Teaching uncertainty propagation as a core component in process engineering statistics

Batstone, Damien John (2013) Teaching uncertainty propagation as a core component in process engineering statistics. Education for Chemical Engineers, 8 4: e132-e139. doi:10.1016/j.ece.2013.10.003


Author Batstone, Damien John
Title Teaching uncertainty propagation as a core component in process engineering statistics
Journal name Education for Chemical Engineers   Check publisher's open access policy
ISSN 1749-7728
Publication date 2013-12-01
Year available 2013
Sub-type Article (original research)
DOI 10.1016/j.ece.2013.10.003
Volume 8
Issue 4
Start page e132
End page e139
Total pages 8
Place of publication London, United Kingdom
Publisher Elsevier
Abstract Propagation of uncertainty refers to evaluation of uncertainty in output(s) given uncertainty in input(s). This can be across a physical process, or can be predicted based on a process model. Uncertainty can be propagated analytically, by application of Taylor series variance propagation, or numerically, through repeated Monte-Carlo simulations. Propagation of uncertainty is an important concept in process engineering statistics, which is not currently widely taught. In this paper, an approach is provided for teaching uncertainty propagation as part of a larger process engineering statistics course, applying analytical and numerical propagation principles, including consideration of correlation in inputs. A saline blending practical is used as a case study, with experimental and theoretical determination of how variability in feed pump flows determines variability in outlet conductivity. Based on a class of 132 2nd year Chemical Engineering students, learning outcomes in analytical and numerical linear and non-linear propagation models can be attained and enhanced applicability and engagement within the core statistics course. An engagement survey particularly noted that the students recognised the importance of propagation as a technical capability, but noted difficulties in linking the experimental work to theory of propagation. Overall, propagation of uncertainty allows educators to increase the direct relevance of statistics to process engineering and engage with students through their existing analytical capabilities.
Keyword Modelling
Monte Carlo
Propagation
Statistics
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: School of Chemical Engineering Publications
Official 2014 Collection
Advanced Water Management Centre Publications
 
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