Tuesday, October 18, 2011: 10:35 AM
200 G (Minneapolis Convention Center)
After a chemical process has been developed, designed, and started-up at commercial scale, it is usually desired to validate the large scale results against design assumptions. However, the engineer usually encounters factors that affect the quality of the data collected at a plant, both from a variability standpoint as well as from data availability or lack thereof. This paper describes Six Sigma and probabilistic methodologies for uncertainty reduction used to ensure successful validation plans, data collection strategies, and methods for data analysis in order to validate a large-scale commercial system. The strategies will be illustrated using the GE designed and validated operation of a coal-to-methanol gasification facility in China using the aforementioned error-reducing strategies.