Imperial College London
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DrKamalKuriyan

Faculty of Engineering, Department of Chemical Engineering

Research Associate / Senior Developer
 
 
 
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Contact

 

+44 (0)20 7594 6645k.kuriyan

 
 
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Location

 

C410Roderic Hill BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Kusumo:2022:10.1039/D1RE00465D,
author = {Kusumo, K and Kuriyan, K and Vaidyaraman, S and Garcia, Munoz S and Shah, N and Chachuat, B},
doi = {10.1039/D1RE00465D},
journal = {Reaction Chemistry and Engineering},
pages = {2359--2374},
title = {Probabilistic framework for optimal experimental campaigns in the presence of operational constraints},
url = {http://dx.doi.org/10.1039/D1RE00465D},
volume = {7},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - The predictive capability of any mathematical model is intertwined with the quality of experimentaldata collected for its calibration. Model-based design of experiments helps compute maximallyinformative campaigns for model calibration. But in early stages of model development it is crucial toaccount for model uncertainties to mitigate the risk of uninformative or infeasible experiments. Thisarticle presents a new method to design optimal experimental campaigns subject to hard constraintsunder uncertainty, alongside a tractable computational framework. This computational frameworkinvolves two stages, whereby the feasible experimental space is sampled using a probabilistic approachin the first stage, and a continuous-effort optimal experiment design is determined by searching overthe sampled feasible space in the second stage. The tractability of this methodology is demonstratedon a case study involving the exothermic esterification of priopionic anhydride with significant risk ofthermal runaway during experimentation. An implementation is made freely available based on thePython packages DEUS and Pydex.
AU  - Kusumo,K
AU  - Kuriyan,K
AU  - Vaidyaraman,S
AU  - Garcia,Munoz S
AU  - Shah,N
AU  - Chachuat,B
DO  - 10.1039/D1RE00465D
EP  - 2374
PY  - 2022///
SN  - 2058-9883
SP  - 2359
TI  - Probabilistic framework for optimal experimental campaigns in the presence of operational constraints
T2  - Reaction Chemistry and Engineering
UR  - http://dx.doi.org/10.1039/D1RE00465D
UR  - https://pubs.rsc.org/en/content/articlelanding/2022/RE/D1RE00465D
UR  - http://hdl.handle.net/10044/1/98457
VL  - 7
ER  -
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