Doctoral Degrees (Statistics and Actuarial Science)

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Browsing Doctoral Degrees (Statistics and Actuarial Science) by browse.metadata.advisor "Coetzer, R. L. J."

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    Multivariate statistical process evaluation and monitoring for complex chemical processes
    (Stellenbosch : Stellenbosch University, 2015-12) Rossouw, Ruan Francois; Le Roux, N. J.; Coetzer, R. L. J.; Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Statistics and Actuarial Science.
    ENGLISH ABSTRACT: In this study, the development of an innovative fully integrated process monitoring methodology is presented for a complex chemical facility, originating at the coal feed from different mines up to the processing of the coal to produce raw gas at the gasification plant. The methodology developed is real-time, visual, detect deviations from expected performance across the whole value chain, and also provide for the integration and standardisation of data from a number of different data sources and formats. Real time coal quality analyses from an XRF analyser are summarised and integrated with various data sources from the Coal Supply Facility to provide information on the coal quality of each mine. In addition, simulation models are developed to generate information on the coal quality of each heap and the quality of the reclaimed coal sent to gasification. A real-time multivariate process monitoring approach for the Coal Gasification Facility is presented. This includes a novel approach utilising Generalised Orthogonal Procrustes Analysis to find the optimal units and time period to employ as a reference set. Principal Component Analysis (PCA) and Canonical Variate Analysis (CVA) theory and biplots are evaluated and extended for the real-time monitoring of the plant. A new approach to process deviation monitoring on many variables is presented based on the confidence ( ) value at a specified T2-value. This methodology is proposed as a general data driven performance index as it is objective, and very little prior knowledge of the system is required. A new multivariate gasifier performance index (GPI) is developed, which integrates subject matter knowledge with a data driven approach for real time performance monitoring. Various software modules are developed which were required for the implementation of the real time multivariate process monitoring methodology, which is made operational and distributed to the clients on an interactive web interface. The methodology has been trademarked by Sasol as the MSPEM™ Technology Package. Following the success of the developed methodology, the MSPEM™ package has been rolled out to many more business units within the Sasol Group. In conclusion, this study presents the development and implementation of the MSPEM™ application for a real-time, integrated and standardised approach to multivariate process monitoring of the Sasol Synfuels Coal Value Chain and Gasification Facility. In summary, the following novel developments were introduced: • The application of distance measures other than Euclidean measures are introduced for space filling designs for computer experiments in mixture variables. • An approach utilising Generalised Orthogonal Procrustes Analysis to specify the optimal units and time period to employ as a reference set is developed. • An approach to process deviation monitoring on many variables is presented based on the confidence ( ) value at a specified T2-value. • An integrated approach to a reactor performance index is developed and illustrated. • A comprehensive software infrastructure is developed and implemented