Differential evolution aluminum electrolysis multi-objective optimization method based on AR preference information

A technology of multi-objective optimization and preference information, applied in instruments, adaptive control, control/regulation systems, etc., can solve problems such as polluting the environment, high energy consumption, and low efficiency

Active Publication Date: 2018-08-03
CHONGQING UNIVERSITY OF SCIENCE AND TECHNOLOGY
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Problems solved by technology

[0003] The present invention proposes a differential evolution multi-objective optimization method for aluminum electrolysis based on AR preference information to solve the huge energy consumption, low efficiency and serious environmental pollution caused by the inability to obtain optimal process parameters in the production process of aluminum electrolysis in the prior art technical problems, and at the same time, it can introduce decision-maker preference information to achieve dynamic and flexible adjustment of preference weights between goals, so as to meet the purpose of decision-makers' real-time preference needs

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  • Differential evolution aluminum electrolysis multi-objective optimization method based on AR preference information
  • Differential evolution aluminum electrolysis multi-objective optimization method based on AR preference information
  • Differential evolution aluminum electrolysis multi-objective optimization method based on AR preference information

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Embodiment Construction

[0051] like figure 1 As shown, a differential evolution multi-objective optimization method for aluminum electrolysis based on AR preference information includes the following steps:

[0052] S1: Select the control parameters that affect the current efficiency, cell voltage, perfluoride emissions and energy consumption per ton of aluminum to form a decision variable X=[x 1 ,x 2 ,···,x M ], M is the number of selected control parameters;

[0053] This embodiment is to calculate the original variables that have an impact on current efficiency, cell voltage, perfluoride emissions and energy consumption per ton of aluminum in the aluminum electrolysis production process, and determine the impact on current efficiency, cell voltage, perfluoride emissions and The parameter with the greatest impact on the energy consumption per ton of aluminum is taken as the decision variable X.

[0054] In this embodiment, by making statistics on the measured parameters in the actual industrial...

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Abstract

The present invention provides a differential evolution aluminum electrolysis multi-objective optimization method based on AR preference information. The method comprises the steps of: employing a recurrent neural network to perform modeling of an aluminum electrolysis production process, setting an expected target value by a decider, employing a preference multi-objective quantum individual groupalgorithm to perform optimization of a production process model to obtain one set of optimal solutions, which the best meet the decider's expectation, of each decision variable, and current efficiencies, cell voltages, perfluorinated compound discharge and ton aluminum energy consumption corresponding to the optimal solutions. The variation, intersection and selection operation in a differentialevolution algorithm are employed to perform preference optimizing of decision variables to determine optimal values of process parameters in the aluminum electrolysis production process so as to effectively improve the current efficiency, reduce the cell voltages, reduce the greenhouse gas emissions and ton aluminum energy consumption and achieve the purpose of energy conservation and emission reduction while meeting the decider's preference.

Description

technical field [0001] The invention belongs to the field of optimal control, and in particular relates to a differential evolution multi-objective optimization method for aluminum electrolysis based on AR preference information. Background technique [0002] The environmentally friendly production process of aluminum electrolysis has long been valued, but it is very challenging. In the electrolytic aluminum industry, the ultimate goal is to improve the current efficiency, reduce the voltage of the cell, reduce the emission of perfluorinated compounds, and reduce the emission of energy consumption per ton of aluminum on the basis of the smooth operation of the electrolytic cell. However, there are many parameters of the aluminum electrolytic cell, and the parameters are nonlinear and strongly coupled, which brings great difficulty to the modeling of the aluminum electrolytic production process. The recurrent neural network has a strong nonlinear mapping ability and is suitab...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 白竣仁易军李倩陈雪梅吴凌周伟
Owner CHONGQING UNIVERSITY OF SCIENCE AND TECHNOLOGY
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