Machine learning applications in minerals processing: A review

Minerals Engineering 132 (2019) 95–109

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Minerals Engineering journal homepage: www.elsevier.com/locate/mineng

Machine learning applications in minerals processing: A review J.T. McCoy, L. Auret

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T

Department of Process Engineering, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa

A R T I C LE I N FO

A B S T R A C T

Keywords: Machine learning Artificial intelligence Machine vision Fault detection and diagnosis Data-based modelling

Machine learning and artificial intelligence techniques have an ever-increasing presence and impact on a widevariety of research and commercial fields. Disappointed by previous hype cycles, researchers and industrial practitioners may be wary of overpromising and underdelivering techniques. This review aims at equipping researchers and industrial practitioners with structured knowledge on the state of machine learning applications in mineral processing: the supplementary material provides a searchable summary of all techniques reviewed, with fields including nature of case study data (synthetic/laboratory/industrial), level of success, area of application (e.g. milling, flotation, etc), and major problem category (data-based modelling, fault detection and diagnosis, and machine vision). Future directions are proposed, including suggestions on data collection, technique comparison, industrial participation, cost-benefit analyses and the future of mineral engineering training.

1. Introduction The fields of machine learning (ML) and artificial intelligence (AI) have recently seen a number of highly-publicised successes, with systems capable of matching and exceeding human-level performance on a range of computer games using only the pixels on the screen (Mnih et al., 2015), significant improvements to language translation (Wu et al., 2016), defeating the world champion at the complex board game of Go based on learning from expert strategies (Silver et al., 2016), and subsequently achieving superhuman performance at Go without requiring human knowledge input (Silver et al., 2017). Methods based on reinforcement learning show promise for applications such as selfdriving cars and autonomous robots (Sutton and Barto, 2018). More ubiquitous, but no less significant, advances have been made in areas such as spam filtering, voice recognition (including the socalled “personal assistants” now included on most smart phones), facial recognition, and recommendation systems for users of online shopping, video streaming and social media platforms. Special issues of research journals have recently demonstrated significant effort and advances in machine learning applications in medical contexts (Calhoun, 2018; Criminisi, 2016), finance (in ’t Hout et al., 2018; Sarlin and Björk, 2017), environmental science (Gibert et al., 2018a), outdoor machine vision (Smith and Smith, 2018) and data mining (Boixader, 2017). Many of these advances have been associated with significant improvements in computational power and dramatic increases in the volumes and varieties of data which are available, as well as

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improvements to methods and techniques for artificial intelligence applications. These successes in ML and AI, combined with the advent of the Internet of Things, Big Data and the fourth industrial revolution, have sparked a renewed interest in data science in many technical fields, including chemical and process engineering. A number of recent prominent review papers and special issues have discussed the need for chemical and process engineers to take advantage of techniques from fields such as computer science, applied mathematics and statistics (Beck et al., 2016; Ge, 2017; Ge et al., 2017; Lee et al., 2018; MacGregor et al., 2015; Qin, 2018, 2014). However, the current excitement is reminiscent of the hype which surrounded the use of neural networks in chemical engineering in the 1990s and 2000s, and the subsequent disillusionment and lack of evidence of a significant impact. Indeed, there is ongoing scepticism about any technique related to artificial intelligence in many parts of engineering academia and industry, mirroring trends in the larger field of AI, which has experienced a number of “hype cycles” in which unrealistic promises were made, followed by periods of under-delivery and subsequent funding cuts and slowdowns in research and development. Fig. 1 shows the Gartner Hype Cycle for developing technologies (Linden and Fenn, 2003), and the pertinent question of the current positioning of ML and AI on this curve. This raises an important question: should the current hype about machine learning be of interest to the minerals processing industry, or is this just another period of unrealistic optimism? This literature

Corresponding author. E-mail address: [email protected] (L. Auret).

https://doi.org/10.1016/j.mineng.2018.12.004 Received 8 August 2018; Received in revised form 29 November 2018; Accepted 1 December 2018 0892-6875/ © 2018 Elsevier Ltd. All rights reserved.

Minerals Engineering 132 (2019) 95–109

J.T. McCoy, L. Auret

Fig. 1. Gartner Hype Cycle, redrawn from (Linden and Fenn, 2003).

publications on mining are also included, where relevant, although the interface between mining, geology and minerals processing, also known as geometallurgy or process mineralogy (Ntlhabane et al., 2018), was excluded; some thoughts on this are presented in the future directions section of the paper. The time frame was also restricted to 2004–2018. The search for relevant publications was focussed on the following publications:

survey will attempt to equip researchers and minerals engineers with a resource to help them answer this question by providing a review of recent published research on machine learning applications in mineral processing, and ongoing developments in the machine learning field. This review article is structured as follows: - First, clearly define the scope of the literature survey, including sources and key terms, then introduce searchable summaries of published applications of machine learning techniques in minerals processing, which have been made available as a resource to researchers and industry in the Supplementary Material, and provide some analysis of the review; - Sections 3–5 describe the three main applications of machine learning techniques: Data-based Modelling, Fault Detection and Diagnosis, and Machine Vision; - Section 6 provides some comments on the future directions of machine learning applications in mineral processing; - Conclusions.

– American Institute of Chemical Engineers Journal (http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1547–5905) – Chemometrics and Intelligent Laboratory Systems (https://www. journals.elsevier.com/chemometrics-and-intelligent-laboratorysystems/) – Computers & Chemical Engineering (https://www.journals.elsevier. com/computers-and-chemical-engineering) – Control Engineering Practice (https://www.journals.elsevier.com/ control-engineering-practice/) – Engineering Applications of Artificial Intelligence (https://www. journals.elsevier.com/engineering-applications-of-artificialintelligence/) – IFAC Symposium on Control, Optimization and Automation in Mining, Mineral and Metal Processing (via http://www. sciencedirect.com/science/journal/14746670 and https://www. journals.elsevier.com/ifac-papersonline/) – Industrial & Engineering Chemistry Research (https://pubs.acs.org/ journal/iecred) – International Journal of Mineral Processing (now combined with Minerals Engineering) (https://www.journals.elsevier.com/ international-journal-of-mineral-processing) – International Journal of Mining, Reclamation and Environment (http://www.tandfonline.com/loi/nsme20) – Journal of Process Control (https://www.journals.elsevier.com/ journal-of-process-control) – Journal of the Southern African Institute of Mining and Metallurgy (https://www.saimm.co.za/publications/journal-papers) – Minerals & Metallurgical Processing (http://mmp.smenet.org/) – Minerals Engineering (https://www.journals.elsevier.com/ minerals-engineering) – IEEE Transactions on Automation Science and Engineering (https:// ieeexplore.ieee.org/servlet/opac?punumber = 8856) – IEEE Transactions on Industrial Informatics (https://ieeexplore.ieee. org/servlet/opac?punumber = 9424) – International Journal of Minerals, Metallurgy and Materials (https://link.springer.com/journal/12613)

2. Overview of the review 2.1. Review scope For the purposes of this review article, machine learning is defined as the development and application of mathematical and statistical models with an emphasis on using data, rather than domain knowledge, to determine the appropriate structure of the models. An ML model typically has a non-parametric structure, in the sense that the number of model parameters is not defined on the basis of domain knowledge. This is in contrast to first principles and empirical models, which have clearly defined structures and parameters which usually have a physical or phenomenological interpretation (von Stosch et al., 2014). These models are then typically used for the purposes of regression or classification. Publications focussing on process control are outside of the scope, as is research into optimisation techniques (such as genetic and evolutionary algorithms) and the rapidly growing field of reinforcement learning (which has some parallels with process control). In order to provide a literature survey of ML methods of relevance to the minerals processing industry, only publications which demonstrate the application of ML techniques to minerals processing systems and processes have been included. This article is not intended as a review of the entire field of ML research. Minerals processing includes comminution, flotation, physical concentration, hydro- and pyrometallurgy, and processes for material handling, such as ore sorting. Some 96

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Fig. 2 shows the clear increase in publications of machine learning techniques in minerals processing over the period of the review; this increase reflects the increasing interest in these techniques in the wider research community over the period. Table 2 summarises the number of applications in each category in this survey. It is interesting to note that the greatest portion of applications are in machine vision, likely due to the drive to find methods to measure parameters such as particle size or chemical composition using image-based methods as a cheaper and more rapid alternative than techniques based on sample analysis. This may also indicate the relatively widespread implementation of machine vision systems in industry, and the research effort to improve the results attainable from machine vision systems. It must also be noted that many of the techniques categorised as machine vision relate to the development of models relating process parameters (such as grade or recovery) to features extracted from machine vision techniques (such as bubble or particle size, or statistical descriptions of texture). Data-based modelling which is unrelated to machine vision applications makes up a relatively small fraction of the total. Fault detection and diagnosis applications are focussed on fault detection, with two thirds of the techniques described being applied to detection. Table 3 provides a summary of the process applications of the machine learning techniques. The majority of the applications are in flotation, due to its importance as a concentration process of valuable minerals and metals; most of these applications are machine vision techniques. Similarly, ore sorting and particle sizing involves many machine vision techniques, due to the ease with which cameras can be installed near equipment such as conveyors. The next largest area of application is to mills and milling circuits; here, most applications are of data-based modelling. As shown in Fig. 3, for the first six years of the review period these three applications were the only processes considered. Other process operations, such as pyro- and hydrometallurgy and physical concentration, appear in the literature only from 2010 onwards. The harsh environment of smelting/furnace environments makes these applications enticing to machine learning methods that can exploit the information-content of simple robust sensors to predict complex properties which are difficult to measure online. Table 4 details the type of data and implementation of the reviewed techniques, the vast majority of which were based on experimental data (either from laboratory work or industrial equipment under controlled conditions), which may bring into question the industrial applicability of many of the proposed techniques. Demonstrations using industrial data make around a quarter of the reviewed techniques, and just over an eighth of the reviewed techniques have actually been implemented in an operating plant. Fig. 4 shows the data sources and implementations as a function of year of publication. Unfortunately, there is no indication that published techniques are being demonstrated on industrial data or implemented in industry with greater frequency, as demonstrations on experimental data remain dominant in most years. As shown in Table 5, the majority of techniques published are considered successful; this is likely a result of a bias against publishing negative results, rather than an indication that machine learning techniques are necessarily extremely successful. Techniques were labelled as being of limited success in cases where publications compared multiple techniques and demonstrated inferior performance, or where results were promising but not definitive. Techniques were only labelled as unsuccessful where the authors of the publication clearly stated that a method was unsuitable or unsuccessful, an exceedingly rare occurrence. In the following sections, the three categories of application are discussed in more detail, including common process applications and machine learning techniques, and publications or techniques of interest.

These specific publications were selected based on their subject-area focus (i.e. mineral processing and associated chemical, control and process engineering principles) rather than their technique focus (e.g. publications focused on the development of new machine learning techniques independent of intended use in minerals engineering). The importance of domain-knowledge in the application of machine learning techniques will be commented on later in this article, motivating the selection of these publications. In general, these publications also have wide audiences, and scopes that explicitly mention the importance of industrial relevance of submitted research articles. 2.2. Searchable summaries The supplementary material to this review article is a searchable spreadsheet of summaries of the publications which are referenced in the review. These summaries are provided as a resource to the minerals processing field, in order to aid research and application of machine learning techniques to industrially-relevant challenges. Three categories of application are used in this review: – Data-based modelling, most often applied as “soft sensors”, which use frequent plant measurements (such as temperatures, pressures, levels, flow rates, and spectra) as the input to predict measurements which are slow, difficult or expensive (such as chemical composition, mineral grade, or mill load); – Fault detection and/or diagnosis, also known as process monitoring. In fault detection, new measurements from the process are categorised as normal or abnormal, with the underlying assumption being that any abnormal measurement corresponds to a fault in the process. Fault diagnosis involves determining the cause of the detected faults; – Machine vision, a type of data-based modelling which uses images or video, rather than process measurements, as the input for the prediction of other measurements. Where a publication describes multiple techniques, or approaches with multiple, distinct steps, each technique or step is categorised and summarised on a separate line in the spreadsheet. The spreadsheet further categorises each publication (or parts of publications, where a paper describes multiple techniques or techniques with multiple steps) based on the process of interest, the type of machine learning algorithm employed, the input and output data, and hyperparameters required. Hyperparameters are tunable parameters which must be specified for many machine learning algorithms, and which can have a significant impact on the success of the implementation. The entry for each paper also includes a high-level summary of the paper or technique, an indication of whether the technique was successful, and the type of data to which the technique was applied. The column headings of the spreadsheet are described in more detail in Table 1. It is hoped that this reference list of ML methods which have been proposed for minerals processing applications will be useful for plant engineers, who can rapidly determine which methods have been applied, and to which processes, in order to identify the most promising techniques for their own challenges. Similarly, it is hoped that the reference list will be valuable for researchers in the machine learning field to easily see where existing methods have been applied, and to identify challenges or applications which may benefit from developing techniques in the field. 2.3. Analysis of review summaries Apart from being a resource for plant engineers and researchers, the summaries allow for some further analysis of trends in the field, which is provided in the tables and figures below. 97

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Table 1 Description of column headings of literature summaries. Column heading

Description

Paper title Authors Journal Year Category

Title of paper. Authors of paper. Name of academic journal. Year of publication. Type of method described: – Data-based modelling; – Fault detection and/or diagnosis; – Machine vision. Brief summary of the most important idea from the paper, or the most important idea from the step of an implementation. Process application of the method. Machine learning technique applied. Type of data used as the input to the method. Type of data produced by the method. Parameters which must be specified/optimised by the user in order to implement the method. An indication of whether the method was successful: – Yes (method was successful); – No (method was not successful); – Limited (method was partially successful) If applicable, an indication of the type of data and/or implementation of the method: – Simulated data: the method was demonstrated on simulated or synthetic data only; – Experimental data: the method was demonstrated on laboratory data, or industrial data collected under controlled conditions such as a test run or sampling campaign; – Industrial data: the method was demonstrated on industrial data collected during normal operations; – Industrial implementation: the method has been implemented in an industrial application.

Summary Application Method Inputs Outputs Hyperparameters Success

Implementation

Fig. 2. Increasing trend of publications of machine learning techniques in minerals processing, 2004–2018. Table 2 Categories of application of machine learning techniques in this review. Category

Count of techniques

Data-based modelling Fault detection and/or diagnosis Machine vision

53 33 107

Table 3 Summary of process application of machine learning techniques described in this review.

3. Data-based modelling Data-based modelling, in this review, consists of classical applications of machine learning techniques, particularly for supervised learning, in which the class label (for classification) or target predicted value (for regression) is available in the training data, allowing the development of a statistical model for the prediction of class or value for new observations. Common classification techniques include linear and nonlinear discriminant analysis (LDA) (Fisher, 1936), decision trees (also known

Process application

Data-based modelling

Fault detection and diagnosis

Machine vision

Total

Flotation Ore sorting/particle sizing Milling/milling circuits Concentration Smelting/furnaces Hydrometallurgy Other

16 2

8 0

71 26

95 28

15

7

3

25

2 12 1 5

6 8 3 1

3 0 0 4

11 20 4 10

as classification and regression trees, CART) and random forests (RF) (Breiman, 2001; Breiman et al., 1984), k-nearest neighbours (kNN) classifiers (Coomans and Massart, 1982; Hall et al., 2008; Kowalski and 98

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Fig. 3. Process application as a function of year of publication. Table 4 Summary of implementations of machine learning techniques described in this review.

Table 5 Summary of the success of applications of machine learning techniques described in this review.

Implementation

Count of techniques

Success

Count of techniques

Experimental data Simulated data Industrial data Industrial implementation

106 8 50 29

Yes Limited No

157 35 1

unsupervised learning, the objective is typically to discover some underlying structure in a dataset of observations, often by techniques such as dimensionality reduction or clustering (Ben-Hur et al., 2001; Hinton and Salakhutdinov, 2006; MacQueen, 1967). For a more detailed overview of machine learning techniques for supervised and unsupervised learning, a wide variety of reference sources is available (Bishop, 2006; Goodfellow et al., 2016; Hastie et al., 2009; Murphy, 2012). One of the challenges for data-based modelling in complex industrial processes is a direct result of the nature of process data, which is frequently high-dimensional, non-normally distributed and nonstationary, with nonlinear relationships and multiple modes, missing and faulty values, noise and outliers, significant correlation and

Bender, 1972), support vector machines (SVMs) (Cortes and Vapnik, 1995), and artificial neural networks (ANNs) (LeCun et al., 2015; Schmidhuber, 2015). Common regression techniques include linear regression, principal component regression (PCR) and partial least squares (PLS) (Kettaneh et al., 2005; Wold et al., 2001), decision trees and random forests (Breiman, 2001; Breiman et al., 1984), support vector regression (Cortes and Vapnik, 1995), extreme learning machines (ELM) (Huang et al., 2015), and artificial neural networks (LeCun et al., 2015; Schmidhuber, 2015). Another common task in machine learning is unsupervised learning, in which class labels or target predicted values are unknown. In

Fig. 4. Data source and implementation as a function of year of publication. 99

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Fast, easy measurements eg flow rate pressure temperature spectra

Slow, difficult measurements

Data-based modelling techniques PCA, PCR, PLS kNN, k-means clustering CART, RF SVM, SVR, ANN, ELM QTA

eg composition size distribution mill load equipment failure

(Chelgani et al., 2010; Jahedsaravani et al., 2016; Massinaei and Doostmohammadi, 2010; Nakhaei et al., 2012), and furnaces (Feng et al., 2013; Gomes et al., 2017). However, the majority of these neural networks were relatively small, often with only one hidden layer due to computational or data limitations. Recent developments in the design and training of complex, deep neural networks (LeCun et al., 2015; Schmidhuber, 2015) have not been demonstrated in the minerals processing literature. Comparisons between neural networks and methods such as linear or nonlinear regression have not confirmed a significant benefit of neural networks over simpler methods (Massinaei and Doostmohammadi, 2010; Nakhaei et al., 2012). An interesting application of adaptive modelling was implemented and described for flotation monitoring (Wang et al., 2016). The method uses multiple single-layer feedforward neural networks (SLFNs) to predict froth properties based on measurements (including image features). Data is first classified by a radial basis network (essentially performing clustering on the data), and each SLFN trained on a separate cluster of data. New data is again classified/clustered, to determine which of the SLFNs is applicable for prediction. Retraining of the SLFNs and radial basis network is triggered by a threshold on prediction error (referenced to offline measurements) in a sliding window. Although the specifics of the method could be adapted, the idea of adaptive retraining and modelling is of interest for many processes with important but infrequent measurements, such as grade, recovery and composition. The adaptive nature of the technique accounts for natural variation in the process, creating a robust soft sensor. The use of clustering and support vector machines (SVM) to predict the next failure type in a fleet of mining shovels was described, based on historical data (Dindarloo and Siami-Irdemoosa, 2017). The clustering process provided insight into characteristics of the fleet of shovels (such as age, machinery type and whether the shovel was used mostly for easy or difficult mining), and the SVM method achieved up to 90% accurate predictions of failure types. Another successful application related to condition monitoring and maintenance was the prediction of mill liner remaining useful life by a neural network with data preprocessing by PCA (Ahmadzadeh and Lundberg, 2013). Qualitative Trend Analysis (QTA) was applied to flotation froth condition monitoring (Zhao et al., 2017), to detect imminent froth flooding and sinking, and has been implemented industrially. QTA is a technique to extract qualitative information from time series by approximating the data with polynomials or splines, and assigning descriptive symbols to segments of data based on the first and second derivative of the approximation in each segment. QTA has received significant attention in the process engineering literature, with applications in process monitoring, modelling and control (Dash et al., 2004; Maurya et al., 2010; Thürlimann et al., 2018; Villez et al., 2013; Villez and Habermacher, 2016; Zhou and Ye, 2016). The prediction of dangerous seismic events in coal mining has been the subject of a number of publications and a data mining challenge (Janusz et al., 2017, 2016). The results showed that machine learning techniques to predict the risk of seismic events from sensor readings and mining activity logs significantly outperformed expert assessments (the current industry standard). However, the results also showed that including some expert assessment information for each site dramatically increased the success of the predictive techniques. An ensemble of machine learning techniques was applied, including decision trees,

autocorrelation, varying sampling/measurement rates, and sampling and process delays (Ge et al., 2013; Kadlec et al., 2009). The availability of data which contains sufficient variation to allow a data-based model to capture the true relationships between the variables can also be a challenge, as can the availability of labelled data, particularly for supervised learning techniques. The typical information flow for many data-based modelling applications is shown in Fig. 5, which summarises some of the common techniques applied in order to use process measurements for the prediction of parameters which may be difficult, slow or expensive to measure.

3.1. Data-based modelling in minerals processing Although Table 4 showed that most of the techniques described in this review were demonstrated on experimental data, more than half of the techniques for data-based modelling were applied to industrial data or have been implemented industrially, as shown in Table 6. This suggests that the techniques being presented are relatively mature and robust to industrial applications, but that new techniques are still being tested on more controlled datasets. Data-based modelling methods in the minerals processing literature are frequently applied as “soft sensors”, for the prediction of infrequently-measured (or difficult to measure) variables as a function of frequently-measured variables (Kadlec et al., 2011, 2009; Lin et al., 2007; Souza et al., 2016). Some applications include prediction of grindability for coal as a function of standard analyses (Khoshjavan et al., 2013; Matin et al., 2016; Özbayoğlu et al., 2008; Venkoba Rao and Gopalakrishna, 2009), prediction of mill performance indicators as a function of process measurements (Zhou et al., 2009; Ahmadzadeh and Lundberg, 2013; Gonzalez et al., 2008; Makokha and Moys, 2012; Mitra and Ghivari, 2006), prediction of flotation performance indicators as a function of process measurements (Jahedsaravani et al., 2016, 2014; Massinaei and Doostmohammadi, 2010; Nakhaei et al., 2012), and prediction of furnace quality variables as a function of process measurements (Li et al., 2018; Tian and Mao, 2010; Ling et al., 2012; Zhao et al., 2012; Feng et al., 2013; Liu et al., 2017; Picon et al., 2018; Liu et al., 2018). Prediction of elemental composition based on reflectance spectroscopy in flotation has been described, using the partial least squares (PLS) method (Haavisto, 2010; Haavisto et al., 2008); a number of proprietary solutions using reflectance spectroscopy for slurry composition exist currently (Kewe et al., 2014). A number of papers have described the use of neural networks for data-based modelling of hydrocyclones (Karimi et al., 2010), milling circuits (Makokha and Moys, 2012; Mitra and Ghivari, 2006), flotation Table 6 Summary of implementations of machine learning techniques for databased modelling. Implementation

Count of techniques

Experimental data Simulated data Industrial data Industrial implementation

21 2 21 9

Fig. 5. Typical information flow for data-based modelling applications, with example inputs and outputs. Technique acronyms are specified in the text.

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conditions changed from normal to faulty. The most common approach to this task is to base a statistical model on data which is considered representative of normal operating conditions (NOC); any observations which exceed some threshold or limit in this NOC model are considered anomalous, and assumed to be due to a fault (Qin, 2003). Fault diagnosis is the task of determining which measured variables are closely associated with the detected fault (also referred to as fault identification), and subsequently determining whether each variable is a symptom or cause of the fault (also referred to as root cause analysis). Fault identification is commonly achieved through the use of contribution or variable importance methods, which determine the influence of measured variables on the predictions of fault detection models (Alcala and Qin, 2011; Auret and Aldrich, 2011). Root cause analysis typically requires the use of methods which detect causal relationships between variables, based on observations and/or expert knowledge (Granger, 1988; Landman and Jämsä-Jounela, 2016; Li et al., 2016; Maurya et al., 2003a, b; Spirtes, 2010). Simultaneous fault detection and diagnosis is possible when labelled data is available for both normal and faulty conditions; in this case, the detection model classifies new data as either normal or belonging to a specific fault. Assuming that the labelled faulty conditions are associated with specific variables or known faults, identification and/or root cause analysis can be achieved. One of the challenges for fault detection and diagnosis in complex processes, just as in data-based modelling, is the nature of process data, which is frequently high-dimensional, non-normally distributed and nonstationary, with nonlinear relationships and multiple modes, missing and faulty values, noise and outliers, significant correlation and autocorrelation, varying sampling/measurement rates, and sampling and process delays (Ge et al., 2013; Kadlec et al., 2009). In addition, historical process data contains relatively few examples of labelled faulty operating conditions, so there is insufficient data to build models of faulty conditions; similarly, the data may contain many unlabelled faulty conditions. Thus, it can be a significant challenge to select historical data which contains sufficient range and variation to cover normal operating conditions (without which variations in the process will be incorrectly identified as alarms), but does not include unlabelled faulty conditions (whose inclusion may result in the classification of faulty conditions as normal). The typical flows of information for fault detection and diagnosis are shown in Fig. 6, along with examples of the techniques which have been applied.

support vector machines and logistic regression, and an algorithm to automate the selection of techniques described. Ensemble techniques use multiple simple models and aggregate their results, which has been shown to improve model performance (Hastie et al., 2009). A similar approach has been successfully applied to methane gas monitoring (Ślęzak et al., 2018). A recent publication presented a very thorough description of a framework for the analysis of the impact of rock properties and operational settings on plant key performance indicators (Suriadi et al., 2018). The technique, named the Integrated Analysis Method, uses clustering approaches applied to short term mine plan data (describing feedstock properties) and key performance indicator measurements (KPIs) to find periods of operation in which feedstock properties were similar but performance was significantly different. Process measurements are then used as inputs to supervised learning models, based on these periods of operation, in order to investigate the effects of operating parameters on plant performance, independent of feedstock variations. As machine learning techniques become increasingly accessible as part of software packages, it is likely that applications of data-based modelling will become more common and make use of more advanced techniques and analyses. In particular, techniques to model the dynamics of complex processes may be of interest, as most applications currently assume that observations are independent, and incorporating time-series information may prove useful. 4. Fault detection and diagnosis Fault detection and diagnosis (FDD) is primarily concerned with the detection of abnormal/faulty conditions in complex processing systems, and the identification of the root causes of the faulty conditions. When FDD is performed on the basis of historical data (rather than first principles models of a system) then it is also referred to as data-based or statistical process monitoring. The FDD work considered in this review (i.e. with mineral processing case studies) comprises a small subset of the extensive and mature FDD literature; for more general introductions to the topic, the interested reader is referred to the extensive review articles of Venkatasubramanian and co-authors (Venkatasubramanian et al., 2003c, 2003a, b), with particular emphasis on the third part of the review, which focusses on data-based techniques (Venkatasubramanian et al., 2003b). Clearly structured reviews and introductions can also be found by Qin and co-authors (Qin, 2012, 2003), whilst more recent reviews have placed process monitoring in the context of Big Data (He and Wang, 2017; Reis and Gins, 2017). Fault detection is the task of determining at which point process

4.1. Fault detection and diagnosis in minerals processing Although the analysis of all of the reviewed techniques showed that

Fault detection PCA, kPCA, ELM-PCA LLE SVM

Process state normal or faulty

Process measurements flow rate pressure temperature spectra

Fault diagnosis Contribution plots, Variable importance Cross-correlation matrix Graph learning Granger causality

Fault cause symptom and/or causal variables

Fig. 6. Typical information flow for fault detection and diagnosis applications, with example inputs and outputs. Technique acronyms are specified in the text. 101

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simple classification approaches can be applied to distinguish normal and faulty observations. LLE was presented for overload detection in a milling circuit (McClure et al., 2014; McClure and Gopaluni, 2015). Although the results were better than standard implementations of either kPCA or support vector machines, the sensitivity of the technique to hyperparameters such as the number of embedding dimensions and number of nearest neighbours may pose a challenge to implementation. A semi-supervised approach to simultaneous fault detection and diagnosis was presented for a milling circuit (Wang et al., 2015), making use of graph theory projections of the data. Given a very small number (between one and three) of labelled data points for each class (normal, fault one, fault two, etc), the method was able to successfully detect and identify multiple fault conditions for the milling circuit. This method could be particularly useful industrially, where limited labelled data is available. Fault diagnosis is still developing in the general literature, with considerable effort focused on causal methods for root cause analysis; however, very few applications to minerals processing have been presented. Fault identification, the identification of variables associated with faulty conditions, is most commonly achieved through the use of contribution methods, which calculate the relative contribution of each variable to observed violations of monitoring thresholds of PCA-based approaches. Contribution methods have been successfully demonstrated on concentration (Groenewald and Aldrich, 2015; Lindner and Auret, 2015), leaching (Strydom et al., 2018) and milling circuit applications (Wakefield et al., 2018). Variable importance techniques, which identify the effects of individual variables in a complex model, have been proposed for concentration plants, with better results than contribution methods (Groenewald and Aldrich, 2015). Root cause analysis techniques for fault diagnosis attempt to construct causal relationships from data; linear and partial cross-correlation methods have been applied to tailings pumping and platinum concentration (Lindner and Auret, 2015; Yang et al., 2010). Granger causality has been proposed as an efficient and effective technique for extracting causal relationships between variables (Bressler and Seth, 2011; Granger, 1988; Yuan and Qin, 2014), but an application to root cause analysis in a simulated milling circuit revealed the method is very sensitive to the selection of data used to generate the causal relationships, suggesting that further work is required to formalise the method’s application (Wakefield et al., 2018). As mentioned above, fault detection and diagnosis is a widely-applied and mature field of research, although there are relatively few publications on the topic in the minerals processing literature. Fault detection is well-developed and successful, but fault diagnosis requires further research effort before industrial implementation becomes widespread. Fault prognosis appears to be the next development to receive attention in the literature (Reis and Gins, 2017), with the promise of techniques which go beyond identifying the causes of faults, and can propose corrective actions, such as whether to shut down a plant for maintenance (Olivier and Craig, 2017).

Table 7 Summary of implementations of machine learning techniques for fault detection and diagnosis. Implementation

Count of techniques

Experimental data Simulated data Industrial data Industrial implementation

1 6 24 2

most are demonstrated on experimental data (Table 4), the majority of fault detection and diagnosis techniques were demonstrated on industrial data, as shown in Table 7, although few industrial implementations are described. Almost all of the fault detection techniques described rely on the application of principal component analysis (PCA) or extensions and modifications of PCA to account for non-linear relationships or for datasets which violate the standard assumptions of independent and identically-distributed observations. A description of PCA for fault detection is not given here, as it has been described in detail in the literature; the reader is referred to Qin (2003) for an overview. Standard and successful applications of PCA to fault detection have been described for flotation systems (Aldrich et al., 2007; Bergh et al., 2005), milling circuits (Groenewald et al., 2006; Wakefield et al., 2018), base metal refineries (Miskin et al., 2016; Strydom et al., 2018), and furnaces (Groenewald et al., 2018; Wang et al., 2018; Zhou et al., 2016; Zhang and Li, 2013). An early approach to account for nonlinearity of process data was the use of kernel PCA (kPCA) for flotation and furnace operations (Jemwa and Aldrich, 2006), which uses kernel functions to find a higher-dimensional representation of the original data, in which the nonlinear relationships become linear. A key challenge in the application of any kernel-based method is the selection of the kernel function, and associated hyperparameters. The suggested approach used kPCA to remove nonlinearities from the data, and then applied PCA to the residual errors between the data and the kPCA predictions. Support vector machine (SVM) density estimation was used to account for nonnormal data distributions and ensure monitoring limits were robust. A related technique was more recently described in the context of platinum concentration (Groenewald and Aldrich, 2015). This method used extreme learning machines (ELMs), a type of single-layer feedforward neural network which can be very rapidly trained to represent complex and nonlinear data (Huang et al., 2015), to remove nonlinearities from the data. PCA was then applied to the residual errors between the observations and ELM predictions. An alternative technique to account for non-normally distributed data in blast furnace monitoring used convex hulls to determine the monitoring threshold in the score-space of PCA-based fault detection (Zhou et al., 2016). Convex hulls define the smallest convex set containing all points, and can define a non-elliptical confidence region for monitoring the scores in PCA, which may be less conservative than the standard approach based on Hotelling’s T2. The convex hull PCA was also successfully extended to make use of moving windows, which allows monitoring of time-varying processes. Monitoring of alumina feeding cycle variation in aluminium smelting was achieved using Multiway PCA, which builds a monitoring model on multiple observations of similar trends (such as batches or feeding cycles) and identifies abnormal trajectories (Abd Majid et al., 2011); the standard PCA approach would not be successful in this type of application. Locally linear embedding (LLE) is the only notable non-PCA based technique presented for fault detection in minerals processing. LLE finds linear manifolds which can describe neighbouring points in a high-dimensional, nonlinear space (such as historical data); the linear manifolds are of lower dimension than the original data space, and

5. Machine vision Machine vision is the application of machine learning techniques to images and video, rather than numerical data from plant sensors. Image data presents a significant challenge for any analytical technique due to its high-dimensional nature, as each pixel represents information about the image. By this measure, a single colour photograph from a consumer-grade digital camera can contain millions or tens of millions of variables, which makes analysis by traditional techniques (such as linear regression) infeasible. Machine vision is the study of techniques to extract meaningful information from high-dimensional images. One approach to machine vision makes use of a range of digital image processing techniques, such as filtering, thresholding, edge detection, segmentation, or colour analysis, in order to obtain relevant 102

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Image processing techniques

Image/video data flotation froth ore on conveyor falling particles grinding media

filtering, thresholding edge detection, segmentation morphological transformations colour analysis

Feature extraction techniques

Process parameters Data-based modelling techniques

particle/bubble size froth speed mineral composition operating mode

PCA GLCM, LBP, WTA cross correlation matrix ANN, CNN

Fig. 7. Typical information flow for machine vision applications, with example inputs and outputs. Technique acronyms are specified in the text.

to automatically extract the edges of particles, in order to extract particle sizes, or determine sub-regions of images for further analysis, particularly estimation of size distribution (Andersson et al., 2012; Claudio et al., 2009; Kaartinen et al., 2006; Koh et al., 2009; Perez et al., 2015, 2011; Thurley and Andersson, 2008; Vinnett et al., 2009). A solution to the problem of estimating particle size given partiallyvisible particles in heaps or piles has been proposed, using linear discriminant analysis as a supervised learning technique (Thurley, 2009; Thurley and Andersson, 2008). The discriminant analysis can distinguish between fully- and partially-visible particles, based on the outputs of image processing, including aspect ratios and visibility ratios; however, a large database of manually labelled images is required to train the classifier. The particle size distribution is then calculated only using fully-visible particles, reducing the bias of the size estimate. The technique has been commercialised and implemented industrially (Thurley and Andersson, 2008). Feature extraction methods applied to ore and particle sizing and sorting applications are mostly textural feature extractors. Common techniques are the grey-level co-occurrence matrix (GLCM), Gabor filters and wavelet texture analysis (Perez et al., 2015, 2011; Singh and Rao, 2005; Tessier et al., 2007), methods which have seen much wider application in flotation froth monitoring. Ore sorting applications typically make use of classification models, such as support vector machines (Perez et al., 2015, 2011) or neural networks (Chatterjee and Bhattacherjee, 2011; Singh and Rao, 2005) to link the image features to ore or rock type, ore grindability, or ore grade. In the particle sizing application, the particle size estimate is typically extracted directly from the image processing steps, although Gaussian process regression has been demonstrated to estimate size distribution for falling particles (Chen et al., 2014). Cost-sensitive classifiers were introduced for the classification of gold-bearing ore into waste and ore classes (Horrocks et al., 2015), based on textural, spectral and spectrographic features. The cost-sensitive method, called MetaCost (Domingos, 1999), can be applied on top of any other classification model, and trains the classifier to be sensitive to user-specified costs for false positives and false negatives. In this case, the cost elements were derived from process understanding, in order to quantify the impact of incorrectly identifying waste as ore (increasing the processing load of the plant) and of incorrectly identifying ore as waste (causing a loss of valuable material). Despite the increase in the use of deep neural networks for machine vision in the wider literature, no published applications of this technique to ore sorting or particle sizing were found for this review.

information from the image. In the minerals processing context, edge detectors and segmentations can be applied to detect the boundaries of particles or bubbles, and extract information such as particle or bubble size distribution or froth stability. The alternative approach involves feature extraction, a range of techniques to extract lower dimensional, abstract features from the high-dimensional images; these features may be correlated with parameters of interest (such as particle size or chemical composition), but do not directly measure these parameters. Feature extraction techniques are closely linked with dimensionality reduction methods, such as principal component analysis. Whether the image processing or feature extraction approach (or both) is followed, the subsequent step in a machine vision application is to relate the features and/or parameters of interest from the images to process conditions or variables, or to key performance indicators such as grade, chemical composition or size distribution. These relationships can be modelled using a range of supervised learning techniques, some of which were described in the section on data-based modelling. The information flow for machine vision applications is summarised in Fig. 7, including the two techniques (image processing or feature extraction) and the subsequent prediction of process parameters, sometimes through an additional data-based modelling step. Machine vision methods are primarily described in two applications for minerals processing: sizing and sorting of particles and ore fragments, and flotation froth monitoring.

5.1. Particle/ore sorting and sizing The task of automatically detecting ore properties (including particle size and mineral content), typically on a moving conveyor, has received considerable attention in the literature, although most techniques have only been demonstrated on experimental data, as shown in Table 8. Image processing techniques are very common in this application, particularly approaches such as the Watershed segmentation algorithm Table 8 Summary of implementations of machine learning techniques for particle/ore sorting and sizing. Implementation

Count of techniques

Experimental data Simulated data Industrial data Industrial implementation

24 0 0 2

103

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as a feature extraction technique, and used the features as inputs to linear and nonlinear classifiers (Horn et al., 2017). The second used a pre-existing neural network known as AlexNet (Krizhevsky et al., 2012), which is designed to classify a wide range of images, as a feature extraction technique, reducing the data requirements for training (Fu and Aldrich, 2018). In both cases, the lack of large sets of labelled images of flotation froths was an obstacle, but the results were promising. It is expected that applications of convolutional neural networks, and other deep neural network architectures, will become more widespread as the technology continues to mature. It is likely that these state of the art techniques could provide significant improvements for flotation froth and particle/ore sorting and sizing applications.

Table 9 Summary of implementations of machine learning techniques for flotation froth machine vision. Implementation

Count of techniques

Experimental data Simulated data Industrial data Industrial implementation

50 0 5 16

5.2. Flotation froths Flotation froth analysis and monitoring is the topic with the most published applications covered in this review, largely due to the wellestablished links between flotation froth appearance and flotation performance, including grade and recovery (Aldrich et al., 2010). Two thirds of the industrial implementations of machine learning techniques identified in this review are for flotation systems, as can be seen by comparing Tables 9 and 4. However, the majority of applications are still demonstrated on experimental data, most of which is obtained from carefully controlled test runs on industrial operations. The most common approach to flotation froth monitoring is extraction of textural features from froth images. Textural feature extractors are designed to extract features from the images which will be strongly correlated with parameters such as bubble size distribution, solids attachment and froth stability, as well as key performance indicators such as grade and recovery. The grey-level co-occurrence matrix (GLCM) is one of the most common techniques; the GLCM determines summary statistics describing the relationships between the intensity of the light at each pixel, primarily as a function of distances between pixels (Bartolacci et al., 2006; Cao et al., 2013; Fu and Aldrich, 2018; Horn et al., 2017; Kistner et al., 2013; Marais and Aldrich, 2011, 2010). Other textural techniques include variograms (Mesa et al., 2016), Gabor filters (Zhang et al., 2016), local binary patterns (LBP), wavelet texture analysis (WTA) and steerable pyramids (Bartolacci et al., 2006; Horn et al., 2017; Kistner et al., 2013; Liu et al., 2005). Image processing techniques have also been applied to flotation froth monitoring, including the Watershed segmentation algorithm for bubble size distribution estimation (Cao et al., 2013; Jahedsaravani et al., 2014; Kaartinen et al., 2006; Mehrshad and Massinaei, 2011; Zhang et al., 2018; Zhao et al., 2017; Zhu et al., 2014). Techniques such as the cross-correlation matrix have been applied to consecutive images to determine properties such as bubble collapse rate and froth speed (Barbian et al., 2007; Cao et al., 2013; Kaartinen et al., 2006). Two methods to extract depth information from single-camera images were presented, in attempts to augment the data which can be extracted from the images. Equivalent binary vision (Zhao et al., 2016) uses consecutive frames from an image to extract depth, but no significant improvement over existing methods was demonstrated. Defocus depth recovery (Zhao et al., 2017) estimates depth in an image based on image blurring and geometric analysis. Although this method has been implemented industrially to estimate froth level, standard techniques (such as laser levels) may be simpler to implement. Features from image processing or feature extraction are most often used in prediction or classification models for grade prediction, sometimes in conjunction with other measured process parameters. Commonly-applied techniques include simple neural networks (Estrada-Ruiz and Pérez-Garibay, 2009; Jahedsaravani et al., 2017; Marais and Aldrich, 2011), support vector machines (Cao et al., 2013; Popli et al., 2018a, 2018b) and discriminant analysis (Fu and Aldrich, 2018, 2016; Horn et al., 2017; Marais and Aldrich, 2010). Only two applications of deep neural networks for flotation froth machine vision have been published. The first used a convolutional neural network, which extracts features from small sections of the image and then combines these features in increasingly complex ways,

6. Future directions 6.1. Data quality and the role of simulations Although a wide range of applications of machine learning techniques appears in the minerals processing literature, one key element which has not been explicitly discussed is data quality. The quality of the data used for any machine learning technique is critical to the success of the method. Training data should preferably cover all operating regimes of interest, include plant dynamics, be at high resolution, be labelled, and contain sufficient observations for the training of ML techniques, many of which are extremely data-hungry. In addition, sufficient data for representative validation and testing sets is vital to any rigorous model building process. Although industrial data is often available in large volumes (at least to those in industry), it is typically at relatively low resolution (particularly critical measurements such as chemical composition, which require laboratory analysis), with differing sampling rates between measurements, includes multiple issues such as missing values and process and measurement noise, and is unlabelled. This problem is not limited to minerals processing, or even chemical and process engineering (Ge et al., 2017; MacGregor et al., 2015; Qin, 2014); fields such as environmental science have also raised this as a significant challenge to further implementation of machine learning techniques in the field (Gibert et al., 2018b). A limitation of the work reviewed above is the lack of demonstrations on industrial data, suggesting that many proposed techniques are not yet ready for industrial implementation, and are still in the phase of being developed, tested and demonstrated on experimental or simulation data. Confidentiality and intellectual property constraints may also limit researchers’ access to industrial data, and their ability to publish work using industrial data in the literature. For this reason, simulation data may prove to be the best testing ground for machine learning techniques. A number of fields have benchmark problems which are used as a demonstration of the capabilities of new techniques; notable benchmarks include the MNIST handwritten digit dataset for handwriting recognition systems (http:// yann.lecun.com/exdb/mnist/), the Tennessee Eastman Process in the fault detection and diagnosis literature (Downs and Vogel, 1993), and the ImageNet challenge in image classification (http://www.image-net. org/). Simulations of minerals processing systems, such as milling circuits (le Roux et al., 2013; Wakefield et al., 2018) and leaching processes (Dorfling et al., 2013a, 2013b; Strydom et al., 2018) could be used as benchmarks for testing of data-based modelling and fault detection and diagnosis techniques; simulations for testing machine vision techniques may pose a greater challenge. It is vital that any simulation is sufficiently complex to represent the challenges of working with industrial data, including process variation and disturbances, measurement noise, and regulatory and supervisory control. Given a simulation of sufficient fidelity, industrial and simulation data should become almost indistinguishable, apart from one key 104

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difference: in the simulation data, the “ground truth” is known (for example, true values of missing measurements, start time of a faulty condition, or the causal relationship between variables), and so the accuracy of the technique being tested can be accurately assessed, and compared to the results for other techniques.

may provide through appropriate machine learning techniques. Optimal sensor location algorithms (Mazzour et al., 2003) could be adapted to incorporate the potential benefit of machine learning techniques with additional instrumentation. 6.4. Industrial application of machine learning research

6.2. Technique comparison and hyperparameter selection As stated previously, it is hoped that this review, and particularly the searchable summaries in the Supplementary Material, will provide a starting point for industrial researchers, metallurgists and plant engineers to see what methods have been successfully applied to problems they may already face. The first step for the metallurgist interested in applying machine learning techniques on his/her plant is to start building a database of extensive, well-labelled data, including as wide a range of sources as possible. Only once this data is available can frameworks such as the Integrated Analysis Method for a minerals processing facility (Suriadi et al., 2018) be applied. Other works have proposed similar frameworks for data mining and clustering in process plants (Thomas et al., 2018), and data mining and forecasting of big data (Sakurai et al., 2015) and nonlinear and time-varying systems (Cheng et al., 2015). A rich source of data which appears to be largely unexplored for machine learning techniques in minerals processing is geological and mineralogical data. Although modelling frameworks using process mineralogy have been proposed for the investigation of the effects of geological and mineralogical variation on metallurgical performance (Navarra et al., 2018; Ntlhabane et al., 2018), there is potential for the use of data-based modelling techniques to further exploit this data, such as by extending the ideas in Suriadi et al. (2018), who linked short term mine plan data to plant performance. As machine learning methods become increasingly data-intensive and specialised, questions about the infrastructure and expertise required to conduct analysis arise (Gibert et al., 2018b). Cloud computing, making use of distributed computational resources, is common, but may pose data security concerns to process industries. However, the information technology departments of many industrial and corporate companies may not be equipped to manage the computing resources necessary for large machine learning systems. Similarly, plant metallurgists may not be the best choice to construct and analyse complex machine learning models (although they have the domain knowledge and know the questions to ask), while external data scientists may be equipped to run the analyses, but may not be prepared to deal with minerals processing data and systems. Data mining challenges and “hackathons” are one way in which data science expertise can be exposed to industrial data, and in which industries can make industrially-relevant data and problems available for experts to tackle. Challenges can take the form of standalone events, such as the Unearthed hackathons, which focus on the resources sector (https://unearthed.solutions/hackathons/cape-town−2016/), sessions at conferences, such as the prediction of seismic events in coal mining challenge (Janusz et al., 2017, 2016), or online competitions, such as those hosted by Kaggle (https://www.kaggle.com/).

Many of the papers reviewed typically focus only on one or two novel techniques, without comparison to existing benchmark approaches for the specific application. Comparison of techniques of different hyperparametric and computational complexity is important from the perspective of industrial implementation: Would the reduced computational complexity and fewer user-specific hyperparameters of a simpler (e.g. linear) technique merit a decrease in accuracy, as compared to a more computationally complex and hyperparameter-greedy approach? Performance metrics for machine learning techniques should not only include the obvious accuracy and computational complexity metrics, but also metrics that would be required to de-risk its use in industry. For example, a metric which summarises the applicability of a technique to a specific new data point (i.e. Is the technique interpolating or extrapolating in comparison to its training data? Would retraining of the technique be required due to a shift in process conditions?). Another useful metric would be the robustness of the method to variability in the training data: How sensitive is the technique to different manifestations of the training data? How sensitive is the technique to different hyperparameter selections? Optimal hyperparameter selection is a particular challenge to industrial application: research publications should provide clear guidelines on how such hyperparameters should be selected, and how sensitive results are to incorrect hyperparameter selection. 6.3. Cost-benefit analyses and instrumentation From an industrial perspective, any new technology needs to provide a promising business case in order to be of immediate interest. For automation/process control technologies, such a business case can typically be constructed by evaluating the expected reduction in variance of key performance indicators after the implementation of such technologies, and assessing improved economic performance based on this variance minimization and an economic performance function (Bauer and Craig, 2008). The cost of new technologies (e.g. additional instrumentation) can then be weighed against the potential benefits. A similar approach could be used to assess the cost-benefit trade-offs for certain classes of machine learning technologies: e.g. for data-based modelling approaches that allow the development of soft sensors, employed in automated inferential controllers. However, not all cost-benefit trade-offs can be cast as variance minimization estimation – the benefit estimation of fault detection and diagnosis technologies is a relatively unexplored field; with some inspiration that can be drawn from prognosis and condition-based maintenance research fields. For example, a technical value metric has been proposed (Saxena et al., 2008) that incorporates the probability of abnormal events, their potential effects on process performance (e.g. through reduced uptime), and the potential false alarms (and associated costs) that can be raised by such technologies. Further research in such cost-benefit analysis techniques is essential to create sufficient confidence in industries in terms of the potential benefits of machine learning techniques. Another important consideration is that of instrumentation installation or upgrade: since machine learning techniques are reliant on good and diverse data, sufficient instrumentation and continual maintenance and recalibration becomes essential. Where insufficient instrumentation is available, the costs of additional instrumentation should be weighed against the potential benefits that these instruments

6.5. The mineral processing engineers of tomorrow Machine learning application research is most successful where there is a combination of the following factors: large quantities of diverse data; mineral processing expertise and understanding of the process operation under consideration; deep knowledge and extensive experience of the machine learning techniques of interest. As machine learning applications in mineral processing will only increase with time, there is a real need to increase the availability of these resources (data, process understanding and technique expertise). This suggests that the training of the mineral processing engineers of tomorrow should involve greater emphasis on non-traditional curricular elements, i.e. computational thinking, data analysis, and machine learning 105

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techniques (Moreno-Leon et al., 2018). This can either take shape through introductory courses in undergraduate programmes, or through specialized courses in postgraduate programmes. Short courses aimed at continuous professional development for already qualified mineral processing professionals should also be further developed and enhanced. Even if mineral engineers will not be directly involved in designing machine learning algorithms for specific applications, the overall computational literacy will allow them to consider industrial challenges with new perspectives, armed with a basic understanding of the capabilities and limitations of machine learning.

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7. Conclusions In this paper, a review of recent machine learning applications in mineral processing, as represented by published academic research in a subset of journals from 2004 to 2018, has been presented. The searchable summaries should provide researchers with a valuable tool for easily accessing relevant techniques in their own area of interest, with additional information highlighting a measure of maturity of the research. For the three main categories of application identified (databased modelling, fault detection and diagnosis, and machine vision), interesting techniques, challenges and opportunities have been identified. A number of future directions have been identified, which will hopefully inspire critique from researchers in this field, as well as guiding new researchers interested in exploring machine learning methods for their own applications. Acknowledgements The financial support of this research by Anglo American Platinum is gratefully acknowledged. Appendix A. Supplementary material Supplementary data to this article can be found online at https:// doi.org/10.1016/j.mineng.2018.12.004. References Abd Majid, N.A., Taylor, M.P., Chen, J.J.J., Young, B.R., 2011. Multivariate statistical monitoring of the aluminium smelting process. Comput. Chem. Eng. 35, 2457–2468. https://doi.org/10.1016/j.compchemeng.2011.03.001. Ahmadzadeh, F., Lundberg, J., 2013. Remaining useful life prediction of grinding mill liners using an artificial neural network. Miner. Eng. 53, 1–8. https://doi.org/10. 1016/j.mineng.2013.05.026. Alcala, C.F., Qin, S.J., 2011. Analysis and generalization of fault diagnosis methods for process monitoring. J. Process Control 21, 322–330. https://doi.org/10.1016/j. jprocont.2010.10.005. Aldrich, C., Jemwa, G.T., Krishnannair, S., 2007. Multiscale process monitoring with singular spectrum analysis. IFAC Proc. 40, 167–172. https://doi.org/10.3182/ 20070821-3-CA-2919.00024. Aldrich, C., Marais, C., Shean, B.J., Cilliers, J.J., 2010. Online monitoring and control of froth flotation systems with machine vision: a review. Int. J. Miner. Process. 96, 1–13. https://doi.org/10.1016/j.minpro.2010.04.005. Andersson, T., Thurley, M.J., Carlson, J.E., 2012. A machine vision system for estimation of size distributions by weight of limestone particles. Miner. Eng. 25, 38–46. https:// doi.org/10.1016/j.mineng.2011.10.001. Auret, L., Aldrich, C., 2011. Empirical comparison of tree ensemble variable importance measures. Chemom. Intell. Lab. Syst. 105, 157–170. https://doi.org/10.1016/j. chemolab.2010.12.004. Barbian, N., Cilliers, J.J., Morar, S.H., Bradshaw, D.J., 2007. Froth imaging, air recovery and bubble loading to describe flotation bank performance. Int. J. Miner. Process. 84, 81–88. https://doi.org/10.1016/j.minpro.2006.10.009. Bartolacci, G., Pelletier, P., Tessier, J., Duchesne, C., Bossé, P.-A., Fournier, J., 2006. Application of numerical image analysis to process diagnosis and physical parameter measurement in mineral processes—Part I: flotation control based on froth textural characteristics. Miner. Eng. 19, 734–747. https://doi.org/10.1016/j.mineng.2005. 09.041. Bauer, M., Craig, I.K., 2008. Economic assessment of advanced process control – a survey and framework. J. Process Control 18, 2–18. https://doi.org/10.1016/j.jprocont. 2007.05.007. Beck, D.A.C., Carothers, J.M., Subramanian, V.R., Pfaendtner, J., 2016. Data science:

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