Chris Aldrich

Monitoring of carbon steel corrosion by use of electrochemical noise and recurrence quantification analysis

Publisher: Elsevier BV

Date: 11-2016

DOI: 10.1016/J.CORSCI.2016.07.009

Deep Learning Approaches to Image Texture Analysis in Material Processing

Publisher: MDPI AG

Date: 18-02-2022

DOI: 10.3390/MET12020355

Abstract: Texture analysis is key to better understanding of the relationships between the microstructures of the materials and their properties, as well as the use of models in process systems using raw signals or images as input. Recently, new methods based on transfer learning with deep neural networks have become established as highly competitive approaches to classical texture analysis. In this study, three traditional approaches, based on the use of grey level co-occurrence matrices, local binary patterns and textons are compared with five transfer learning approaches, based on the use of AlexNet, VGG19, ResNet50, GoogLeNet and MobileNetV2. This is done based on two simulated and one real-world case study. In the simulated case studies, material microstructures were simulated with Voronoi graphic representations and in the real-world case study, the appearance of ultrahigh carbon steel is cast as a textural pattern recognition pattern. The ability of random forest models, as well as the convolutional neural networks themselves, to discriminate between different textures with the image features as input was used as the basis for comparison. The texton algorithm performed better than the LBP and GLCM algorithms and similar to the deep learning approaches when these were used directly, without any retraining. Partial or full retraining of the convolutional neural networks yielded considerably better results, with GoogLeNet and MobileNetV2 yielding the best results.

Extraction of Gold and Copper from a Gold Ore Thiosulfate Leachate by Use of Functionalized Magnetic Nanoparticles

Publisher: Informa UK Limited

Date: 02-08-2020

DOI: 10.1080/08827508.2019.1636048

Dynamic Monitoring of Grinding Circuits by Use of Global Recurrence Plots and Convolutional Neural Networks

Publisher: MDPI AG

Date: 27-10-2020

DOI: 10.3390/MIN10110958

Abstract: Reliable control of grinding circuits is critical to more efficient operation of concentrator plants. In many cases, operators still play a key role in the supervisory control of grinding circuits but are not always able to act timely to deal with disturbances, such as changes in the mill feed. Reliable process monitoring can play a major role in assisting operators to take more timely and reliable action. These monitoring systems need to be able to deal with what could be complex nonlinear dynamic behavior of comminution circuits. To this end, a dynamic process monitoring approach is proposed based on the use of convolutional neural networks. To take advantage of the availability of pretrained neural networks, the grinding circuit variables are treated as time series which can be converted into images. Features extracted from these networks are subsequently analyzed in a multivariate process monitoring framework with an underlying principal component model. Two variants of the approach based on convolutional neural networks are compared with dynamic principal component analysis on a simulated and real-world case studies. In the first variant, the pretrained neural network is used as a feature extractor without any further training. In the second variant, features are extracted following further training of the network in a synthetic binary classification problem designed to enhance the extracted features. The second approach yielded nominally better results than what could be obtained with dynamic principal component analysis and the approach using features extracted by transfer learning.

Use of Decision Trees for the Development of Decision Support Systems for the Control of Grinding Circuits

Publisher: MDPI AG

Date: 31-05-2021

DOI: 10.3390/MIN11060595

Abstract: Grinding circuits can exhibit strong nonlinear behaviour, which may make automatic supervisory control difficult and, as a result, operators still play an important role in the control of many of these circuits. Since the experience among operators may be highly variable, control of grinding circuits may not be optimal and could benefit from automated decision support. This could be based on heuristics from process experts, but increasingly could also be derived from plant data. In this paper, the latter approach, based on the use of decision trees to develop rule-based decision support systems, is considered. The focus is on compact, easy to understand rules that are well supported by the data. The approach is demonstrated by means of an industrial case study. In the case study, the decision trees were not only able to capture operational heuristics in a compact intelligible format, but were also able to identify the most influential variables as reliably as more sophisticated models, such as random forests.

Explaining anomalies in coal proximity and coal processing data with Shapley and tree-based models

Publisher: Elsevier BV

Date: 03-2023

DOI: 10.1016/J.FUEL.2022.126891

Atmospheric leach process of high-chromitite PGM-bearing oxidized mineralized ore through a single-stage and two-stage techniques

Publisher: Elsevier BV

Date: 08-2018

DOI: 10.1016/J.MINENG.2018.05.007

Removal of arsenic from gold processing circuits by use of novel magnetic nanoparticles

Publisher: Informa UK Limited

Date: 26-10-2017

DOI: 10.1080/00084433.2017.1391737

Assessing the Influence of Operational Variables on Process Performance in Metallurgical Plants by Use of Shapley Value Regression

Publisher: MDPI AG

Date: 22-10-2022

DOI: 10.3390/MET12111777

Abstract: Shapley value regression with machine learning models has recently emerged as an axiomatic approach to the development of diagnostic models. However, when large numbers of predictor variables have to be considered, these methods become infeasible, owing to the inhibitive computational cost. In this paper, an approximate Shapley value approach with random forests is compared with a full Shapley model, as well as other methods used in variable importance analysis. Three case studies are considered, namely one based on simulated data, a model predicting throughput in a calcium carbide furnace as a function of operating variables, and a case study related to energy consumption in a steel plant. The approximately Shapley approach achieved results very similar to those achieved with the full Shapley approach but at a fraction of the computational cost. Moreover, although the variable importance measures considered in this study consistently identified the most influential predictors in the case studies, they yielded different results when fewer influential predictors were considered, and none of the variable importance measures performed better than the other measures across all three case studies.

Stellenbosch University

Location: South Africa

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Industrial Transformation Training Centres - Grant ID: IC190100034

Start Date: 12-2020

End Date: 12-2025

Amount: $3,574,272.00

Funder: Australian Research Council

Industrial Transformation Training Centres - Grant ID: IC180100030

Start Date: 02-2019

End Date: 02-2025

Amount: $3,925,357.00

Funder: Australian Research Council

Industrial Transformation Training Centres - Grant ID: IC190100017

Start Date: 08-2020

End Date: 08-2026

Amount: $3,703,664.00

Funder: Australian Research Council

ARC Centres Of Excellence - Grant ID: CE200100009

Start Date: 07-2020

End Date: 07-2027

Amount: $35,000,000.00

Funder: Australian Research Council