Characterisation of Dynamic Process Systems by Use of Recurrence Texture Analysis

This thesis proposes a method to analyse the dynamic behaviour of process systems using sets of textural features extracted from distance matrices obtained from time series data. Algorithms based on the use of grey level co-occurrence matrices, wavelet transforms, local binary patterns, textons, and the pretrained convolutional neural networks (AlexNet and VGG16) were used to extract features. The method was demonstrated to effectively capture the dynamics of mineral process systems and could outperform competing approaches

Predicting the Operating States of Grinding Circuits by Use of Recurrence Texture Analysis of Time Series Data

Grinding circuits typically contribute disproportionately to the overall cost of ore beneficiation and their optimal operation is therefore of critical importance in the cost-effective operation of mineral processing plants. This can be challenging, as these circuits can also exhibit complex, nonlinear behavior that can be difficult to model. In this paper, it is shown that key time series variables of grinding circuits can be recast into sets of descriptor variables that can be used in advanced modelling and control of the mill. Two real-world case studies are considered. In the first, it is shown that the controller states of an autogenous mill can be identified from the load measurements of the mill by using a support vector machine and the abovementioned descriptor variables as predictors. In the second case study, it is shown that power and temperature measurements in a horizontally stirred mill can be used for online estimation of the particle size of the mill product