Optimization method for energy saving and emission reduction of aluminum electrolysis based on decision maker preference information

Abstract

The invention discloses an optimization method for energy conservation and emission reduction of aluminum electrolysis based on decision maker preference information. Firstly, a recursive neural network is used to model an aluminum electrolysis production process, and then a decision maker sets a desired target value, an R-dominance preference dominance method is introduced, a multi-objective quantum particle swarm algorithm is combined to optimize a production process model, and an optimal decision variable that best meets the expectation of the decision maker, and the corresponding current efficiency, cell voltage, perfluorinated compound emissions and the energy consumption per ton of aluminum are further obtained. The MQPSO algorithm does not need to perform crossover and mutation operations, and only requires the simplest location update step, so that the encoding process is simple, a strong global search capability is realized, the integrity of a preference optimal value in the process of population evolution can be easily implemented, and the needs of the decision maker can be met. By using the method to determine the optimal value of process parameters in the aluminum electrolysis production process, the current efficiency can be effectively improved, the cell voltage can be reduced, the greenhouse gas emissions and the energy consumption per ton of aluminum can be reduced, and the purpose of energy conservation and emission reduction can be achieved.

Description

technical field [0001] The invention belongs to the field of optimal control, and in particular relates to an optimization method for energy saving and emission reduction of aluminum electrolysis based on preference information of decision makers. Background technique [0002] The production process of environmentally friendly aluminum electrolysis has been very challenging for a long time. In the electrolytic aluminum industry, the ultimate goal is to improve current efficiency, reduce cell voltage, reduce perfluorinated compounds, and reduce energy consumption per ton of aluminum on the basis of smooth operation of the electrolytic cell emissions. 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 suitable for Solve the nonlinear sy...

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Problems solved by technology

[0003] The present invention proposes an optimization method for energy saving and emission reduction of aluminum electrolysis based on decision maker preference information to solve the problem 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 problems, and at the same time, it can introduce decision-maker preference information to achieve the purpose of dynamically and flexibly adjusting the preference weights between goals

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