Browsing by Author "Strydom, Johannes Jacobus"

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    Fault detection, identification and economic impact assessment for a pressure leaching process
    (Stellenbosch : Stellenbosch University, 2017-12) Strydom, Johannes Jacobus; Auret, Lidia; Dorfling, Christie; Stellenbosch University. Faculty of Engineering. Dept. of Process Engineering.
    ENGLISH SUMMARY: Modern chemical and metallurgical processes consist of numerous process units with several complex interactions existing between them. The increased process complexity has in turn amplified the effect of faulty process conditions on the overall process performance. Fault diagnosis forms a critical part of a process monitoring strategy and is crucial for improved process performance. The increased amount of process measurements readily available in modern process plants allows for more complex data-driven fault diagnosis methods. Linear and nonlinear feature extraction methods are popular multivariate fault diagnosis procedures employed in literature. However, these methods are yet to find wide spread industrial application. The multivariate fault diagnosis methods are not often evaluated on real-world modern chemical processes. The lack of real world application has in turn led to the absence of economic performance assessments evaluating the potential profitability of these fault diagnosis methods. The aim of this study is to design and investigate the performance of a fault diagnosis strategy with both traditional fault diagnosis performance metrics and an economic impact assessment (EIA). A complex dynamic process model of the pressure leach at a base metal refinery (BMR) was developed by Dorfling (2012). The model was recently updated by Miskin (2015), who included the actual process control layers present at the BMR. A fault library was developed, through consultation of expert knowledge from the BMR, and incorporated into the dynamic model by Miskin (2015). The pressure leach dynamic model will form the basis for the investigation. Principal component analysis (PCA) and kernel PCA (KPCA) were employed as feature extraction methods. Traditional and reconstruction based contributions were employed as fault identification methods. Economic Performance Functions (EPFs) were developed from expert knowledge from the plant. The fault diagnosis performance was evaluated through the traditional performance metrics and the EPFs. Both PCA and KPCA provided improved fault detection results when compared to a simple univariate method. PCA provided significantly improved detection results for five of the eight faults evaluated, when compared to univariate detection. Fault identification results suffered from significant fault smearing. The significant fault detection results did not translate into a significant economic benefit. The EIA proved the process to be robust against faults, when implementing a basic univariate fault detection approach. Recommendations were made for possible industrial application and future work focusing on EIAs, training data selection and fault smearing.
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    Regsaspekte van die bestuur van 'n deeltitelskema
    (Stellenbosch : Stellenbosch University, 1996) Strydom, Johannes Jacobus; Stellenbosch University. Faculty of . Dept. of .