Application of data analytics and knowledge-based systems in mineral processing

[摘要] ENGLISH ABSTRACT: This dissertation covers research carried out over the past 20 years in the area of knowledgeengineering in mineral processing, specifically with regard to process data as a form ofknowledge. This focus on data-driven plant automation includes the acquisition,interpretation and application of data in the development of decision support systems inmineral processing, as well as the development of data analytical methodologies required toaccomplish this.The following subthemes have been covered:o Inferential sensors - predominantly the development of computer vision systems forfroth flotation and the analysis of particulate systems, but also acoustic sensors andthe interpretation of electrochemical noise. My research into inferential sensors hascentred on the development of methodologies and algorithms to interpret image dataand not the development of hardware, such as camera systems or other types of sensing devices. A major part of this pioneering research has focused on theinterpretation of froth flotation images. Instead of attempting to identify individualobjects (bubbles) in these images, we have treated the froth images as statistical patterns. These patterns could be interpreted by suitable feature extraction algorithms and models that could relate these features to meaningful process indicators. The novelty and impact of my research in this area can be inferred not only from the corpus of highly cited papers that associated with the technology, but alsofrom the commercialization of the technology.o Exploratory data analysis - Focusing on unsupervised learning, such as applied in data visualization, cluster analysis and feature extraction. In exploratory data analysis, the main issue is attempting to make sense of many measurements of large sets ofvariables. Standard multivariate statistical methods have their limitations when dealing with complex data, and a significant part of my research has concentrated on the extension of linear methods to their nonlinear variants by use of neural networksor other machine learning approaches. Work in this area has formed the basis of a sizeable number of industrial workshops and has significantly influenced thedevelopment of commercial process systems software.o Data-based process modelling - Machine learning approaches to predictive and diagnostic modelling. The construction of process models plays a key role in process systems engineering. This is the case in advanced control systems, where the abilityto predict future process states is critical. Models also play an important role in the interpretation of process data and hence the acquisition of insight into processbehaviour and mechanisms. Such models can be developed from first principles, but this is costly and with the abundance of process data, often not necessary. The primary impact of this research has been in the development and application of methods topredict process states or key performance indicators for mineral processing systems.o Process monitoring and fault diagnosis - Multivariate statistical process control froma machine learning perspective. Process monitoring and fault diagnosis has evolvedinto a key element of process control over the last couple of decades, and is currentlyexperiencing strong growth, with commercial application still lagging significantlybehind the advances in academia. My research in this area has centred on the application of neural networks, kernel-based systems, random forests and other machine learning methods to extend current approaches. It has led to the foundationof the Anglo American Platinum Centre for Process Monitoring at Stellenbosch University and the development of algorithms that were adopted by industry on a proprietary basis.o Intelligent decision support and advanced control - Fuzzy decision support systemsand neurocontrol based on the use of reinforcement learning. Apart from data that aregenerated by instruments, tacit knowledge in the form of plant operator experience and theoretical knowledge is also a valuable resource that can be used in theautomation of plant operations. This is the domain of knowledge-based or expert systems and research was undertaken in the development and application of thesesystems in mineral processing. The novelty of this research has mainly been in the proof-of-concept studies published in academic journals and conference proceedings.It goes without saying that in my research, I have been assisted by many colleagues,industrial collaborators, students and assistants. The contributions of these co-workerswere often critical to the investigations indicated in this thesis and are indicated as such,hopefully without omission, where appropriate.

[发布机构] Stellenbosch University