Stochastic modelling in the petrochemical industry (discrete event simulation based) / Marlize Meyer
This study provides a fully described and streamlined simulation process that will assist the modeler in successfully completing a simulation project. This study also highlights the shortcomings of many of the processes discussed in literature, and it reduces the risk of an unsuccessful project. One of the main reasons why all the steps are not usually mentioned in modeling environment is because most models do not require a base model. People dynamics in the project should be monitored and managed carefully due to the impact it has on the overall project and the message that will be distributed in forums where the modeler is not present. The Petrochemical industry poses a huge challenge for stochastic modeling due to its continuous nature, its discrete continuous interfaces and the highly interactive processes and plants. The Petrochemical industry is also a tough modeling environment with well established software tools and technologies. The aspects of the actual system, not covered by the historic software, have left a gap where stochastic modeling fits nicely. During this study it is proven that stochastic modeling can fill this gap with huge success. Some examples of stochastic models where they are applied in the Petrochemical Industry are discussed in this study. One of the valuable contributions that stochastic modeling can make in the petrochemical industry is to show that infrastructure constraints combined with an imbalance in available blend volumes at the time of blending can often, surprisingly, create a huge gap between expected and actual volumes sold to market. Timing is critical and having the right volumes in the right balance is essential. Blend sequence can also have a huge impact on volumes achieved.
- ETD@Vaal Triangle Campus