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MOEA/D algorithm based aluminum electrolysis production optimization method

An optimization method and technology of aluminum electrolysis, applied in computing, electrical digital data processing, special data processing applications, 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

Inactive Publication Date: 2016-02-03
CHONGQING UNIVERSITY OF SCIENCE AND TECHNOLOGY
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Problems solved by technology

[0003] This application provides an optimization method for aluminum electrolysis production based on the MOEA / D algorithm to solve the problems of huge energy consumption, low efficiency and serious environmental pollution caused by the inability to obtain optimal process parameters in the aluminum electrolysis production process in the prior art technical problem

Method used

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  • MOEA/D algorithm based aluminum electrolysis production optimization method
  • MOEA/D algorithm based aluminum electrolysis production optimization method
  • MOEA/D algorithm based aluminum electrolysis production optimization method

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Embodiment

[0044] Such as figure 1 Shown, a kind of aluminum electrolytic production optimization method based on MOEA / D algorithm is characterized in that, comprises the following steps:

[0045] S1: Count 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, and determine the parameters that have a greater impact on current efficiency, energy consumption per ton of aluminum, and perfluoride emissions as decision variable X;

[0046]The current efficiency y is obtained by statistics of the measured parameters in the actual industrial production process 1 and greenhouse gas emissions y 2 The most influential variables are: series current x 1 , Normal feeding times x 2 , Molecular ratio x 3 , aluminum output x 4 , aluminum level x 5 , electrolyte level x 6 , bath temperature x 7 , cell voltage x 8 There are 8 variables in total.

[0047] S2: Collect ...

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Abstract

The invention provides an MOEA / D algorithm based aluminum electrolysis production optimization method. The method comprises: at first, modeling an aluminum electrolysis production process by utilizing a BP neural network; and then, optimizing a production process model by utilizing an MOEA / D algorithm to obtain a group of optimal solutions of decision variables as well as current efficiency, ton aluminum energy consumption and perfluoro-compound discharge amount corresponding to the optimal solutions, wherein the MOEA / D algorithm decomposes multi-target optimization into a plurality of single-target optimization sub-problems, and a core idea of the MOEA / D algorithm is that information of other sub-problems adjacent to one sub-problem is obtained and then cooperative optimization is performed, so that the optimization speed of multi-target optimization is greatly increased. The method determines an optimal value of process parameters in the aluminum electrolysis production process, effectively improves the current efficiency, reduces the greenhouse gas discharge amount, and truly achieves the purposes of energy conservation and discharge reduction.

Description

technical field [0001] The invention relates to the field of optimal control, in particular to an aluminum electrolytic production optimization method based on MOEA / D algorithm. Background technique [0002] Environmentally friendly aluminum electrolysis production process has long been a challenging issue. In the electrolytic aluminum industry, the ultimate goal is to improve the current efficiency, reduce the energy consumption per ton of aluminum and reduce the emission of perfluorinated compounds on the basis of the smooth operation of the electrolytic cell. However, this goal is very difficult to achieve because the aluminum electrolytic There are many tank parameters, and the parameters show nonlinearity and strong coupling, which brings great difficulty to the modeling of aluminum electrolysis production process. BP neural network has a strong nonlinear mapping ability, which is suitable for solving nonlinear system modeling problems, and provides a new idea for the ...

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Application Information

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IPC IPC(8): G06F17/50
Inventor 陈实易军黄迪何海波李太福周伟张元涛刘兴华
Owner CHONGQING UNIVERSITY OF SCIENCE AND TECHNOLOGY
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