Prediction of flotation efficiency of metal sulfides using an original hybrid machine learning model
Prediction of flotation efficiency of metal sulfides using an original hybrid machine learning model
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DOI：
10.1002/eng2.12167
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发表时间：
2020-06-01
影响因子：
2
通讯作者：
Alagha, Lana
中科院分区：
文献类型：
--
作者：
Cook, Rachel;Monyake, Keitumetse Cathrine;Alagha, Lana
泡沫浮选工艺广泛用于贱金属硫化物的选择性分离。可靠地预测工艺结果（金属回收率和品位）对于确保最佳性能至关重要。这项工作采用了一种创新的混合机器学习（ML）模型，通过结合随机森林模型和萤火虫算法来预测方铅矿和黄铜矿的泡沫浮选效率与各种实验工艺参数。严格评估了混合模型的预测性能，并与四种不同的独立ML模型进行了比较。这项研究的结果表明，混合ML模型具有高保真处理结果的预测能力，同时始终优于独立ML模型。
Froth flotation process is extensively used for selective separation of base metal sulfides from uneconomic mineral resources. Reliable prediction of process outcomes (metal recovery and grade) is vital to ensure peak performance. This work employs an innovative hybrid machine learning (ML) model-constructed by combining the random forest model and the firefly algorithm-to predict froth flotation efficiency of galena and chalcopyrite in relation to various experimental process parameters. The hybrid model's prediction performance was rigorously evaluated, and compared against four different standalone ML models. The outcomes of this study illustrate that the hybrid ML model has the prediction ability to process outcomes with high-fidelity, while consistently outperforming the standalone ML models.