Enhancing flotation of oxidized copper ores through the integration of artificial neural network and the design of experiments approach for process optimization

Q1 Environmental Science
Hassan Oumesaoud , Rachid Faouzi , Khalid Naji , Intissar Benzakour , Hakim Faqir , Rachid Oukhrib , Moulay Abdelazize Aboulhassan
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引用次数: 0

Abstract

This study tackles the challenge of low copper recovery rates in supergene zones where copper oxides are associated with iron oxides. An artificial neural network (ANN) model was developed, achieving high accuracy (R2 = 0.866) to optimize flotation processes for oxidized copper ores. Shapley values ranked sulfidizing agent (NaHS) and collector dosage (PAX) as the most influential factors, with NaHS and iron negatively affecting recovery, while PAX and copper oxide content had positive effects. Optimal conditions were validated on an industrial scale, achieving 75.66 % copper recovery, confirming the effectiveness of the optimized parameters through mineralogical analysis.

Abstract Image

采用人工神经网络与实验设计相结合的方法优化氧化铜矿浮选工艺
本研究解决了铜氧化物与氧化铁相关的表生带铜回收率低的挑战。建立了人工神经网络(ANN)模型,对氧化铜矿浮选工艺进行优化,准确率较高(R2 = 0.866)。Shapley值显示,硫化剂(NaHS)和捕收剂用量(PAX)对回收率影响最大,其中NaHS和铁对回收率有负影响,PAX和氧化铜含量对回收率有积极影响。在工业规模上验证了优化条件,铜回收率达到75.66%,矿物学分析证实了优化参数的有效性。
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来源期刊
Case Studies in Chemical and Environmental Engineering
Case Studies in Chemical and Environmental Engineering Engineering-Engineering (miscellaneous)
CiteScore
9.20
自引率
0.00%
发文量
103
审稿时长
40 days
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