A stereological correction method employing an artificial neural network for mineral liberation assessment of ore particles. (1st October 2020) - Ueda, Takao; Oki, Tatsuya
Graphical abstract: Highlights: A stereological correction method using an artificial neural network was developed. Its advantages are its small-sized input data requirement and speedy computation. It was experimentally validated using X-ray CT analysis of artificial particles. Its performance was compared to the previously developed texture method. Abstract: Mineral liberation and surface exposure, which are important features of processing crushed ore particles in minerals, are generally assessed in terms of cumulative distributions (such as volumetric composition distribution). These distributions are, however, distorted by stereological bias when they are determined by two-dimensional methods such as microscopy or by scanning electron microscopes or energy dispersive X-ray based analyzers. Thus, in the present study, a stereological correction method using an artificial neural network (ANN) was developed. Through preliminary and parametric investigation of the ANN, the network was designed with 12 neurons in each of the input and output layers, and 400 neurons in a hidden layer, using distribution bin-frequencies as the input and output parameters. Particle models exhibiting 17, 630 different patterns of internal mineral structure and particle shape were computed and used as training data for the ANN, and a high training level, with a correlation coefficient over 0.999, was obtained. Then, experimental validation was conducted using two- and three-dimensional data obtained by X-ray computed tomography involving artificial bi-phase particles. The method showed very high stereological correction performance in the case of samples that were well approximated by the computed training data, but lesser performance in the case of an unexpected sample distribution. In addition, through comparison with the authors' previously developed texture method, the advantages and disadvantages of the proposed ANN method are discussed.