@article{
 title = {Robust dynamic optimization of enzyme-catalyzed carboligation: A point estimate-based back-off approach},
 type = {article},
 year = {2019},
 keywords = {Back-off strategy,Benzaldehyde lyase,Dynamic optimization,Enzyme catalysis,Optimal reactor design,Point estimate method,Robust optimization},
 pages = {232-247},
 volume = {121},
 websites = {https://linkinghub.elsevier.com/retrieve/pii/S0098135418306689},
 month = {2},
 id = {ce68022d-3dc2-3c4c-8ce1-fc4af6fc0358},
 created = {2021-10-26T13:36:46.072Z},
 file_attached = {true},
 profile_id = {46206c9e-d69a-378c-9478-6dd168f65080},
 group_id = {cc302413-c146-306b-8008-abbf67f3b420},
 last_modified = {2021-10-26T13:36:57.046Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {In this paper, we present a systematic robust dynamic optimization framework applied to the benzaldehyde lyase-catalyzed carboligation of propanal and benzaldehyde to produce (R)-2-hydroxy-1-phenylbutan-1-one (BA). First, the elementary process functions approach was used to screen between different dosing concepts, and it was found that simultaneously dosing propanal and benzaldehyde leads to the highest final concentration of BA. Next, we applied global sensitivity analysis and found that 10 out of 13 kinetic parameters are relevant. Time-varying back-offs were then used to handle parametric uncertainties due to these 10 parameters. A major contribution in our work is the use of the point estimate method instead of Monte Carlo simulations to calculate the back-offs in an efficient and reproducible manner. We show that this new approach is at least 10 times faster than the conventional Monte Carlo approach while achieving low approximation errors.},
 bibtype = {article},
 author = {Emenike, Victor N. and Xie, Xiangzhong and Schenkendorf, René and Spiess, Antje C. and Krewer, Ulrike},
 doi = {10.1016/j.compchemeng.2018.10.006},
 journal = {Computers & Chemical Engineering}
}

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