Efficient sensitivity analysis and interpretation of parameter correlations in chemical engineering

Efficient sensitivity analysis and interpretation of parameter correlations in chemical engineering. Xie, X., Schenkendorf, R., & Krewer, U. Reliability Engineering & System Safety, 187(January):159-173, Elsevier Ltd, 7, 2019.

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Efficient sensitivity analysis and interpretation of parameter correlations in chemical engineering [link]

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Parameter uncertainties affect model-based system reliability analysis and may lead to safety issues in model-based process design. Global sensitivity analysis (GSA) is a valuable tool to quantify the influence of parameter uncertainties in the variation of the model output. However, GSA has not been widely employed in the field of chemical engineering, especially for processes with correlated model parameters. Parameter correlations, in turn, are quite common when identifying model parameters with experimental data. Thus, we propose and critically compare (co)variance-based and moment-independent GSA techniques for analyzing chemical processes in the absence and presence of parameter correlations. Technically, polynomial chaos expansion is used to reduce the computational burden for GSA. The proposed methods are demonstrated for a continuous synthesis process. Here, the results show significant differences in the parameter sensitivity rankings when parameter correlations are considered or not while the moment-independent technique provides a universal and easy-to-interpret sensitivity measure.

@article{
 title = {Efficient sensitivity analysis and interpretation of parameter correlations in chemical engineering},
 type = {article},
 year = {2019},
 keywords = {Chemical processes,Gaussian copula,Parameter correlation,Polynomial chaos expansion,Sensitivity analysis},
 pages = {159-173},
 volume = {187},
 websites = {https://doi.org/10.1016/j.ress.2018.06.010,https://linkinghub.elsevier.com/retrieve/pii/S0951832018300541},
 month = {7},
 publisher = {Elsevier Ltd},
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 abstract = {Parameter uncertainties affect model-based system reliability analysis and may lead to safety issues in model-based process design. Global sensitivity analysis (GSA) is a valuable tool to quantify the influence of parameter uncertainties in the variation of the model output. However, GSA has not been widely employed in the field of chemical engineering, especially for processes with correlated model parameters. Parameter correlations, in turn, are quite common when identifying model parameters with experimental data. Thus, we propose and critically compare (co)variance-based and moment-independent GSA techniques for analyzing chemical processes in the absence and presence of parameter correlations. Technically, polynomial chaos expansion is used to reduce the computational burden for GSA. The proposed methods are demonstrated for a continuous synthesis process. Here, the results show significant differences in the parameter sensitivity rankings when parameter correlations are considered or not while the moment-independent technique provides a universal and easy-to-interpret sensitivity measure.},
 bibtype = {article},
 author = {Xie, Xiangzhong and Schenkendorf, René and Krewer, Ulrike},
 doi = {10.1016/j.ress.2018.06.010},
 journal = {Reliability Engineering & System Safety},
 number = {January}
}

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