Development of a deep neural network and a PSO algorithm to predict ore hardness using X-ray diffraction and atomic emission spectroscopy

• Published in: Minerals engineering Vol. 213; p. 108760
• Main Authors: De Almeida, T., Nicolau, A.S., Schirru, R., Bueno, M.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2024
• Subjects: Artificial Intelligence; Comminution; Deep Neural Network; Geometallurgy; Nuclear Engineering; Particle Swarm Optimization; Spectroscopy; X-ray diffraction; Spectroscopy; Geometallurgy; Deep Neural Network; Nuclear Engineering; Artificial Intelligence; X-ray diffraction; Comminution; Particle Swarm Optimization
• ISSN: 0892-6875; 1872-9444
• DOI: 10.1016/j.mineng.2024.108760

•In the present work, nuclear analytical techniques (XRD and ICP-AES) are used to evaluate ore hardness.•Deep Neural Network (DNN) and Particle Swarm Optimization (PSO) models were developed for two comminution parameters.•The PSO algorithm model performed better to estimate the Drop Weight Index (DWI), with a mean error of 6.8 %.•The DNN model performed better to estimate the Bond Work Index (BWI), with a mean error of 3.8 %.•All four regression models had a coefficient of determination (R2) greater than 0.99 against the reference Geopyörä results. 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 (R2) of 99.5 % for DWI and 99.8 % for BWI. Similarly, the PSO model achieving an R2 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.