Doctoral Degrees (Chemical Engineering)

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Browsing Doctoral Degrees (Chemical Engineering) by browse.metadata.advisor "Bauer, Margret"

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    Improving the performance of causality analysis techniques for automated fault diagnosis in mineral processing plants
    (Stellenbosch : Stellenbosch University, 2019-04) Lindner, Brian Siegfried; Auret, Lidia; Bauer, Margret; Stellenbosch University. Faculty of Engineering. Dept. of Process Engineering.
    ENGLISH ABSTRACT: Modern mineral processing companies are driven towards improving productivity by leveraging existing processes optimally. This can be achieved by improving diagnosis of faults that degrade process performance to provide insightful and actionable information to process engineers. In mineral processing plants, units and variables are connected to each other through material ow, energy ow, and information ow. Faults propagate through a process along these interconnections, and can be traced back along their propagation paths to their root causes. Techniques have been developed for extracting these causal connections from historical process data. These techniques have proven successful for fault diagnosis in chemical processes. However, they have not been widely accepted by industry due to lack of automation of the techniques, complicated implementation, and complicated interpretation. This dissertation investigated the limitations of the causality analysis procedures currently available to process engineers as fault diagnosis tools and developed improvements on them. Improvements were developed and tested using a combination of simulated case studies and real world case studies of operational faults occurring in a mineral processing plant. Objective I: was to investigate the factors that a ect performance of causality analysis techniques. The use of transfer entropy for fault diagnosis in a minerals processing concentrator plant was demonstrated. The desired performance criteria of causality analysis techniques were then de ned in terms of: general applicability; automatability; interpretability; accuracy; precision; and computational complexity. The impact of process conditions on the performance of Granger causality and transfer entropy were then investigated. An analysis of variance (ANOVA) was performed to investigate the impact of process dynamics, fault dynamics, and the parameters on the accuracy of transfer entropy. Objective II: was to design a systematic work ow for application of causality analysis for fault diagnosis. The ANOVA was used to develop a novel relationship between the optimal transfer entropy parameters and the process and fault dynamics. This relationship was then placed within a systematic work ow developed for the application of transfer entropy for oscillation diagnosis, addressing the need for clear procedures and guidelines for data selection and parameter selection. The work ow was applied to an oscillation diagnosis case study from a minerals concentrator plant, and shown to provide a systematic approach to accurately determining the fault propagation path. Objective III: was to design a tool to aid the decision of which causality analysis method to select. A comparative analysis of Granger causality and transfer entropy for fault diagnosis based on the performance criteria de ned was performed. The comparison showed that transfer entropy was more precise, generalisable, and visually interpretable. Granger causality was more automatable, less computationally expensive, and easier to interpret. Guidelines were developed from these comparisons to aid users in deciding when to use Granger causality or transfer entropy Objective IV: was to present tools for interpretation of causal maps for root cause analysis. Methods for construction of causal maps from the results of the causality analysis calculation were presented, and methods for interpretation of causal maps. The usefulness of these techniques for diagnosis of real world case studies was demonstrated.