Cell Voltage optimization of aluminum electrolysis Based on Neural Network-genetic Algorithm

Authors

Chenhua Xu, School of Electrical Engineering, Guangxi University, Nanning 530004, China;
Li Zhi, School of Electrical Engineering, Guangxi University, Nanning 530004, China;

Abstract

Abstract: In order to reduce the production cost of electrolytic aluminum, an optimization extreme method was proposed based on neural network and genetic algorithm, to find the optimal production cell voltage and the corresponding production conditions. Using kernel principal component analysis method to determine the key parameters affecting of aluminum electrolysis production, a neural network model of electrolytic aluminum was established. Using the genetic algorithm, the global optimal value of the cell voltage of the electrolytic aluminum and the corresponding production conditions were found. The simulation results show that the neural network and genetic algorithm can predict the cell voltage of electrolytic aluminum accurately, at the same time it can find the optimal cell voltage, and the production conditions.

Recommended Citation

Xu, Chenhua and Zhi, Li (2020) "Cell Voltage optimization of aluminum electrolysis Based on Neural Network-genetic Algorithm," Journal of System Simulation: Vol. 28: Iss. 5, Article 17.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol28/iss5/17

First Page

1124

Revised Date

2015-04-03

DOI Link

https://doi.org/

Last Page

1130

CLC

TP391.9

Recommended Citation

Xu Chenhua, Li Zhi. Cell Voltage optimization of aluminum electrolysis Based on Neural Network-genetic Algorithm[J]. Journal of System Simulation, 2016, 28(5): 1124-1130.