Journal Article10.1016/j.seppur.2022.122430
Machine-learning-assisted multi-objective optimization in vertical zone refining of ultra-high purity indium
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TL;DR: In this article , a multi-objective optimization strategy was proposed to optimize the process parameters for vertical zone refining of 7N-grade ultra-high purity indium (In) by using machine learning.
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About: This article is published in Separation and Purification Technology. The article was published on 01 Oct 2022. The article focuses on the topics: Refining (metallurgy) & Indium.
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Citations
Multi-objective optimization of water-alternating flue gas process using machine learning and nature-inspired algorithms in a real geological field
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Security evaluation of China's indium industrial chain: Perspective on substance flow throughout the whole life cycle
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Machine learning-based multi-objective parameter optimization for indium electrorefining
Hong-Qiang Fan,Xuan Zhu,Hong-Xing Zheng,Peng Lu,Mei-Zhen Wu,Ju-Bo Peng,He-Sheng Zhang,Quan Qian +7 more
TL;DR: A machine learning-based approach integrates support vector regression and multi-objective genetic algorithm to optimize indium electrorefining parameters, achieving high-purity indium with optimized Cu and Pb concentrations through experimental validation.
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