Imperial College London
Alternate

Professor Nilay Shah OBE FREng

Faculty of Engineering, Department of Chemical Engineering

Professor of Process Systems Engineering
 
 
 
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Contact

 

+44 (0)20 7594 6621n.shah

 
 
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Assistant

 

Miss Jessica Baldock +44 (0)20 7594 5699

 
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Location

 

ACEX 522ACE ExtensionSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Kusumo:2022:10.1016/j.compchemeng.2022.107680,
author = {Kusumo, K and Kuriyan, K and Vaidyaraman, S and Garcia, Munoz S and Shah, N and Chachuat, B},
doi = {10.1016/j.compchemeng.2022.107680},
journal = {Computers and Chemical Engineering},
title = {Risk mitigation in model-based experiment design: a continuous-effort approach to optimal campaigns},
url = {http://dx.doi.org/10.1016/j.compchemeng.2022.107680},
volume = {159},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - A key challenge in maximizing the effectiveness of model-based design of experiments for calibrating nonlinear process models is the inaccurate prediction of information that is afforded by each new experiment. We present a novel methodology to exploit prior probability distributions of model parameter estimates in a bi-objective optimization formulation, where a conditional-value-at-risk criterion is considered alongside an average information criterion. We implement a tractable numerical approach that discretizes the experimental design space and leverages the concept of continuous-effort experimental designs in a convex optimization formulation. We demonstrate effectiveness and tractability through three case studies, including the design of dynamic experiments. In one case, the Pareto frontier comprises experimental campaigns that significantly increase the information content in the worst-case scenarios. In another case, the same campaign is proven to be optimal irrespective of the risk attitude. An open-source implementation of the methodology is made available in the Python software Pydex.
AU  - Kusumo,K
AU  - Kuriyan,K
AU  - Vaidyaraman,S
AU  - Garcia,Munoz S
AU  - Shah,N
AU  - Chachuat,B
DO  - 10.1016/j.compchemeng.2022.107680
PY  - 2022///
SN  - 0098-1354
TI  - Risk mitigation in model-based experiment design: a continuous-effort approach to optimal campaigns
T2  - Computers and Chemical Engineering
UR  - http://dx.doi.org/10.1016/j.compchemeng.2022.107680
UR  - https://www.sciencedirect.com/science/article/pii/S0098135422000242?via%3Dihub
UR  - http://hdl.handle.net/10044/1/93769
VL  - 159
ER  -
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