Browsing by Author "Coetzer, R. L. J."
Now showing 1 - 2 of 2
Results Per Page
- ItemSimulation of a coal stacking process using an online X-Ray Fluorescence analyser(Operations Research Society of South Africa (ORSSA), 2018) Rossouw, R. F.; Coetzer, R. L. J.; Le Roux, N. J.The Sasol Coal Value Chain is a complex system consisting of blending, stacking and reclaiming of no fewer than six different coal sources with vastly different coal qualities. The amount and quality of the gas produced from coal depend crucially on the quality of the coal reclaimed from the coal stacking yards. In this paper the development of a real time coal quality simulation model using information from an online X-Ray Fluorescence analyser, integrated with various data sources from the Coal Supply Facility, is presented. The integration of different data sources is discussed to create a centralised and standardised data framework for input to the simulation model. The simulation of a heap profile of the coal quality for each heap stacked, together with the quality of the reclaimed coal, is discussed in detail. It is shown how the generated information from the model is utilised in the development of a reclaiming strategy.
- ItemVariable contribution identification and visualization in multivariate statistical process monitoring(Elsevier, 2020-01) Rossouw, R. F.; Coetzer, R. L. J.; Le Roux, N. J.Multivariate statistical process monitoring (MSPM) has received book-length treatments and wide spread application in industry. In MSPM, multivariate data analysis techniques such as principal component analysis (PCA) are commonly employed to project the (possibly many) process variables onto a lower dimensional space where they are jointly monitored given a historical or specified reference set that is within statistical control. In this paper, PCA and biplots are employed together in an innovative way to develop an efficient multivariate process monitoring methodology for variable contribution identification and visualization. The methodology is applied to a commercial coal gasification production facility with multiple parallel production processes. More specifically, it is shown how the methodology is used to specify the optimal principal component combinations and biplot axes for visualization and interpretation of process performance, and for the identification of the critical variables responsible for performance deviations, which yielded direct benefits for the commercial production facility.