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@article{Wiebe:2019:10.1021/acs.iecr.8b03292,
author = {Wiebe, J and Cecílio, I and Misener, R},
doi = {10.1021/acs.iecr.8b03292},
journal = {Industrial & Engineering Chemistry Research},
pages = {17177--17191},
title = {Data-driven optimization of processes with degrading equipment},
url = {http://dx.doi.org/10.1021/acs.iecr.8b03292},
volume = {57},
year = {2019}
}

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TY  - JOUR
AB  - In chemical and manufacturing processes, unit failures due to equipment degradation can lead to process downtime and significant costs. In this context, finding an optimal maintenance strategy to ensure good unit health while avoiding excessive expensive maintenance activities is highly relevant. We propose a practical approach for the integrated optimization of production and maintenance capable of incorporating uncertain sensor data regarding equipment degradation. To this end, we integrate data-driven stochastic degradation models from Condition-based Maintenance into a process level mixed-integer optimization problem using Robust Optimization. We reduce computational expense by utilizing both analytical and data-based approximations and optimize the Robust optimization parameters using Bayesian Optimization. We apply our framework to five instances of the State-Task-Network and demonstrate that it can efficiently compromise between equipment availability and cost of maintenance.
AU  - Wiebe,J
AU  - Cecílio,I
AU  - Misener,R
DO  - 10.1021/acs.iecr.8b03292
EP  - 17191
PY  - 2019///
SN  - 0888-5885
SP  - 17177
TI  - Data-driven optimization of processes with degrading equipment
T2  - Industrial & Engineering Chemistry Research
UR  - http://dx.doi.org/10.1021/acs.iecr.8b03292
UR  - http://hdl.handle.net/10044/1/63919
VL  - 57
ER  -

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