacterial foraging algorithm

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Embodiment Construction

[0037]The present invention will be further described below in conjunction with the embodiments and accompanying drawings.

[0038] Such as figure 1 A multi-objective optimization method for the production process of aluminum electrolysis based on the adaptive step-size bacterial foraging algorithm is shown, the key of which is to include the following steps:

[0039] S1: Select 3 aluminum electrolysis production indicators Y=[y 1 ,y 2 ,y 3 ], including: current efficiency, energy consumption per ton of aluminum and emissions of perfluorinated compounds;

[0040] Select 8 parameters X=[x 1 ,x 2 ,...x 8 ], including: series current, NB times, molecular ratio, aluminum output, aluminum level, electrolyte level, bath temperature, bath voltage.

[0041] S2: Using the parameter X as input and the production index Y as output, use the BP neural network to model the aluminum electrolysis process to obtain an aluminum electrolysis model; the BP neural network modeling process i...

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Abstract

The invention discloses an aluminum electrolysis production process multi-objective optimization method based on an adaptive-step bacterial foraging algorithm. The method includes the following steps that firstly, an aluminum electrolysis production index Y is determined, and a parameter X which has the largest influence on the aluminum electrolysis production index is selected; then the parameter X serves as input, the production index Y serves as output, and modeling is carried out on the aluminum electrolysis process through a back propagation (BP) neutral network, so that an aluminum electrolysis model is obtained; and afterwards, the output Y of aluminum electrolysis is used as a fitness function, the bacterial advancing step length is adjusted in a self-adaptive mode on the basis of Pareto differential entropy, the parameter X is optimized within a value range of the parameter X through the bacterial foraging algorithm, and therefore the optimal aluminum electrolysis production process parameter is obtained. The aluminum electrolysis production process multi-objective optimization method has the beneficial effects that the aluminum electrolysis parameter is optimized based on the bacterial foraging algorithm, so that the aluminum electrolysis production efficiency is effectively improved; the bacterial advancing step length is adjusted in an adaptive-step mode, so that the bacterial foraging algorithm is effectively protected against the locally optimal solution; the flora step length is dynamically adjusted through the Pareto differential entropy, and therefore the optimal parameter of aluminum electrolysis production can be quickly obtained.

Description

technical field [0001] The invention relates to the field of optimal control, in particular to a multi-objective optimization method for aluminum electrolysis production process based on an adaptive step size bacterial foraging 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 t...

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Application Information

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IPC IPC(8): C25C3/20
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
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