Flotation separation is the most important upgrading critical raw material technique. Measuring interactions within flotation variables and modeling their metallurgical responses (grade and recovery) is quite challenging on the industrial scale. These challenges are because flotation separation includes several sub-micron processes, and their monitoring won’t be possible for the processing plants. Since many flotation plants are still manually operating and maintaining, understanding interactions within operational variables and their effect on the metallurgical responses would be crucial. As a unique approach, this study used the “Conscious Lab” concept for modeling flotation responses of an industrial copper upgrading plant when Potassium Amyl Xanthate substituted the secondary collector (Sodium Ethyl Xanthate) in the process. The main aim is to understand and compare interactions before and after the collector substitution. For the first time, the conscious lab was constructed based on the most advanced explainable artificial intelligence model, Shapley Additive Explanations, and Catboost. Catboost- Shapley Additive Explanations could accurately model flotation responses (less than 2% error between actual and predicted values) and illustrate variations of complex interactions through the substitution. Through a comparative study, Catboost could generate more precise outcomes than other known artificial intelligence models (Random Forest, Support Vector Regression, Extreme Gradient Boosting, and Convolutional Neural Network). In general, substituting Sodium Ethyl Xanthate by Potassium Amyl Xanthate reduced process predictability, although Potassium Amyl Xanthate could slightly increase the copper recovery.

中文翻译：

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CatBoost-SHAP 用于对工业操作浮选变量进行建模 – 一种“有意识的实验室”方法


浮选是最重要的关键原料升级技术。在工业规模上，测量浮选变量之间的相互作用并对其冶金响应（品位和回收率）进行建模非常具有挑战性。这些挑战是因为浮选分离包括多个亚微米过程，加工厂无法对其进行监控。由于许多浮选厂仍在手动操作和维护，因此了解操作变量之间的相互作用及其对冶金响应的影响至关重要。作为一种独特的方法，本研究使用“意识实验室”概念来模拟工业铜提纯厂在工艺中用戊基黄原酸钾替代二级捕收剂（乙基黄原酸钠）时的浮选反应。主要目的是了解和比较收集器替换前后的交互。意识实验室首次基于最先进的可解释人工智能模型、Shapley Additive Explanations 和 Catboost 构建。 Catboost-Shapley Additive Explanations 可以准确地模拟浮选响应（实际值和预测值之间的误差小于 2%），并通过替代说明复杂相互作用的变化。通过比较研究，Catboost 可以比其他已知的人工智能模型（随机森林、支持向量回归、极限梯度提升和卷积神经网络）生成更精确的结果。一般来说，用戊基黄原酸钾替代乙基黄原酸钠会降低工艺的可预测性，尽管戊基黄原酸钾可以稍微提高铜的回收率。