Imperial College London
Professor Pantelides

Professor Pantelides

Faculty of Engineering, Department of Chemical Engineering

Professor
 
 
 
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Contact

 

+44 (0)20 7594 5622c.pantelides

 
 
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Location

 

C401Roderic Hill BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Kusumo:2019:10.1021/acs.iecr.9b05006,
author = {Kusumo, KP and Gomoescu, L and Paulen, R and García, Muñoz S and Pantelides, CC and Shah, N and Chachuat, B},
doi = {10.1021/acs.iecr.9b05006},
journal = {Industrial & Engineering Chemistry Research},
pages = {2396--2408},
title = {Bayesian approach to probabilistic design space characterization: a nested sampling strategy},
url = {http://dx.doi.org/10.1021/acs.iecr.9b05006},
volume = {59},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Quality by design in pharmaceutical manufacturing hinges on computational methods and tools that are capable of accurate quantitative prediction of the design space. This paper investigates Bayesian approaches to design space characterization, which determine a feasibility probability that can be used as a measure of reliability and risk by the practitioner. An adaptation of nested sampling—a Monte Carlo technique introduced to compute Bayesian evidence—is presented. The nested sampling algorithm maintains a given set of live points through regions with increasing probability feasibility until reaching a desired reliability level. It furthermore leverages efficient strategies from Bayesian statistics for generating replacement proposals during the search. Features and advantages of this algorithm are demonstrated by means of a simple numerical example and two industrial case studies. It is shown that nested sampling can outperform conventional Monte Carlo sampling and be competitive with flexibility-based optimization techniques in low-dimensional design space problems. Practical aspects of exploiting the sampled design space to reconstruct a feasibility probability map using machine learning techniques are also discussed and illustrated. Finally, the effectiveness of nested sampling is demonstrated on a higher-dimensional problem, in the presence of a complex dynamic model and significant model uncertainty.
AU  - Kusumo,KP
AU  - Gomoescu,L
AU  - Paulen,R
AU  - García,Muñoz S
AU  - Pantelides,CC
AU  - Shah,N
AU  - Chachuat,B
DO  - 10.1021/acs.iecr.9b05006
EP  - 2408
PY  - 2019///
SN  - 0888-5885
SP  - 2396
TI  - Bayesian approach to probabilistic design space characterization: a nested sampling strategy
T2  - Industrial & Engineering Chemistry Research
UR  - http://dx.doi.org/10.1021/acs.iecr.9b05006
UR  - https://pubs.acs.org/doi/abs/10.1021/acs.iecr.9b05006
UR  - http://hdl.handle.net/10044/1/75841
VL  - 59
ER  -
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