Multi-objective optimization driven by preponderant individuals and symmetric sampling for operational parameter design in aluminum electrolysis process
Author:
Funder
Chongqing Normal University
Natural Science Foundation of Chongqing Municipality
Natural Science Foundation Project of Chongqing
National Natural Science Foundation of China
Chongqing Municipal Education Commission
Publisher
Elsevier BV
Reference61 articles.
1. Comprehensive assessment of energy conservation and CO2 emission reduction in future aluminum supply chain;Tian;Appl. Energy,2022
2. Technologies for CO2 emission reduction and low-carbon development in primary aluminum industry in China: A review;Shen;Renew. Sustain. Energy Rev.,2024
3. Dynamic evolutionary model based on a multi-sampling inherited HAPFNN for an aluminium electrolysis manufacturing system;Ding;Appl. Soft Comput.,2021
4. An improved feed-forward neural network based on UKF and strong tracking filtering to establish energy consumption model for aluminum electrolysis process;Yao;Neural Comput. Appl.,2019
5. Constructing a stable interface film on both cathode and anode via a novel electrolyte additive for high performance LiNi0.8Co0.1Mn0.1O2/graphite pouch cell;Li;J. Power Sources,2024
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