Mining serves as the initial link in the supply chain for metals in manufacturing, and comminution − the process of reducing ore size through crushing and milling during mineral processing − is notably energy-intensive, accounting for up to 4 % of global power consumption, and its efficient management hinges on the variability within the mineral body, relying heavily on a comprehensive understanding of the ore’s strength and grinding resistance. The assessment of these parameters typically involves comminution tests such as the Geopyörä Breakage Test, the SMC test, and the Bond Ball Mill Grindability test. Additionally, analytical techniques like the X-ray Diffraction (XRD) and the Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP-AES) prove indispensable for the determination of crystalline structures and chemical composition analysis within different segments of the mineral body. This article delves into the application of Deep Neural Network (DNN) and Particle Swarm Optimization (PSO) to predict rock strength and grinding resistance parameters: Drop weight index (DWI) and Bond work index (BWI) of mineral samples using ICP-AES and XRD data in order to develop models capable of unravelling the intricate relationships among material properties, chemical and mineralogical composition, and their collective influence on ore strength and grindability. The current article utilizes a dataset from a real comminution project to evaluate the performance of the DNN and PSO models. The results obtained with both DNN and PSO models are promising and remarkably similar, with the DNN achieving a coefficient of determination (R) of 99.5 % for DWI and 99.8 % for BWI. Similarly, the PSO model achieving an R of 99.4 % for DWI and 99.7 % for BWI when compared directly with reference results, surpassing previous works that employed methodologies based on conventional regression models. Thus, the results found from this study suggest that the use of PSO and DNN is promising in solving this type of problem.

中文翻译：

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开发深度神经网络和 PSO 算法，利用 X 射线衍射和原子发射光谱预测矿石硬度


采矿是制造业金属供应链的初始环节，而粉碎（矿物加工过程中通过破碎和研磨来减小矿石尺寸的过程）尤其是能源密集型，占全球电力消耗的 4%，并且其有效管理取决于矿体内部的可变性，很大程度上依赖于对矿石强度和耐磨性的全面了解。这些参数的评估通常涉及粉碎测试，例如 Geopyörä 破碎测试、SMC 测试和 Bond Ball Mill 可磨性测试。此外，X 射线衍射 (XRD) 和电感耦合等离子体原子发射光谱 (ICP-AES) 等分析技术对于确定矿体不同部分的晶体结构和化学成分分析是必不可少的。本文深入研究了深度神经网络 (DNN) 和粒子群优化 (PSO) 的应用来预测岩石强度和研磨阻力参数：使用 ICP-AES 和矿物样品的落重指数 (DWI) 和粘结功指数 (BWI) XRD 数据，以便开发能够阐明材料特性、化学和矿物成分之间复杂关系及其对矿石强度和可磨性的共同影响的模型。本文利用真实粉碎项目的数据集来评估 DNN 和 PSO 模型的性能。 DNN 和 PSO 模型获得的结果都非常有希望，并且非常相似，DNN 的 DWI 确定系数 (R) 为 99.5%，BWI 的确定系数 (R) 为 99.8%。同样，PSO 模型的 DWI 和 99 的 R 分别为 99.4% 和 99.4%。与参考结果直接比较时，BWI 降低了 7%，超过了之前采用基于传统回归模型的方法的工作。因此，本研究的结果表明，PSO 和 DNN 的使用在解决此类问题方面很有前景。