Multi-objective optimization algorithm for aluminum electrolysis preference based on angle dominance relationship
A technology of multi-objective optimization and dominance relationship, applied in the field of aluminum electrolysis preference multi-objective optimization algorithm, can solve problems such as polluting the environment, high energy consumption, low efficiency, etc., to reduce energy consumption per ton of aluminum, reduce emissions, and reduce cell voltage Effect
Active Publication Date: 2020-09-29
CHONGQING UNIVERSITY OF SCIENCE AND TECHNOLOGY
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[0003] The present invention proposes a multi-objective optimization algorithm for aluminum electrolysis preferences based on the angle dominance relationship 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 production process of aluminum electrolysis in the prior art technical issues
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[0048] Such as figure 1 As shown, a multi-objective optimization algorithm for aluminum electrolysis preferences based on angular dominance includes the following steps:
[0049] S1: Choose control parameters that have an impact on current efficiency, cell voltage and perfluoride emissions to form decision variables X = [x 1 ,x 2 ,···,X M ], M is the number of selected control parameters.
[0050] This embodiment is based on statistics of the original variables that have an impact on current efficiency, cell voltage, perfluoride emissions and energy consumption per ton of aluminum during the aluminum electrolysis production process, and determine the effects on current efficiency, cell voltage, and perfluoride emissions. The parameter that has a large impact on energy consumption per ton of aluminum is used as the decision variable X.
[0051] In this embodiment, through statistics of the measured parameters in the actual industrial production process, the variables with the largest...
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The invention discloses an aluminum electrolysis preference multi-target optimization algorithm based on the angle dominance relation. Firstly, modeling is conducted on the aluminum electrolysis production process through a recurrent neural network; then, an expected target value is set by a decision maker; and then, a production process model is optimized through a preference multi-target quantumindividual group algorithm, and a set of optimal solutions, meeting expectation of the decision maker best, of all decision variables and the current efficiency, the bath voltage, the perfluorocompound emission amount and per-ton aluminum energy consumption which correspond to the optimal solutions are obtained. Through variation, crossing and selecting operation in a differential evolution algorithm, the decision variables are subjected to preference optimizing, thus the optimal value of technological parameters in the aluminum electrolysis production process is determined, the current efficiency can be effectively improved, the bath voltage is lowered, the greenhouse gas emission amount and per-ton aluminum energy consumption are reduced, and the purposes of energy conservation and emission reduction are achieved while preference of the decision maker is met.
Description
Technical field [0001] The invention belongs to the field of optimal control, and specifically relates to a multi-objective optimization algorithm for aluminum electrolysis preference based on an angle dominance relationship. Background technique [0002] The environmentally-friendly aluminum electrolysis production process has been valued for a long time, but it is very challenging. In the electrolytic aluminum industry, the ultimate goal is to improve current efficiency, reduce cell voltage, reduce perfluoride, and reduce energy consumption per ton of aluminum on the basis of smooth operation of the electrolytic cell. However, aluminum electrolysis cell has many parameters, and the parameters show nonlinearity and strong coupling, which brings great difficulty to the modeling of aluminum electrolysis production process, and the recurrent neural network has strong nonlinear mapping ability and is suitable for Solving the problem of nonlinear system modeling provides a new idea ...
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IPC IPC(8): G05B13/04G06N3/08C25C3/20
CPCC25C3/20G05B13/042G06N3/084Y02P80/10
Inventor 易军白竣仁陈雪梅吴凌周伟陈实
Owner CHONGQING UNIVERSITY OF SCIENCE AND TECHNOLOGY