Despite the long history of flotation in the mineral processing industry, its prediction and understanding remains a great challenge owing to its many variables acting in a complex manner. In this study, we introduced machine learning (ML) models to predict the grade and yield of a multi-stage flotation process of a complex lead–zinc sulfide ore. Over 100 batch flotation tests were conducted in a stepwise manner to characterize different rougher-cleaner-scavenger configurations. Performing a pre-flotation of talc prior to sulfide flotation remarkably improved the grade of lead concentrate. The experimental data were divided into four subsets for the ML models: rougher without pre-flotation, rougher with pre-flotation, cleaner, and cleaner-scavenger datasets. Different ML models were evaluated to determine whether they could predict the lead grade, zinc grade, and yield related to key flotation parameters, including particle size, reagent dosage, and pulp pH. The integrated ensemble neural network and random forest model yielded the best prediction results with R values of 0.924, 0.902, 0.973, and 0.894 for the rougher without pre-flotation, rougher with pre-flotation, cleaner, and cleaner-scavenger subsets, respectively. The developed ML model, with the connection of subset models, effectively predicted the flotation outcome of the rougher-cleaner-scavenger circuit, demonstrating a better prediction performance than previous methods. This indicates that the developed ML model can potentially predict flotation process performance and evaluate the efficiency of newly designed multi-stage froth flotation processes.

中文翻译：

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使用集成集成神经网络-随机森林模型预测复杂铅锌硫化矿的多级泡沫浮选效率

尽管浮选在选矿工业中有着悠久的历史，但由于其许多变量以复杂的方式发挥作用，对其预测和理解仍然是一个巨大的挑战。在这项研究中，我们引入了机器学习（ML）模型来预测复杂铅锌硫化矿石的多阶段浮选过程的品位和产量。以逐步的方式进行了 100 多次批量浮选测试，以表征不同的粗选-精选-扫选配置。在硫化物浮选之前对滑石进行预浮选可显着提高铅精矿的品位。 ML 模型的实验数据分为四个子集：不带预浮选的粗选数据集、带预浮选的粗选数据集、清洁数据集和清洁清除数据集。对不同的 ML 模型进行了评估，以确定它们是否可以预测与关键浮选参数（包括粒度、试剂用量和矿浆 pH 值）相关的铅品位、锌品位和产量。集成的集成神经网络和随机森林模型产生了最佳预测结果，无预浮选粗选机、预浮选粗选机、清洁剂和清洁清除剂子集的 R 值分别为 0.924、0.902、0.973 和 0.894。开发的机器学习模型通过子集模型的连接，有效地预测了粗选-清选-扫选回路的浮选结果，表现出比以前的方法更好的预测性能。这表明所开发的机器学习模型可以预测浮选工艺性能并评估新设计的多级泡沫浮选工艺的效率。