In mineral processing plants, grinding is a crucial step, accounting for approximately 50% of the total mineral processing costs. Semi-autogenous grinding (SAG) mills are extensively employed in the grinding circuit of mineral processing plants. Maximising SAG mill throughput is of significant importance considering its profound financial outcomes. However, the optimum process parameter setting aimed at achieving maximum mill throughput remains an uninvestigated domain in prior research. This study introduces an intelligent framework leveraging expert knowledge, machine learning techniques and evolutionary algorithms to address this research need. In this study, an extensive industrial dataset comprising 36,743 records is utilised and relevant features are selected based on the insights of industry experts. Following the removal of erroneous data, an evaluation of 17 machine learning models is undertaken to identify the most accurate predictive model. To improve the performance of the model, feature selection and outlier detection are executed. The resultant optimal model, trained with refined features, serves as the objective function within three distinct evolutionary algorithms. These algorithms are employed to identify parameter configurations that maximise SAG mill throughput while adhering to the working limits of input parameters as constraints. Notably, analysis revealed that CatBoost, as an ensemble model, stands out as the most accurate predictor. Furthermore, differential evolution emerges as the preferred optimisation algorithm, exhibiting superior performance in both achieving the highest mill throughput predictions and ensuring robustness in predictions, surpassing alternative methods.

中文翻译：

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用于最大化半自磨机产量的集成智能框架：结合专家知识、机器学习和进化算法进行参数优化


在选矿厂中，磨矿是至关重要的一步，约占选矿总成本的50%。半自磨 (SAG) 磨机广泛应用于选矿厂的研磨回路中。考虑到其深远的财务成果，最大化半自磨机产量至关重要。然而，旨在实现最大磨机产量的最佳工艺参数设置在先前的研究中仍然是一个未经研究的领域。本研究引入了一个利用专家知识、机器学习技术和进化算法的智能框架来满足这一研究需求。在这项研究中，利用了包含 36,743 条记录的广泛工业数据集，并根据行业专家的见解选择了相关特征。删除错误数据后，将对 17 个机器学习模型进行评估，以确定最准确的预测模型。为了提高模型的性能，执行特征选择和异常值检测。由此产生的最佳模型，经过精细特征的训练，充当三种不同进化算法中的目标函数。这些算法用于识别参数配置，使半自磨机产量最大化，同时遵守输入参数的工作限制作为约束。值得注意的是，分析表明 CatBoost 作为集成模型，是最准确的预测器。此外，差分进化成为首选的优化算法，在实现最高轧机产量预测和确保预测稳健性方面表现出卓越的性能，超越了替代方法。